All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
IRJET - Big Data: Evolution Cum RevolutionIRJET Journal
- Big data has revolutionized many fields by enabling the extraction of useful insights from vast amounts of data.
- The document discusses the evolution of big data and its applications in areas like healthcare, search engines, transportation, finance, social media, and government identification systems.
- It also covers technologies used for big data like machine learning, artificial intelligence, the internet of things, and highlights challenges of collecting, analyzing, and managing large datasets.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
IRJET - Big Data: Evolution Cum RevolutionIRJET Journal
- Big data has revolutionized many fields by enabling the extraction of useful insights from vast amounts of data.
- The document discusses the evolution of big data and its applications in areas like healthcare, search engines, transportation, finance, social media, and government identification systems.
- It also covers technologies used for big data like machine learning, artificial intelligence, the internet of things, and highlights challenges of collecting, analyzing, and managing large datasets.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
This document provides a summary of a group project report on big data analytics. It discusses how big data and analytics can help companies optimize supply chains by improving decision making and handling risks. It defines big data as large, diverse, and rapidly growing datasets that are difficult to manage with traditional tools. It also discusses data sources, management, quality dimensions, and using statistical process control methods to monitor and control data quality throughout the production process.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
This document provides an overview of big data basics. It discusses how big data is not just about data size but also about analyzing large, diverse datasets from multiple sources. It describes how big data systems can efficiently process huge volumes of data in parallel from petabytes of data. Examples of big data applications include digital marketing, fraud detection, social media analysis, and machine-generated data analytics. Common sources of big data are also listed. The document then covers new analytics approaches for big data and different types of data warehouses. It concludes by discussing current and future trends in data storage and analytics for big data.
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
This document outlines the phases of the data analytics lifecycle, with a focus on Phase 1: Discovery. The Discovery phase involves understanding the business problem, available resources, and formulating initial hypotheses to test. Key activities in Discovery include interviewing stakeholders, learning the domain, assessing available data and tools, and framing the business and analytics problems. The goal is to have enough information to draft an analytic plan and scope the project before moving to the next phase of data preparation.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Banks are making customer centricity a top priority over the next two years and see data analytics technologies like predictive analytics, data visualization, and real-time data processing as key to achieving this, with over half of large bank executives surveyed seeing each of these as important; however, most executives also admit their current capabilities are not mature enough to fully support their customer-focused strategies.
Machine learning algorithms analyze large amounts of data to identify patterns and make predictions. There are two main types: supervised learning predicts outcomes based on historical labeled data, while unsupervised learning identifies structure in unlabeled data to group it into categories. Effective machine learning requires choosing the right algorithm for the specific data and application, and allows creating intelligent processes that generate useful predictions for a data-driven world.
The document discusses how companies can leverage data and analytics to gain competitive advantages. It notes that many companies collect large amounts of data but lack the skills and resources to extract useful insights from it. The document promotes Idiro as a company that can help organizations address common data challenges like too much data to manage, lack of analytical skills, and disparate data sources. Idiro provides tools and expertise to clean, analyze and generate business intelligence from big data to help companies better understand their business and customers.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?VIRGOkonsult
This document discusses how banks can maximize the value of customer data through big data analytics. It notes that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units prevent a unified customer view. Additionally, banks lack analytics skills and view big data initiatives as traditional IT projects rather than strategic opportunities. The document outlines how banks can enhance customer retention, acquisition, and share of wallet by leveraging big data analytics across the customer lifecycle from lead generation to risk assessment and personalized marketing. Case studies show analytics improving lead conversion rates by over 100% and top-line growth by ten times.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Hadoop is an open-source software framework used for storing and processing large datasets in a distributed manner across commodity hardware. It was created in 2005 by Doug Cutting and Mike Cafarella to address the issue of processing big data at a reasonable cost and time. Hadoop uses HDFS for storage and MapReduce for processing data distributed over a network of nodes in parallel. It allows organizations to gain insights from vast amounts of structured and unstructured data faster and at lower costs than traditional approaches.
Here are the answers to the assignment questions:
1. Big data refers to huge volumes of both structured and unstructured data that is so large in size and complex that traditional data processing applications are inadequate to deal with it.
2. The three main types of data are:
- Structured data: Data that is organized and has a predefined data model e.g. numbers in a database. Sources include CRM systems, transactions etc.
- Semi-structured data: Data that has some structure but not fully structured e.g. log files, XML files. Sources include sensors, images, audio/video etc.
- Unstructured data: Data with no predefined structure e.g. text, emails. Sources include
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
This document provides a summary of a group project report on big data analytics. It discusses how big data and analytics can help companies optimize supply chains by improving decision making and handling risks. It defines big data as large, diverse, and rapidly growing datasets that are difficult to manage with traditional tools. It also discusses data sources, management, quality dimensions, and using statistical process control methods to monitor and control data quality throughout the production process.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
This document provides an overview of big data basics. It discusses how big data is not just about data size but also about analyzing large, diverse datasets from multiple sources. It describes how big data systems can efficiently process huge volumes of data in parallel from petabytes of data. Examples of big data applications include digital marketing, fraud detection, social media analysis, and machine-generated data analytics. Common sources of big data are also listed. The document then covers new analytics approaches for big data and different types of data warehouses. It concludes by discussing current and future trends in data storage and analytics for big data.
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
This document outlines the phases of the data analytics lifecycle, with a focus on Phase 1: Discovery. The Discovery phase involves understanding the business problem, available resources, and formulating initial hypotheses to test. Key activities in Discovery include interviewing stakeholders, learning the domain, assessing available data and tools, and framing the business and analytics problems. The goal is to have enough information to draft an analytic plan and scope the project before moving to the next phase of data preparation.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Banks are making customer centricity a top priority over the next two years and see data analytics technologies like predictive analytics, data visualization, and real-time data processing as key to achieving this, with over half of large bank executives surveyed seeing each of these as important; however, most executives also admit their current capabilities are not mature enough to fully support their customer-focused strategies.
Machine learning algorithms analyze large amounts of data to identify patterns and make predictions. There are two main types: supervised learning predicts outcomes based on historical labeled data, while unsupervised learning identifies structure in unlabeled data to group it into categories. Effective machine learning requires choosing the right algorithm for the specific data and application, and allows creating intelligent processes that generate useful predictions for a data-driven world.
The document discusses how companies can leverage data and analytics to gain competitive advantages. It notes that many companies collect large amounts of data but lack the skills and resources to extract useful insights from it. The document promotes Idiro as a company that can help organizations address common data challenges like too much data to manage, lack of analytical skills, and disparate data sources. Idiro provides tools and expertise to clean, analyze and generate business intelligence from big data to help companies better understand their business and customers.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?VIRGOkonsult
This document discusses how banks can maximize the value of customer data through big data analytics. It notes that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units prevent a unified customer view. Additionally, banks lack analytics skills and view big data initiatives as traditional IT projects rather than strategic opportunities. The document outlines how banks can enhance customer retention, acquisition, and share of wallet by leveraging big data analytics across the customer lifecycle from lead generation to risk assessment and personalized marketing. Case studies show analytics improving lead conversion rates by over 100% and top-line growth by ten times.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Hadoop is an open-source software framework used for storing and processing large datasets in a distributed manner across commodity hardware. It was created in 2005 by Doug Cutting and Mike Cafarella to address the issue of processing big data at a reasonable cost and time. Hadoop uses HDFS for storage and MapReduce for processing data distributed over a network of nodes in parallel. It allows organizations to gain insights from vast amounts of structured and unstructured data faster and at lower costs than traditional approaches.
Here are the answers to the assignment questions:
1. Big data refers to huge volumes of both structured and unstructured data that is so large in size and complex that traditional data processing applications are inadequate to deal with it.
2. The three main types of data are:
- Structured data: Data that is organized and has a predefined data model e.g. numbers in a database. Sources include CRM systems, transactions etc.
- Semi-structured data: Data that has some structure but not fully structured e.g. log files, XML files. Sources include sensors, images, audio/video etc.
- Unstructured data: Data with no predefined structure e.g. text, emails. Sources include
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
The document discusses big data, including its definition, types, benefits, and challenges. It describes how big data is generated from a variety of sources and is characterized by its volume, velocity, and variety (the 3Vs). Big data provides benefits like improved customer insights and business optimization. However, it also poses challenges to deal with its huge volume, high velocity, varied types (structured and unstructured), and issues of data veracity (uncertainty). Techniques to address these challenges include using distributed file systems, parallel processing frameworks like Hadoop, and data fusion or advanced mathematics to manage uncertainty.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
This document provides an overview of big data, including definitions of key terms like data, big data, and examples of big data. It describes why big data is important, how big data analytics works, and the benefits it provides. It outlines different types of big data like structured, unstructured, and semi-structured data. It also discusses characteristics of big data like volume, velocity, variety, and veracity. Additionally, it identifies primary sources of big data and examples of big data tools and software. Finally, it briefly discusses how big data and machine learning are related and how AI can be used to enhance big data analytics.
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
Introduction to big data – convergences.saranya270513
Big data is high-volume, high-velocity, and high-variety data that is too large for traditional databases to handle. The volume of data is growing exponentially due to more data sources like social media, sensors, and customer transactions. Data now streams in continuously in real-time rather than in batches. Data also comes in more varieties of structured and unstructured formats. Companies use big data to gain deeper insights into customers and optimize business processes like supply chains through predictive analytics.
The document discusses big data analytics and provides tips for organizations looking to implement big data initiatives. It notes that while organizations have large amounts of customer, sales, and other operational data, most are not effectively analyzing and extracting insights from this data. The value is in using analytics to uncover hidden patterns and correlations to help businesses make better decisions. However, most companies currently take a slow, manual approach to data compilation and analysis. The document recommends that organizations consider big data as a business solution rather than just an IT problem. It suggests taking a journey approach, focusing on insights over data, using proven analytics tools, and delivering early business value from big data projects in order to justify further investment.
Big data is generated from various sources producing huge volumes of data every minute, including blog posts, YouTube videos, searches, and social media activity. Big data can provide businesses several benefits like instant insights, improved analytics, vast data management, and better decision making. It allows understanding customers better, reducing costs, and increasing operating margins. Big data has applications in many industries like banking for fraud detection, healthcare for personalized medicine, retail for inventory optimization, and transportation for traffic control. Government uses it for claims processing and insurance uses it for customer insights, pricing, and fraud detection.
1. Data science involves applying scientific methods and processes to extract knowledge and insights from data. It includes techniques like machine learning, statistical analysis, and data visualization.
2. Data science has many applications in fields like marketing, healthcare, banking, and government. It helps with tasks like demand forecasting, fraud detection, personalized recommendations, and policymaking.
3. The key characteristics of data science include business understanding, intuition, curiosity, and skills in areas like machine learning algorithms, statistics, programming, and communication. Data scientists help organizations make better decisions using data-driven insights.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Semelhante a BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND SOCIAL MEDIA DATA (20)
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
in Python and can be used both in offline and real-time environments, which effectively combines security
concepts and image processing algorithms to obtain data assessment.
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
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Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
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This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
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Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
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SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
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Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Nordic Marketo Engage User Group_June 13_ 2024.pptx
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND SOCIAL MEDIA DATA
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
DOI :10.5121/ijscai.2016.5105 41
BIG DATA ANALYTICS: CHALLENGES AND
APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND
SOCIAL MEDIA DATA
Jai Prakash Verma
1
, Smita Agrawal
1
, Bankim Patel
2
and Atul Patel
3
1
CSE Department, Institute of Technology, Nirma University, Ahmedabad
2
SRIMCA, UKA Trasadia University, Surat
3
CMPICA, CHARUSAT University, Changa
ABSTRACT
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
KEYWORDS
Big Data, Big Data Analytics, Social Media Analytics, Content Based Analytics, Text Analytics, Audio
Analytics, Video Analytics.
1. INTRODUCTION
The term big data is used to describe the growth and the availability of huge amount of structured
and unstructured data. Big data which are beyond the ability of commonly used software tools to
create, manage, and process data within a suitable time. Big data is important because the more
data we collect the more accurate result we get and able to optimize business processes. The Big
data is very important for business and society purpose. The data came from everywhere like
sensors that used to gather climate information, available post or share data on the social media
sites, video movie audio etc. This collection of data is called ―BIG DATA‖.
Now a days this big data is used in multiple ways to grow business and to know the world [1,2,
15].
In most enterprise scenarios the data is too big or it moves too fast or it exceeds current
processing capacity. Big data has the potential to help companies improve operations and make
faster, more intelligent decisions. Big data usually includes data sets with sizes beyond the ability
of commonly used software tools to capture, curate, manage, and process data within a tolerable
elapsed time. Big data is a set of techniques and technologies that require new forms of
integration to uncover large hidden values from large datasets that are diverse, complex, and of a
massive scale. Wal-Mart handles more than 1 million customer transaction every hour. Facebook
handles 40 billion photos from its user base. Big data require some technology to efficiently
process large quantities of data. It use some technology like, data fusion and integration, genetic
algorithms, machine learning, and signal processing, simulation, natural language processing,
time series Analytics and visualization [12,13,16]
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
42
1.1. Characteristics of Big Data:-
Volume: Many factors contribute to the increase in data volume. Transaction-based data stored
through the years. Unstructured data streaming in from social media. Increasing amounts of
sensor and machine-to-machine data being collected. In the past, excessive data volume was a
storage issue. But with decreasing storage costs, other issues emerge, including how to determine
relevance within large data volumes and how to use analytics to create value from relevant data
[10, 12,13, 15,16].
Velocity: Data is streaming in at unprecedented speed and must be dealt with in a timely manner.
RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-
real time. Reacting quickly enough to deal with data generation speed is a challenge for most
organizations.
Variety: Data today comes in all types of formats. Structured, numeric data in traditional
databases. Information created from line-of-business applications. Unstructured text documents,
email, video, audio, stock ticker data and financial transactions. Managing, merging and
governing different varieties of data is something many organizations still grapple with.
Variability: In addition to the increasing velocities and varieties of data, data flows can be highly
inconsistent with periodic peaks. Daily, seasonal and event-triggered peak data loads can be
challenging to manage. Even more so with unstructured data involved.
Complexity: Today's data comes from multiple sources. And it is still an undertaking to link,
match, cleanse and transform data across systems. However, it is necessary to connect and
correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out
of control.
Value: It includes how we can use this big data for enhancing the business and living style. We
know that different types of business or social application generate different types of data. Still
identifying values form Big Data in their application areas is a big issue.
2. BIG DATA ANALYTICS
Big Data Anlytics refers to the process of collecting, organizing, analyzing large data sets to
discover different patterns and other useful information. Big data analytics is a set of
technologies and techniques that require new forms of integration to disclose large hidden values
from large datasets that are different from the usual ones, more complex, and of a large enormous
scale. It mainly focuses on solving new problems or old problems in better and effective ways
[12,13, 15, 16].
The main goal of the big data analytic is to help organization to make better business
decision,future prediction, analysis large numbers of transactions that done in organization and
update the form of data that organization is used. Example of big data Analytics are big online
business website like Flipkart, snapdeal uses Facebook or Gmail data to view the customer
information or behaviour. Analyzing big data allows analysts, researchers, and business users to
make better and faster decisions using data that was previously inaccessible or unusable. Using
advanced analytics techniques such as text analytics, machine learning, predictive analytics, data
mining, statistics, and natural language processing, businesses can analyze previously untapped
data sources independent or together with their existing enterprise data to gain new insights
resulting in significantly better and faster decisions. It helps us to uncover hidden patterns,
unknown correlations, market trends, customer preferences etc. It leads us to more effective
marketing, revenue opportunities, better customer service etc. Big Data can be analyzed through
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
43
predictive analytics, text analytics, statistical analytics and data mining[1,2,4]. Types of big data
analytics are: Prescriptive: - This type of analytics help to decide what actions should be taken. It
very valuable but not used largely. It focuses on answer specific question like, hospital
management, diagnosis of cancer patients, diabetes patients that determine where to focus
treatment. Predictive: - This type of analytics help to predict future or what might be happen. For
example some companies use predictive analytics to take decision for sales, marketing,
production, etc. Diagnostic: - In this type look at past and analyze the situation what happen in
past and why it happen. And how we can overcome this situation. For example weather
preadiction, customer behavioral analysis etc. Descriptive:-It describes what is happening
currently and prediction near future. For example market analysis, compatains behavioral
analysis etc.
By using appropriate analytics organization can increase sales, increase customer service, and can
improve operations. Predictive Analytics allow organizations to make better and faster decisions
[1, 2, 4, 10].
2.1. Predictive Analytics
Predictive Analytics is a method through which we can extract information from existing data
sets to predict future outcomes and trends and also determine patterns. It does not tell us what
will happen in future. It forecasts what might happen in future with acceptable level of reliability.
It also includes what if-then-else scenarios and risk assessment. Applications areas of Predictive
Analytics are [1, 2, 4, 10]:
CRM (Customer Relationship Management): Predictive analytics is useful in CRM in fields such
as marketing campaigns, sales, customer services etc. The focus is to put their efforts effectively
on analyzeing product in demand and predict customer’s buying habits .
Clinical Decision Support: Predictive Analytics helps us to determine that which patients are at
risk of developing certain conditions like diabetes, asthma, lifetime illness etc.
Collection Analytics: Predictive Analytics helps financial institutions for the allocation for
collecting resources by identifying most effective collection agencies, contact strategies etc. to
each customer.
Cross Sell: An Organization that offers multiple products, Predictive Analytics can help to
analyze customer’s spendings, their behavior etc. This can help to lead cross sales that means
selling additional products to current customers.
Customer Retention: As the number of competing services is increasing, businesses should
continuously focus on maintaining customer satisfaction, rewarding loyal customers and
minimize customer reduction. If Predictive Analytics is properly applied, it can lead to active
retention strategy by frequently examining customer’s usage, spending and behavior patterns.
Direct marketing: When marketing consumer products and services, there is the challenge of
keeping up with competing products and consumer behavior. Apart from identifying prospects,
predictive analytics can also help to identify the most effective combination of product versions,
marketing material, communication channels and timing that should be used to target a given
consumer.
Fraud detection: Fraud is a big problem for many businesses and can be of various types:
inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and
false insurance. These problems plague firms of all sizes in many industries. Some examples of
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likely victims are credit card issuers, insurance companies, retail merchants, manufacturers,
business-to-business suppliers and even services providers. Predictive analysis can help to
identify high-risk fraud candidates in business or the public sector.
Portfolio, product or economy-level prediction: These types of problems can be addressed by
predictive analytics using time series techniques. They can also be addressed via machine
learning approaches which transform the original time series into a feature vector space, where
the learning algorithm finds patterns that have predictive power.
Risk management: When employing risk management techniques, the results are always to
predict and benefit from a future scenario. Predictive analysis helps organizations or business
enterprises to identify future risk, Natural Disaster and its effect. Risk management helps them to
take correct decision on correct time.
Underwriting: Many businesses have to account for risk exposure due to their different services
and determine the cost needed to cover the risk. For example, auto insurance providers need to
accurately determine the amount of premium to charge to cover each automobile and driver. For
a health insurance provider, predictive analytics can analyze a few years of past medical claims
data, as well as lab, pharmacy and other records where available, to predict how expensive an
enrollee is likely to be in the future. Predictive analytics can help underwrite these quantities by
predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the
process of customer acquisition by predicting the future risk behaviour of a customer using
application level data.
2.2. Big Data Analytics usage in India
From predicting ticket confirmations of trains to checking for water supply leakages and even for
finding the perfect bride and groom, Big Data is being used in a number of creative ways in
India. Following are few uses of Big Data Analytics in india in last few years [3,9].
a) Win elections (exit poll).
b) Finding a perfect match.
c) Detecting water leakages.
d) Gaining insights into shopping behavior.
e) Ensuring proper water supply.
f) Improve India’s financial inclusion ratio.
g) Improve product development.
h) Predict ticket confirmations for trains.
3. SOCIAL MEDIA ANALYTICS
The Social Media analytics is collecting information or data form the social media websites,
blogs etc. and uses it in business purpose or decision making. Now a Days Social Media is the
best platform for understand the real-time customer choice or intentions and sentiments, using
social media business advertising, product marketing easily. EBay.com uses two data warehouses
at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer
recommendations, and merchandising. Inside eBay’s 90PB data warehouse. Amazon.com
handles millions of back-end operations every day, as well as queries from more than half a
million third-party sellers. The core technology that keeps Amazon running is Linux-based and
as of 2005 they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5
TB, and 24.7 TB. Facebook handles 50 billion photos from its user base. As of August 2012,
Google was handling roughly 100 billion searches per month [8, 9, 14].
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3.1. Application areas
a) Behavior Analytics
b) Location-based interaction Analytics
c) Recommender systems development
d) Link prediction
e) Customer interaction and Analytics & marketing
f) Media use
g) Security
h) Social studies
3.2. Challenges of social media analytics
a) Massive amounts of data require lots of storage space and processing power.
b) Shifting social media platforms.
c) Worldwide online accessibility provides more data in many languages.
d) Evolution of online language.
4. CONTENT BASE ANALYTICS
Content Base Analytics means whatever data that store in social media back-end site. For
example Facebook users store their data, photos, and videos on Facebook storage. For this
content they need big amount of storage but now a days number of users increasing rapidly so,
social networking sites like Facebook, twitter, WhatsApp need to increase their storage capacity
day by day and that’s the obstacle because they don’t know how much of storage capacity they
need to increase.
Content-based predictive analytics recommender systems mostly match features (tagged
keywords) among similar items and the user’s profile to make recommendations. When a user
purchases an item that has tagged features, items with features that match those of the original
item will be recommended. The more features match, the higher the probability the user will like
the recommendation. This degree of probability is called precision. [4,6,13] User-based tagging,
however, turns up other problems for a content-based filtering system (and collaborative
filtering) like:
a) Credibility: Not all customers tell the truth (especially online), and users who have only a
small rating history can skew the data. In addition, some vendors may give (or encourage
others to give) positive ratings to their own products while giving negative ratings to their
competitors’ products.
b) Scarcity: Not all items will be rated or will have enough ratings to produce useful data.
c) Inconsistency: Not all users use the same keywords to tag an item, even though the meaning
may be the same. Additionally, some attributes can be subjective. For example, one viewer
of a movie may consider it short while another says it’s too long.
4. 1. Precision with constant feedback
One way to improve the precision of the system’s recommendations is to ask customers for
feedback whenever possible. Collecting customer feedback can be done in many different ways,
through multiple channels. Some companies ask the customer to rate an item or service after
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purchase. Other systems provide social-media-style links so customers can ―like‖ or ―dislike‖ a
product.
4.2. Measurement for effectiveness of system recommendations
The success of a system’s recommendations depends on how well it meets two criteria: precision
(think of it as a set of perfect matches — usually a small set) and recall (think of it as a set of
possible matches — usually a larger set). Issues in measurement for effectiveness:
a) Precision measures how accurate the system’s recommendation was. Precision is difficult to
measure because it can be subjective and hard to quantify.
b) Some recommendations may connect with the customer’s interests but the customer may still
not buy. The highest confidence that a recommendation is precise comes from clear evidence:
The customer buys the item. Alternatively, the system can explicitly ask the user to rate its
recommendations.
c) Recall measures the set of possible good recommendations your system comes up with.
Think of recall as an inventory of possible recommendations, but not all of them are perfect
recommendations. There is generally an inverse relationship to precision and recall. That is,
as recall goes up, precision goes down, and vice versa.
The ideal system would have both high precision and high recall. But realistically, the best
outcome is to strike a delicate balance between the two. Emphasizing precision or recall really
depends on the problem you’re trying to solve [4,6,13].
5. TEXT ANALYTICS
Most of all information or data is available in textual form in databases. From these contexts,
manual Analytics or effective extraction of important information are not possible. For that it is
relevant to provide some automatic tools for analyzing large textual data. Text analytics or text
mining refers process of deriving important information from text data. It will use to extract
meaningful data from the text. It use many ways like associations among entities, predictive
rules, patterns, concepts, events etc. based on rules. Text analytics widely use in government,
research, and business needs. Data simply tells you what people did but text analytics tell you
why. From unstructured or semi structured text data all information will retrieve. From all textual
data it will extract important information. After extracting information it will be categorized. And
from these categorized information we can take decision for business [5, 6].
5.1. Steps for Text Analytics system (Figure -1):
a) Text: In initial stage data is unstructured.
b) Text processing: All information will transfer in Semantic Syntactic text.
c) Text transformation: In it important text will extract for future use.
d) Feature selection: In it data is counted and display in Statistics format.
e) Data mining: All data is classified and clustered.
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Figure 1. The steps for Text Analytics system
5.2. Text Analytics applications areas:
a) Security application: It will we monitoring and analyzing internet blogs, news, social sites
etc. for national security purpose. It will use full detect unethical thing on internet.
b) Marketing application: By analyzing text data we can identify which type of product
customer most like.
c) Analyzing open – ended survey responses: In survey research one company ask to customer
some question like, pros and cons about some products or asking for suggestion. For
analyzing these types of data, text analytics is require.
d) Automatic process on emails and messages: By using big data analytics we can filter huge
amount of emails based on some terms or words. It is also useful when you want to
automatically divert messages or mails to appropriate department or section.
5.3. Distinct Aspects of Text in Social Media:
a) Time Sensitivity: An important feature of the social media services is their real-time nature.
With the rapid growth of the content and communication styles, text is also changing. As the
time sensitivity of the textual data the people’s thoughts also changes from time to time.
b) Short Length: Successful processing of the short texts is essential for the text analytics
method. As the messages are short, it makes people more efficient with their participation in
social networking websites. Short messages are used in social media which consists of few
phrases or sentences.
c) Unstructured Phrases: An important difference between the text in social media and
traditional media is the difference in the quality of content. Different people posts different
things according to their knowledge, ideas, and thoughts. When composing a message also
many new abbreviations and acronyms are used for e.g. How r u? ―Gr8‖ are actually not
words but they are popular in social media.
5.4. Applying Text Analytics to Social Media:
a) Event Detection: It aims to monitor a data source and detect the occurrence of an event that is
to be captured within that source. These data sources includes images, videos, audios, text
documents.
b) Collaborative Question Answering: As social networking websites has emerged, the
collaborative question answering services have also emerged. It includes several expert
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people to answer the questions posted by the people. A large number of questions and
answers are posted on the social networking websites.
c) Social Tagging: Tagging of the data has also increased to a great extent. For example when
any particular user is looking or searching for a recent event like ―Bihar Election‖ then the
system will return the results that are tagged as ―Bihar‖ or ‖Election‖.
Textual data in social media provides lots of information and also the user-generated content
provides diverse and unique information in forms of comments, posts and tags. [5,6]
6. AUDIO ANALYTICS
Audio analytics is the process of compressing data and packaging the data in to single format
called audio. Audio Analytics refers to the extraction of meaning and information from audio
signals for Analysis. There are two way to represent the audio Analytics is 1) Sound
Representation 2) Raw Sound Files. Audio file format is a format for store digital audio data on a
system. There are three main audio format: Uncompressed audio format, Lossless compressed
audio format, Lossy compressed audio format. [11]
5.1. Application Area of Audio Analytics:
The audio is the file format that used to transfer the data to one place to another. Audio analytics
is used to check whether given audio data is available in proper format or in similar format that
sender send. The Application of audio Analytics are many:
a) Surveillance application: Surveillance application is based on approach for systematic choice
of audio classes for detection of crimes done in society. A surveillance application is based
on audio Analytics framework is the only way to detect suspicious kind of activity. The
application is also used to send some important information to surveillance at some crisis
situation urgently.
b) Detection of Threats: The audio mechanism is used to indentify the thread that take place
between sender and receiver.
c) Tele-monitoring System: New technology have camera with the facilities to record the audio
also. Audio Analytics may provide effective detection of screams, breaking glass, gun sound,
explosions, calling for help sound etc. Combination of audio Analytics and video Analytics
in single monitoring system result as a good threat detection efficiency.
d) Mobile Networking System: The Mobile networking system is used to talk or transfer
information to one place to another place. Sometimes due to some network problem the audio
sound is not work properly at that time Audio Analytics is used to find the information that
not send properly due to some problems.
7. VIDEO ANALYTICS
Video is a major issue when considering big data. Videos and images contribute to 80 % of
unstructured data. Now a days, CCTV cameras are the one form of digital information and
surveillance. All these information is stored and processed for further use, but video contains lots
of information and is generally large in size. For example YouTube has innumerable videos
being uploaded every minute containing a massive information. Not all video are important and
viewed largely. This creates a situation where videos create a junk and hard-core contribution to
big data problems. Apart from videos, surveillance cameras generate a lot of information in
seconds. Even a small Digital camera capturing an image stores millions of pixel information in
mille seconds.
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VIDEO Data Analytics dimensions - Volume: Size of video being more, takes the network as
well as the server, time for processing. Low bandwidth connections create traffic on network as
these videos deliver slowly. When stored on mass storage on secondary storage requires huge
amount of space and takes more time retrieving as well as processing. Variety: Videos consisting
of various format and variety such as HD videos, Blu-ray copies etc. Velocity: It is speed of data.
Now a days, Digital cameras process and capture videos at a very high quality and high speed.
Video editing makes it to grow in size as it contains other extra information about the videos.
Videos grow in size faster as they are simply nothing but collection of images.[7]
7.1. Application of video analytics:
a) Useful in accident cases: With the use of CCTV cameras we can identify what happened at
the time of accident it’s also used for security reason and parking vehicles etc.
b) Useful in schools, traffic police, business, security etc.
c) Video Analytics for investigation (Video Search): Video analytics algorithms is implemented
to analyze video, a task that is challenging and its very time consuming for human operator
especially when there is large amount of data are available using video analytics we can
search particular video when we required.
d) Video analytics for Business Intelligence: It uses to extracts statistical and operational data.
Rather than having operator that review all the video and tally all the people or cars moving
in certain area, or checking which traffic routes are most commonly taken, video analytics
can do it automatically.
e) Target and Scene Analytics: Video Analytics for business Intelligence involves target and
scene Analytics. Target Analytics provides details information about the target movement,
patterns, appearance and other characteristics which can be used for identification of target.
f) Direction Analytics: Direction Analytics is the ability to distinguish behavior by assigning
specific values (low to high) to areas within a camera’s field of view.
g) Remove the human equation through the automation: It removes the tedium involved in
giving one or more set of eyes on a monitor for an extended period of time. The automation
of video analytics allows the insertion of human judgment at the most critical time in the
surveillance process.
7. CONCLUSION AND FUTURE WORK
Now, computer industry accept Big Data as a new challenge for all types of machine automated
systems. There are many issues in storage, management, and retrieval of data known as Big Data.
The main problem is how we can use this data for increasing business and improvement in living
standard of people. In this paper we are discussing the issues, challenges, application as well as
proposing some actionable insight for Big Data. It will motivate researchers for finding
knowledge from the big amount of data available in different forms in different areas.
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AUTHORS
Jai Prakash Verma is associated with Nirma University since 2006. He joined as a
Lecturer in MCA section - Computer Science and Engineering Department. He received
Bachelor in Science (B Sc in PCM) and MCA from University of Rajasthan, Jaipur. He
is currently pursuing his PhD from Charusat University, Changa in the area of Big Data
Analysis. Data Warehousing and Mining is the main area of his expertise. He has been
actively involved in many STTP organized within Nirma university. He is currently
working as Assistant Professor in MCA Section - Computer Science and Engineering Department, Institute
of Technology, Nirma University.
Smita Agrawal received Bachelor in Science (B.Sc. in Chemistry) degree from Gujarat
University, Gujarat, India in 2001 and Master’s Degree in Computer Applications
(M.C.A) from Gujarat Vidhyapith, Gujarat, India in 2004. She is pursuing PhD in
Computer Science and Applications from Charotar University of Science and
Technology (CHARUSAT). She is associated with Computer Science and Engineering
Department of Instutute of Technology - Nirma University since 2009. Her research
interests include Parallel Processing, Object Oriented Analysis & Design and Programming Language(s).
11. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
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Bankim Patel, is currently working as a Director and Professor of Computer Science at
Shrimad Rajchandra Institute of Management and Computer Application, Uka Tarsadi
University, Bardoli. He has 26 years of experience in research and teaching in Computer
Science and Applications. Having obtained Ph.D. from Veer Narmad South Gujarat
University in 1996, he had guided and produced 10 Ph. D. and 2 M. Phil. Degree holders in
Computer Science. Currently 8 research scholars are working under him for their Ph.D.
degree. He has authored 60+ papers in international/ national reputed journals, 2 articles
and 4 books so far. He has been awarded several awards like best research paper award
from Indian Science Congress, Significant Contribution award from CSI, Indira Gandhi Excellence award
from International Business Council, by Microsoft IT Academy for reorganization of commitment to
student success through excellence in IT education, Vikas Rattan Award etc. He is the Chief Editor of
National Journal of System and Information Technology (NJSIT) and member of editorial board of several
research journals like International Journal of Information and Computing Technology. He has lead the
Editorial committees for bringing out Proceedings of several National conferences on different themes of
Computer, IT and Knowledge management topics. His area of research includes Natural Language
Processing, Intelligent Systems, and Operating systems.
Atul Patel received Bachelor in Science B.Sc (Electronics), M.C.A. Degree from Gujarat
University, India. M.Phil. (Computer Science) Degree from Madurai Kamraj University,
India. He has received his Ph.D degree from S. P. University. Now he is Professor and
Dean, Smt Chandaben Mohanbhai Patel Institute of Computer Applications – Charotar
University of Science and Technology (CHARUSAT) Changa, India. His main research
areas are wireless communication and Network Security.