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.
The global intracranial abscess treatment market is anticipated to attain a CAGR of ~18% over the forecast period from 2022-2030. The market is driven by increasing head injuries from accidents and violence which can lead to intracranial abscesses. The hospital segment is estimated to be the largest due to need for expert care in treatment. The Asia Pacific region is estimated to witness the highest growth due to improving healthcare systems and increasing population. However, difficult diagnosis of intracranial abscesses may restrain market growth.
3D Printed Drugs Market 2017-2027 Shares, Trend and Growth ReportMonica Nerkar
3D printing was pioneered way back in 1986 but has recently begun to enter the public consciousness. Over the past ten years, it has blurred the boundaries between science fiction and fact. It is also known as Additive Manufacturing and is used in the automobile industry, aerospace & defence, retail and in the medical healthcare industry, amongst many others. A major component of this is the 3D printed drugs market. 3D printing helps make what was once expensive and inaccessible much more cost-effective. Can this be more apt and necessary anywhere else than in the field of medicine? 3D printing is already used to print artificial bones, to create surgical materials with 3D scans to replace a damaged or missing bone and even to create hearing aid devices. Skull implants have been made for people with head injuries and even titanium heels to replace bone cancer afflicted patients.
3D-Printed Drugs Market Drivers
There are several factors which help the 3D printed drugs market to grow. One key advantage is their instantaneous solubility. 3D printed drugs are produced using powder bed inkjet printing. The elements of the drug are added in a layer by layer approach akin to 3D printing for any other device. This makes the drugs easier to swallow and can be very helpful for patients suffering from dysphagia. 3D printing could also augment the arrival of individualised drugs, or the creation of a combination of drugs. They could be customised for each patient, which would help much more than batch-produced drugs since they would be created specifically taking into account that patient’s medical history. The 3D printed drug market could also make children far less resistant to taking their required medication, since they may be able to choose the shape, colour, design and even taste of the tablet! These are anticipated to be the main drivers of the 3D printed drug market.
This presentation summarizes our research on 40 companies from around the world that are leveraging Artificial Intelligence to improve the Healthcare Industry. They are all well-funded, have highly qualified CEOs & Boards, and are poised to achieve their product development milestones.
The I-Square Ventures proprietary rating algorithm indicates that almost all of these companies will receive more funding, and/or be acquired by larger companies.
Data mining (DM) in the pharmaceutical industrylurdhu agnes
Data mining techniques can help pharmaceutical companies analyze large datasets to identify hidden patterns and relationships. This allows companies to make more informed decisions. Specifically, data mining allows analysis of clinical, financial, and organizational data to support clinicians, manage treatment pathways, and efficiently use resources. Techniques like classification, prediction, clustering, and association rule mining can be applied to areas like drug discovery, predicting patient responses, and optimizing operations.
Use Of Artificial Intelligence In Healthcare Delivery PowerPoint Presentation...SlideTeam
The document discusses the use of artificial intelligence in healthcare delivery. It provides an overview of the healthcare artificial intelligence industry and market trends. It also examines the global artificial intelligence healthcare market segmentation based on factors such as geographical region, end user, technology, and offerings. Additionally, the document analyzes the market dynamics, growth drivers, restraints, and opportunities in the artificial intelligence healthcare sector.
This document describes a real world health data dashboard called Dashboard + that can expedite sales performance. It utilizes clinical data from over 63.5 million UK patients, including prescribing data, referrals, demographics, and more. The dashboard allows custom analysis of this data to identify high-prescribing practices, clinicians, and regions to improve customer targeting and sales effectiveness. It provides flexible visualizations of prescribing trends, market shares, and comparisons to patient lists. This real-world data is mapped and can be accessed on any device, allowing pharmaceutical companies to optimize sales strategies.
Presentation by Mentus: Best Practices for CROs: Successful Marketing StrategiesBIOCOMCRO
This document outlines strategies for marketing contract research organizations (CROs). It discusses the challenges of marketing in the CRO industry given the small number of customers and complex services. Content marketing is recommended as an effective approach. Case studies are presented on how CROs/CMOs like Sanmina, HollisterStier, and Senn Chemicals have successfully used branding, websites, and social media to differentiate themselves and generate leads. The document provides guidance on developing a marketing plan and implementing both online and offline marketing programs for CROs.
The global intracranial abscess treatment market is anticipated to attain a CAGR of ~18% over the forecast period from 2022-2030. The market is driven by increasing head injuries from accidents and violence which can lead to intracranial abscesses. The hospital segment is estimated to be the largest due to need for expert care in treatment. The Asia Pacific region is estimated to witness the highest growth due to improving healthcare systems and increasing population. However, difficult diagnosis of intracranial abscesses may restrain market growth.
3D Printed Drugs Market 2017-2027 Shares, Trend and Growth ReportMonica Nerkar
3D printing was pioneered way back in 1986 but has recently begun to enter the public consciousness. Over the past ten years, it has blurred the boundaries between science fiction and fact. It is also known as Additive Manufacturing and is used in the automobile industry, aerospace & defence, retail and in the medical healthcare industry, amongst many others. A major component of this is the 3D printed drugs market. 3D printing helps make what was once expensive and inaccessible much more cost-effective. Can this be more apt and necessary anywhere else than in the field of medicine? 3D printing is already used to print artificial bones, to create surgical materials with 3D scans to replace a damaged or missing bone and even to create hearing aid devices. Skull implants have been made for people with head injuries and even titanium heels to replace bone cancer afflicted patients.
3D-Printed Drugs Market Drivers
There are several factors which help the 3D printed drugs market to grow. One key advantage is their instantaneous solubility. 3D printed drugs are produced using powder bed inkjet printing. The elements of the drug are added in a layer by layer approach akin to 3D printing for any other device. This makes the drugs easier to swallow and can be very helpful for patients suffering from dysphagia. 3D printing could also augment the arrival of individualised drugs, or the creation of a combination of drugs. They could be customised for each patient, which would help much more than batch-produced drugs since they would be created specifically taking into account that patient’s medical history. The 3D printed drug market could also make children far less resistant to taking their required medication, since they may be able to choose the shape, colour, design and even taste of the tablet! These are anticipated to be the main drivers of the 3D printed drug market.
This presentation summarizes our research on 40 companies from around the world that are leveraging Artificial Intelligence to improve the Healthcare Industry. They are all well-funded, have highly qualified CEOs & Boards, and are poised to achieve their product development milestones.
The I-Square Ventures proprietary rating algorithm indicates that almost all of these companies will receive more funding, and/or be acquired by larger companies.
Data mining (DM) in the pharmaceutical industrylurdhu agnes
Data mining techniques can help pharmaceutical companies analyze large datasets to identify hidden patterns and relationships. This allows companies to make more informed decisions. Specifically, data mining allows analysis of clinical, financial, and organizational data to support clinicians, manage treatment pathways, and efficiently use resources. Techniques like classification, prediction, clustering, and association rule mining can be applied to areas like drug discovery, predicting patient responses, and optimizing operations.
Use Of Artificial Intelligence In Healthcare Delivery PowerPoint Presentation...SlideTeam
The document discusses the use of artificial intelligence in healthcare delivery. It provides an overview of the healthcare artificial intelligence industry and market trends. It also examines the global artificial intelligence healthcare market segmentation based on factors such as geographical region, end user, technology, and offerings. Additionally, the document analyzes the market dynamics, growth drivers, restraints, and opportunities in the artificial intelligence healthcare sector.
This document describes a real world health data dashboard called Dashboard + that can expedite sales performance. It utilizes clinical data from over 63.5 million UK patients, including prescribing data, referrals, demographics, and more. The dashboard allows custom analysis of this data to identify high-prescribing practices, clinicians, and regions to improve customer targeting and sales effectiveness. It provides flexible visualizations of prescribing trends, market shares, and comparisons to patient lists. This real-world data is mapped and can be accessed on any device, allowing pharmaceutical companies to optimize sales strategies.
Presentation by Mentus: Best Practices for CROs: Successful Marketing StrategiesBIOCOMCRO
This document outlines strategies for marketing contract research organizations (CROs). It discusses the challenges of marketing in the CRO industry given the small number of customers and complex services. Content marketing is recommended as an effective approach. Case studies are presented on how CROs/CMOs like Sanmina, HollisterStier, and Senn Chemicals have successfully used branding, websites, and social media to differentiate themselves and generate leads. The document provides guidance on developing a marketing plan and implementing both online and offline marketing programs for CROs.
Pharma’s future has never looked more promising – or more ominous.
Major scientific, technological and socioeconomic changes will revive
the industry’s fortunes in another decade, but capitalising on these
trends will entail making crucial decisions first
The virtual clinical trials market is expected to reach $13.78 billion by 2027, growing at a CAGR of 12.6%. Key drivers of growth include technological innovation in virtual trials and efforts to increase adoption. The market is segmented by design, indication, phase, and region. North America currently contributes most to market revenue due to supportive infrastructure and regulations. Major players are focused on R&D, partnerships, and new technologies to improve products and expand customer bases.
With businesses today needing to store a lot more data and for longer periods of time, while also empowering their customers to analyze it in real time and in unexpected ways, analytical workloads are fast exceeding what traditional databases and data warehouses are capable of. In this session, MariaDB's Shane Johnson describes modern analytics requirements and employs real-world use cases to show how MariaDB ColumnStore is helping customers meet these new requirements.
Sanofi Aventis Analytics Journey: Transforming Big PharmaYuri Kudryavcev
This document discusses Sanofi-Aventis' transformation of its financial performance management in Asia Pacific through the deployment of a standardized core model in IBM Cognos TM1. Key points:
- Sanofi-Aventis is a global healthcare leader with diversified medicines and vaccines in 100+ countries.
- Previously, each local operation had different non-standard planning and reporting processes, making regional visibility difficult.
- The project aimed to build an analytics platform for standardized planning/reporting across APAC, going mobile, and accelerating key finance processes.
- PMsquare partnered with Sanofi to deploy the core TM1 model to 3 countries every 6 months, balancing standardization
This document outlines various services provided by Pharmalytics including descriptive and predictive analytics of clinical data, pricing, payer and provider information, commercial analytics, and Medicare and Medicaid analysis. Additional services include market research and strategy, regulatory compliance, research analytics using machine learning, natural language processing, data mining, and analytics of IoT, health economics, outcomes research and public health data. Pharmalytics also provides data infrastructure and management, big data analytics, business intelligence, reporting, and digital and media analytics for customers, campaigns, and online channels. The services relate to industries like healthcare, pharma, life sciences, research and development, biotech, medical devices, consumer health and generics.
This document provides information about a digital transformation conference for the pharmaceutical industry to be held in Copenhagen on April 13-14, 2016. The conference aims to share digital initiatives for patients and healthcare professionals and discuss how organizations can better utilize online opportunities. It will feature keynote speakers from companies like LEO Innovation Lab and Google discussing how digital technologies are changing pharma business models and engagement. The agenda also includes sessions on using social media, real-world evidence, direct-to-consumer communication, and enhancing customer experiences through data-driven insights. The conference seeks to inspire participants to embrace new digital channels and approaches to collaborating with customers.
Key lessons learned from the worldwide pandemic
Keynote presentation at the Digital Transformation of Pharmaceutical Industry conference organised by United Journal and Conferences on April 13th 2021
Electronic prescribing or E-Prescribing (e-Rx) is electronic transmission of prescriptions from physician to pharmacists using computer and other mobile devices, such as cell phone and tablets. E-Prescribing system has replaced the phone, paper and fax based method of prescription. The system improves patient safety by reducing prescribing errors due to various reasons, such as illegible handwriting and ambiguous abbreviations. It also reduces healthcare costs over the paper based prescription systems. It permits the physician and other healthcare professionals to regenerate a new prescription, when any prescription error occurs during pharmacy operation.
Artificial Intelligence in Medicine Market Report Size 2021 pptShadab Pathan
Artificial intelligence (AI) in medicine is used to analyze complex medical data by approximating human cognition with the help of algorithms and software.
Deriving Business Value from Big Data using Sentiment analysisCTRM Center
‘Big Data’ are two small words that are widely used to describe the massive growth in data of all forms and that hold; the promise of delivering huge potential business impact. The question is, how?
Today, and increasingly in the future, businesses are surrounded by masses of data and raw information. Some of this data is very relevant but much of it is not. Further, most of that data is unstructured in the form of email, documents, images and different types of social media, blogs, and so on. Unstructured data is notoriously difficult to access and query, it is scattered across many different locations and formats, and it requires some form of preprocessing before it can be analyzed and used. Yet, it is this unstructured type data that is primarily exploding in quantity, representing around 80 per cent of the annual growth of data and doubling in quantity every two years.
By 2018, digital drug launches and social media marketing will be standard for most major pharmaceutical companies as digital technologies transform the industry. By 2019, half of the top 100 life science companies will have conducted at least one clinical trial using wearable devices, and big data projects will have moved from pilots to production at 30% of life science research organizations. Global health data is growing rapidly and becoming more open, requiring companies to make use of this additional information to gain advantages over competitors.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
Connected health, also known as technology-enabled care (TEC), involves the convergence of health technology, digital media, and mobile devices. It enables patients, carers, and healthcare professionals (HCPs) to access data and information more easily and improve the quality and outcomes of both health and social care.
Perficient is a leading digital transformation consulting firm that provides services to Global 2000 companies including information technology, management consulting, and creative capabilities. It delivers vision, execution, and value through outstanding digital experiences, business optimization, and industry solutions. The document discusses several trends in healthcare and clinical trials, including employing mobile applications in research, investing in social listening tools, and keeping clinical trial data secure.
Good Foundations: Building Healthcare M&A and Real EstateDuff & Phelps
Several fundamental shifts have changed the face of healthcare in North America. A new report, “Good Foundations: Building Healthcare M&A and Real Estate,” published in association with Mergermarket, explores the way healthcare companies are increasingly embracing innovative ways to raise capital and fund future projects, including selling real estate assets to third-party capital providers.
How Artificial Intelligence is Playing Vital Role in Disrupting the Pharma In...Tyrone Systems
The way industries are adopting AI and machine learning, it will eventually drive remarkable growth in the coming years. Research says the AI market will reach $89.8 Billion in Annual Worldwide Revenue by 2025.
The pharmaceutical industry is not an exception in this scenario. The impact of Artificial Intelligence in the pharmaceutical industry is undeniable. Many pharma companies throughout the world have already started leveraging AI in their business. Various factors are responsible for the advancement of this cutting-edge technology in the pharmaceutical industry and it has a promising future ahead. Today’s post will discuss everything about this.
This proposal aims to analyze consumer complaint data about financial products and services to help improve the financial marketplace. The author plans to collect complaint data from the CFPB database and use text mining, predictive analytics, and risk analysis techniques to identify products and companies that receive the most complaints and determine how policies could be improved. The analysis may show the complaint ratio for issues found to be invalid, evaluate company responses, and identify those needing most improvement. The results could provide risk percentages for different products and companies to help consumers and encourage better industry practices. While useful information is available, some additional data could strengthen the models.
This presentation provides an overview of advances in data mining techniques for healthcare applications. It discusses the knowledge discovery process, including data selection, preprocessing, transformation, mining, and interpretation. It describes common data mining techniques like classification, regression, clustering, association rule mining and sequence discovery. It gives examples of healthcare applications that use these techniques, such as disease detection, length of stay prediction, and treatment recommendations. Finally, it discusses challenges of using data mining in healthcare like diverse data types and performance issues, and concludes that data mining can significantly benefit the healthcare sector if applied properly.
Information Management in Pharmaceutical IndustryFrank Wang
This document discusses the challenges and opportunities for information management in the biopharma industries. It covers topics like challenges in R&D, clinical trials, sales and marketing productivity. It also discusses the evolving biopharma value chain and how information management can help in areas like pharmaceutical R&D, clinical operations, sales, marketing, manufacturing and supply chain optimization. Finally, it discusses how the pharmaceutical industry is at a crossroads with challenges like declining R&D productivity and changing market landscapes in emerging markets and developed nations.
MarketsandMarkets is a global market research firm that publishes strategically analyzed market research reports to help Fortune 500 companies make better business decisions. They offer a wide range of market research services including syndicated reports, consulting services, and custom research. MarketsandMarkets aims to provide actionable business insights into key industries to assist clients with sustainable growth.
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.
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.
Pharma’s future has never looked more promising – or more ominous.
Major scientific, technological and socioeconomic changes will revive
the industry’s fortunes in another decade, but capitalising on these
trends will entail making crucial decisions first
The virtual clinical trials market is expected to reach $13.78 billion by 2027, growing at a CAGR of 12.6%. Key drivers of growth include technological innovation in virtual trials and efforts to increase adoption. The market is segmented by design, indication, phase, and region. North America currently contributes most to market revenue due to supportive infrastructure and regulations. Major players are focused on R&D, partnerships, and new technologies to improve products and expand customer bases.
With businesses today needing to store a lot more data and for longer periods of time, while also empowering their customers to analyze it in real time and in unexpected ways, analytical workloads are fast exceeding what traditional databases and data warehouses are capable of. In this session, MariaDB's Shane Johnson describes modern analytics requirements and employs real-world use cases to show how MariaDB ColumnStore is helping customers meet these new requirements.
Sanofi Aventis Analytics Journey: Transforming Big PharmaYuri Kudryavcev
This document discusses Sanofi-Aventis' transformation of its financial performance management in Asia Pacific through the deployment of a standardized core model in IBM Cognos TM1. Key points:
- Sanofi-Aventis is a global healthcare leader with diversified medicines and vaccines in 100+ countries.
- Previously, each local operation had different non-standard planning and reporting processes, making regional visibility difficult.
- The project aimed to build an analytics platform for standardized planning/reporting across APAC, going mobile, and accelerating key finance processes.
- PMsquare partnered with Sanofi to deploy the core TM1 model to 3 countries every 6 months, balancing standardization
This document outlines various services provided by Pharmalytics including descriptive and predictive analytics of clinical data, pricing, payer and provider information, commercial analytics, and Medicare and Medicaid analysis. Additional services include market research and strategy, regulatory compliance, research analytics using machine learning, natural language processing, data mining, and analytics of IoT, health economics, outcomes research and public health data. Pharmalytics also provides data infrastructure and management, big data analytics, business intelligence, reporting, and digital and media analytics for customers, campaigns, and online channels. The services relate to industries like healthcare, pharma, life sciences, research and development, biotech, medical devices, consumer health and generics.
This document provides information about a digital transformation conference for the pharmaceutical industry to be held in Copenhagen on April 13-14, 2016. The conference aims to share digital initiatives for patients and healthcare professionals and discuss how organizations can better utilize online opportunities. It will feature keynote speakers from companies like LEO Innovation Lab and Google discussing how digital technologies are changing pharma business models and engagement. The agenda also includes sessions on using social media, real-world evidence, direct-to-consumer communication, and enhancing customer experiences through data-driven insights. The conference seeks to inspire participants to embrace new digital channels and approaches to collaborating with customers.
Key lessons learned from the worldwide pandemic
Keynote presentation at the Digital Transformation of Pharmaceutical Industry conference organised by United Journal and Conferences on April 13th 2021
Electronic prescribing or E-Prescribing (e-Rx) is electronic transmission of prescriptions from physician to pharmacists using computer and other mobile devices, such as cell phone and tablets. E-Prescribing system has replaced the phone, paper and fax based method of prescription. The system improves patient safety by reducing prescribing errors due to various reasons, such as illegible handwriting and ambiguous abbreviations. It also reduces healthcare costs over the paper based prescription systems. It permits the physician and other healthcare professionals to regenerate a new prescription, when any prescription error occurs during pharmacy operation.
Artificial Intelligence in Medicine Market Report Size 2021 pptShadab Pathan
Artificial intelligence (AI) in medicine is used to analyze complex medical data by approximating human cognition with the help of algorithms and software.
Deriving Business Value from Big Data using Sentiment analysisCTRM Center
‘Big Data’ are two small words that are widely used to describe the massive growth in data of all forms and that hold; the promise of delivering huge potential business impact. The question is, how?
Today, and increasingly in the future, businesses are surrounded by masses of data and raw information. Some of this data is very relevant but much of it is not. Further, most of that data is unstructured in the form of email, documents, images and different types of social media, blogs, and so on. Unstructured data is notoriously difficult to access and query, it is scattered across many different locations and formats, and it requires some form of preprocessing before it can be analyzed and used. Yet, it is this unstructured type data that is primarily exploding in quantity, representing around 80 per cent of the annual growth of data and doubling in quantity every two years.
By 2018, digital drug launches and social media marketing will be standard for most major pharmaceutical companies as digital technologies transform the industry. By 2019, half of the top 100 life science companies will have conducted at least one clinical trial using wearable devices, and big data projects will have moved from pilots to production at 30% of life science research organizations. Global health data is growing rapidly and becoming more open, requiring companies to make use of this additional information to gain advantages over competitors.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
Connected health, also known as technology-enabled care (TEC), involves the convergence of health technology, digital media, and mobile devices. It enables patients, carers, and healthcare professionals (HCPs) to access data and information more easily and improve the quality and outcomes of both health and social care.
Perficient is a leading digital transformation consulting firm that provides services to Global 2000 companies including information technology, management consulting, and creative capabilities. It delivers vision, execution, and value through outstanding digital experiences, business optimization, and industry solutions. The document discusses several trends in healthcare and clinical trials, including employing mobile applications in research, investing in social listening tools, and keeping clinical trial data secure.
Good Foundations: Building Healthcare M&A and Real EstateDuff & Phelps
Several fundamental shifts have changed the face of healthcare in North America. A new report, “Good Foundations: Building Healthcare M&A and Real Estate,” published in association with Mergermarket, explores the way healthcare companies are increasingly embracing innovative ways to raise capital and fund future projects, including selling real estate assets to third-party capital providers.
How Artificial Intelligence is Playing Vital Role in Disrupting the Pharma In...Tyrone Systems
The way industries are adopting AI and machine learning, it will eventually drive remarkable growth in the coming years. Research says the AI market will reach $89.8 Billion in Annual Worldwide Revenue by 2025.
The pharmaceutical industry is not an exception in this scenario. The impact of Artificial Intelligence in the pharmaceutical industry is undeniable. Many pharma companies throughout the world have already started leveraging AI in their business. Various factors are responsible for the advancement of this cutting-edge technology in the pharmaceutical industry and it has a promising future ahead. Today’s post will discuss everything about this.
This proposal aims to analyze consumer complaint data about financial products and services to help improve the financial marketplace. The author plans to collect complaint data from the CFPB database and use text mining, predictive analytics, and risk analysis techniques to identify products and companies that receive the most complaints and determine how policies could be improved. The analysis may show the complaint ratio for issues found to be invalid, evaluate company responses, and identify those needing most improvement. The results could provide risk percentages for different products and companies to help consumers and encourage better industry practices. While useful information is available, some additional data could strengthen the models.
This presentation provides an overview of advances in data mining techniques for healthcare applications. It discusses the knowledge discovery process, including data selection, preprocessing, transformation, mining, and interpretation. It describes common data mining techniques like classification, regression, clustering, association rule mining and sequence discovery. It gives examples of healthcare applications that use these techniques, such as disease detection, length of stay prediction, and treatment recommendations. Finally, it discusses challenges of using data mining in healthcare like diverse data types and performance issues, and concludes that data mining can significantly benefit the healthcare sector if applied properly.
Information Management in Pharmaceutical IndustryFrank Wang
This document discusses the challenges and opportunities for information management in the biopharma industries. It covers topics like challenges in R&D, clinical trials, sales and marketing productivity. It also discusses the evolving biopharma value chain and how information management can help in areas like pharmaceutical R&D, clinical operations, sales, marketing, manufacturing and supply chain optimization. Finally, it discusses how the pharmaceutical industry is at a crossroads with challenges like declining R&D productivity and changing market landscapes in emerging markets and developed nations.
MarketsandMarkets is a global market research firm that publishes strategically analyzed market research reports to help Fortune 500 companies make better business decisions. They offer a wide range of market research services including syndicated reports, consulting services, and custom research. MarketsandMarkets aims to provide actionable business insights into key industries to assist clients with sustainable growth.
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.
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.
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.
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.
Semelhante a BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND SOCIAL MEDIA DATA (20)
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
44
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].
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
45
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
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
46
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.
7. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
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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
51
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.