Graph intelligence: the future of data-driven investigations

•

1 gostou•959 visualizações

RDF and graph databases are on the rise. The performances, flexibility, and scalability of these systems are attracting a large number of organizations struggling with complex and connected data. While the graph approach offers several advantages, finding insights into the enormous volume of data remains a challenge. In this presentation, we will introduce Graph Intelligence, an advanced combination of human and computer-based intelligence to find insights faster in complex connected datasets. We will explain why we believe this approach is the future for teams of investigators fighting financial crime, national security threats or cyber attacks. From this presentation, you will learn: The nature and benefits of the Graph Intelligence approach How to build a platform leveraging graph technology Real-life examples of money laundering and financial crimes detection and investigation

Tecnologia

SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
Graph intelligence: the
future of data-driven
investigations.

Challenge #2 : where
to look for insights
in the graph.

Challenge #3 : how
to empower your
team.

Turning your data
into a graph.
Graph DBStructured
data ETL
One single structured data source.

Turning your data
into a graph.
Data
sources
NLP
Processing Enrichment
ML
Processing
Knowledge
Graph
Example of a data pipeline for
unstructured data.

Finding the relevant
nodes.
Centrality metrics (PageRank, degree
centrality, closeness centrality,
betweenness centrality).

Finding the relevant
nodes.
Community detection (Louvain, label
propagation).

Finding the relevant
nodes.
Domain-specific algorithms.

Using the graph to
make decisions.
Trans
action
ID
Amount From To
1 $1,500 3751-5710-8
92-2316
6761-0851-36
55-5908
2 $500 3751-5710-8
92-2316
3607-6992-76
8-869
3 $2,000 6761-0851-3
655-5908
3607-6992-76
8-869
Phone from Phone to Duratio
n
585-787-8889 123-458-896
4
00:55s
123-458-8964 585-787-888
9
01:21s
356-589-0312 123-458-896
4
55:00s
Name Phone # Bank #
John
Smith
123-458-89
64
3607-6992-768-8
69
Paul
Simon
585-787-88
89
6761-0851-3655-
5908
Jane
Doe
356-589-03
12
3751-5710-892-2
316

Using the graph to
make decisions.
123-458-8964
3607-6992-768
-869
John Smith
Paul Simon
Jane Doe
6761-0851-365
5-5908
3751-5710-892
-2316
585-787-8889
356-589-0312

Panama Papers
pipeline.
Graph DB Analysis
Structured
data
ETLUnstructured
data
Enrichment

The compliance team
of BforBank uses
Linkurious Enterprise
for fraud detection.

The analyst team of
AEI uses Linkurious
Enterprise for
intelligence
analysis.

Questions?
www.linkurio.us
contact@linkurio.us

Mais conteúdo relacionado

Mais procurados

Using Knowledge Graphs to Predict Customer Needs and Improve Quality

Neo4j

Today's analytical graph databases are taking organizations to another level by connecting all their data, representing knowledge better, and obtaining answers to deeper questions in real time. These benefits extend to the world of machine learning and AI. This talk will illustrate several ways in which graph databases and graph analytics can deliver smarter AI: 1. Unsupervised learning with graph algorithms. 2. Feature extraction and enrichment with graph patterns. 3. In-database ML techniques for graphs

Shift Remote: AI: Smarter AI with analytical graph databases - Victor Lee (Ti...

Shift Conference

Graph technology meetup slides

Sean Mulvehill

Graph Data Science DEMO for fraud analysis

Neo4j

AI, ML and Graph Algorithms: Real Life Use Cases with Neo4j

Ivan Zoratti

4. Document Discovery with Graph Data Science

Neo4j

The contents include what graph databases are, their uses, notations, structure, what is neo4j, its components, what is Graph Data Science and GDS algorithms and their types in Neo4j. It contains an overview of all the features provided by neo4j like querying, visualization, remote access, etc. It will also include information about Neo4j Aura, Sandbox, Desktop, Browser and Bloom. The various tiers of maturity of GDS algorithms and their types will also be explained along with an example of each of the type of algorithms. Akanksha Junawane | Y. L. Puranik "Graph Databases and Graph Data Science in Neo4j" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42358.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42358/graph-databases-and-graph-data-science-in-neo4j/akanksha-junawane

Graph Databases and Graph Data Science in Neo4j

ijtsrd

Graph analytic and machine learning

Stanley Wang

Knowledge Graph for Machine Learning and Data Science

Cambridge Semantics

When it comes to dealing with large, complex, and disparate data sets, traditional database technologies are unable to keep pace with the rich analytics necessary to power today’s data-driven applications. Graph analytics databases are becoming the underlying infrastructure for AI and machine learning. These databases allow users to ask complex questions across complex data, which is not always practical or even possible at scale using other approaches. They also enable faster insights against massive data sets when combined with pattern recognition, statistical analysis, and AI/ machine learning. And in the case of standards-based graph databases, they connect with popular visualization tools like Graphileon, allowing users to easily explore their data stores and quickly build compelling graph-based applications.

Scalable, Fast Analytics with Graph - Why and How

Cambridge Semantics

Programming is an unforgiving art form in which even minor flaws can cause rockets to explode, data to be stolen, and systems to be compromised. Today, a system tasked to automatically identify these flaws not only faces the intrinsic difficulties and theoretical limits of the task itself, it must also account for the many different forms in which programs can be formulated and account for the awe-inspiring speed at which developers push new code into CI/CD pipelines. So much code, so little time. The code property graph – a multi-layered graph representation of code that captures properties of code across different abstractions – (application code, libraries and frameworks) – has been developed over the last six years to provide a foundation for the challenging problem of identifying flaws in program code at scale, whether it is high-level dynamically-typed Javascript, statically-typed Scala in its bytecode form, the syntax trees generated by Roslyn C# compiler, or the bitcode that flows through LLVM. Based on this graph, we define a common query language based on formal code property graph specification to elegantly analyze code regardless of the source language. Paired with the formulation of a state-of-the-art data flow tracker based on code property graphs, we arrive at a distributed cloud native powerful code analysis. This talk provides an introduction to the technology.

Elegant and Scalable Code Querying with Code Property Graphs

Connected Data World

by Lukas Masuch, Henning Muszynski and Benjamin Raethlein The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!

Enterprise Knowledge Graph

Benjamin Raethlein

Graph Analytics

Khalid Salama

BrightTALK - Semantic AI

Semantic Web Company

Sustainability Investment Research Using Cognitive Analytics

Cambridge Semantics

GraphTour 2020 - Graphs & AI: A Path for Data Science

Neo4j

Graph + AI World 2020: Opening Day Keynote

TigerGraph

Neo4j Product Update and Bloom Demo

Neo4j

Your Roadmap for An Enterprise Graph Strategy

Neo4j

Neo4j Graph Data Science Training - June 9 & 10 - Slides #7 GDS Best Practices

Neo4j

Mais procurados (20)

Using Knowledge Graphs to Predict Customer Needs and Improve Quality

Shift Remote: AI: Smarter AI with analytical graph databases - Victor Lee (Ti...

Graph technology meetup slides

Graph Data Science DEMO for fraud analysis

AI, ML and Graph Algorithms: Real Life Use Cases with Neo4j

4. Document Discovery with Graph Data Science

Graph Databases and Graph Data Science in Neo4j

Graph analytic and machine learning

Knowledge Graph for Machine Learning and Data Science

Scalable, Fast Analytics with Graph - Why and How

Elegant and Scalable Code Querying with Code Property Graphs

Enterprise Knowledge Graph

Graph Analytics

BrightTALK - Semantic AI

Sustainability Investment Research Using Cognitive Analytics

GraphTour 2020 - Graphs & AI: A Path for Data Science

Graph + AI World 2020: Opening Day Keynote

Neo4j Product Update and Bloom Demo

Your Roadmap for An Enterprise Graph Strategy

Neo4j Graph Data Science Training - June 9 & 10 - Slides #7 GDS Best Practices

Semelhante a Graph intelligence: the future of data-driven investigations

Leveraging Graphs for AI and ML - Alicia Frame, Neo4j

Neo4j

Python for Data Analysis: A Comprehensive Guide

Aivada

How Graph Databases used in Police Department?

Samet KILICTAS

Survey on Location Based Recommendation System Using POI

IRJET Journal

How Graphs are Changing AI

Neo4j

Map Reduce in Big fata

Suraj Sawant

Finding answers through visualization (GraphDay Barcelona Feb 2016)

Linkurious

KIT-601 Lecture Notes-UNIT-1.pdf

Dr. Radhey Shyam

Poster Abstracts

butest

Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge. Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.

Graph-based Network & IT Management.

Linkurious

Neo4j Graph Data Science - Webinar

Neo4j

3. Relationships Matter: Using Connected Data for Better Machine Learning

Neo4j

Ijariie1184

IJARIIE JOURNAL

Ijariie1184

IJARIIE JOURNAL

Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise. Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage. In this presentation, you will learn about: - Challenges and risks of product development and data management, - How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements, - How to conduct BOM and change management with Linkurious.

Graph-based Product Lifecycle Management

Linkurious

Technical Paper Presentation on data analytics.pptx

A62AnushaGoyalCST

Data centric business and knowledge graph trends

Alan Morrison

Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts. Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities. In this webinar, you will learn about: - The current trends of eCommerce frauds and the risks for organizations; - The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach; - How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.

Detecting eCommerce Fraud with Neo4j and Linkurious

Neo4j

La bi, l'informatique décisionnelle et les graphes

Cédric Fauvet

Sharma social networks

varre vidya sagar

Semelhante a Graph intelligence: the future of data-driven investigations (20)

Leveraging Graphs for AI and ML - Alicia Frame, Neo4j

Python for Data Analysis: A Comprehensive Guide

How Graph Databases used in Police Department?

Survey on Location Based Recommendation System Using POI

How Graphs are Changing AI

Map Reduce in Big fata

Finding answers through visualization (GraphDay Barcelona Feb 2016)

KIT-601 Lecture Notes-UNIT-1.pdf

Poster Abstracts

Graph-based Network & IT Management.

Neo4j Graph Data Science - Webinar

3. Relationships Matter: Using Connected Data for Better Machine Learning

Ijariie1184

Graph-based Product Lifecycle Management

Technical Paper Presentation on data analytics.pptx

Data centric business and knowledge graph trends

Detecting eCommerce Fraud with Neo4j and Linkurious

La bi, l'informatique décisionnelle et les graphes

Sharma social networks

Mais de Connected Data World

After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks. In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set. -- Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them. --

Systems that learn and reason | Frank Van Harmelen

Connected Data World

While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models. RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different. Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin). Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.

Graph Abstractions Matter by Ora Lassila

Connected Data World

"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right. Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide. The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary. This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.

Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...

Connected Data World

A talk by Aleksa Gordic | Software - Deep Learning engineer, Microsoft | The AI Epiphany What can you learn about Graph Machine Learning in 2 months? Aleksa Gordic, Machine Learning engineer @ Microsoft and Founder @ The AI Epiphany, shares his journey in the world of Graph Machine Learning. Aleksa started exploring the basics in the world of Graph Machine Learning, and ended up implementing and open sourcing his own Graph Attention Network on PyTorch. In this talk, Aleksa will share the fundamentals of Graph Machine Learning, provide real-world examples, resources, and everything his younger self would be grateful for. Aleksa will also be available to answer questions. What is Graph Machine Learning? Simply put, Graph Machine Learning is a branch of machine learning that deals with graph data. Graphs consist of nodes, that may have feature vectors associated with them, and edges, which again may or may not have feature vectors attached. The applications are endless. Massive-scale recommender systems, particle physics, computational pharmacology / chemistry / biology, traffic prediction, fake news detection, and the list goes on and on.

How to get started with Graph Machine Learning

Connected Data World

In recent years graphs have been increasingly adopted in financial services for everything from fraud detection to Know Your Customer (KYC) to regulatory requirements. At the same time Environmental Social Governance (ESG) investing has become the fastest growing segment of financial services. In this session James discusses how many of these historical graph techniques are now being enhanced for the era of sustainable investing. Going beyond definitions, let's identify use cases, discuss news and trends, and wrap up with an ask me anything session.

Graphs in sustainable finance

Connected Data World

From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2

Connected Data World

From Taxonomies and Schemas to Knowledge Graphs: Part 3

Connected Data World

For as long as people have been thinking about thinking, we have imagined that somewhere in the inner reaches of our minds there are ghostly, intangible things called ideas which can be linked together to create representations of the world around us — a world that has a certain structure, conforms to certain rules, and to a certain extent, can be predicted and manipulated on the basis of our ideas. Rationalist philosophers have struggled for centuries to make a solid case for this intuitive, almost inborn view of human experience, but it is only with the advent of modern computing that we have the opportunity to build machines which truly think the way we think we think. For the first time, we can give concrete form to our mental representations as graphs or hypergraphs, explicitly specify our mental schemas as ontologies, and formally define the rules by which we reason and act on new information. If we so choose, we can even use these human-like building blocks to construct systems that carry far more information than any single human brain, and that connect and serve millions of people in real time. As enterprise knowledge graphs become increasingly mainstream, we appear to be headed in that direction, although there is no guarantee that the momentum will continue unless actively sustained. Where knowledge graphs are likely to be the most essential, in the long run, is at the interface between human and machine; mental representation versus formal knowledge representation. In this talk, we will take a step back from the many practical and social challenges of building large-scale knowledge graphs, which at this point are well-known. Instead, we will take up the quest for an ideal data model for knowledge representation and data integration, seeking common ground among the most popular data models used in industry and open source software, surveying what we suspect to be true of our own inner models, and previewing structure and process in Apache TinkerPop, version 4. We will also take a tentative step forward into the world of augmented perception via graph stream processing.

In Search of the Universal Data Model

Connected Data World

Graph databases are everywhere right now. The explosive growth in the graph market coupled with the hype of solving graph problems is causing both excitement and confusion. From labeled property graphs to RDF to pure graph analytics to multi-model databases, the breadth of graph offerings is staggering. The good news? DataStax has been listening—and building. In this session, we’ll show you how DataStax Graph is architected into Apache Cassandra to deliver the world’s most scalable graph database. You’ll learn how to integrate Cassandra data into mixed workloads, design scalable property graphs, and even turn your existing tables into graphs. With your high throughput time series data distributed next to its relationships, what will you build next?

Graph in Apache Cassandra. The World’s Most Scalable Graph Database

Connected Data World

As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management. The Data strategy at JP Morgan intends to: a) generate business value b) adhere to regulatory & compliance requirements c) reduce barriers to access d) democratize access to data In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer: 1) What Data do I need? 2) What Data do we have? 3) Where does my Data come from? 4) Where should my Data come from? 5) What Data should be shared most?

Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...

Connected Data World

Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace. This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology. Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data. Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc. Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018). Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance. Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US. We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry. Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.

Graph Realities

Connected Data World

Making true “molecule”-“mechanism”-“observation” relationship connections is a time consuming, iterative and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections. The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting resource from resource-constrained data science departments to refine and redo highly similar ad hoc searches. The result of this is impairment of both the pace and quality of scientific reviews. In this presentation, I show how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. I will take the audience through our pilot journey and highlight practical learnings that should inform subsequent endeavours.

Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...

Connected Data World

Knowledge graphs promise a novel platform for better holistic decision making and analytics. Many projects fail to reach their full potential because of the prohibitively high cost of integrating new knowledge from the required information sources. The talk explains the concept of semantic similarity as a tool for efficient entity clustering and matching based on graph and text embeddings. It will demonstrate the underlying scalable and easy to understand algorithm of Random Indexing. This work is part of the Ontotext Platform, which increases productivity in developing and maintaining large scale knowledge graphs. The platform enables enterprises to develop and operate on top of such mission-critical systems for decision support, information discovery and metadata management.

Semantic similarity for faster Knowledge Graph delivery at scale

Connected Data World

What is the key to the holistic success of the fastest growing and most successful companies of our time globally? Well, often the key is the rapid increase in collected and analysed data. Graph databases provide a way to organise semantically by classes, not tables, are web-aware, and are superior for handling deep, complex relationships than traditional relational or NoSQL data stores. It is these deep, complex relationships that can provide the rich context for hyper-personalising your product offering, inspiring consumers to purchase. In this talk, we describe how we are using artificial intelligence at Farfetch to not only help build a knowledge graph but also to evolve our insights with state-of-the-art graph-based AI.

Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at...

Connected Data World

A world of structured data promises us an incredible future. But most websites struggle to even implement basic schema.org markup. Fewer still represent and connect their pages and content in sophisticated, structured graphs. We can’t reach that incredible future without increasing and improving adoption. To move forward, we need to make constructing rich structured data as easy as writing a recipe. This isn’t a pipe dream: at Yoast, we think we’ve solved schema for everybody, everywhere. We’d love to share our story.

Schema, Google & The Future of the Web

Connected Data World

The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks. To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library. Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph. A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML. This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.

RAPIDS cuGraph – Accelerating all your Graph needs

Connected Data World

May graph technology improve the deployment of humanitarian projects? The goal of using what we call “Graphs for good at Action Against Hunger” is to be more efficient and transparent, and this can have a crucial impact on people’s lives. Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there. The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies (KACHE) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available. It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place.

Graph for Good: Empowering your NGO

Connected Data World

In this talk we will go back to basics with ontologies and from that project forwards to their future. I’ll base most of what I talk about on my experience in bio-ontologies, but most experience will be applicable in many domains; most domains are not as special as they think. When it comes down to the basics, we need to know what our data represent or mean; that is where ontologies come into play; We need to know what we’re talking about. Once we have that clear we can proceed. There is much that one can do with data once we know what it means. We can exploit those data through knowing what it represents. We can exploit these data better if our ontologies are also better. In taking this simple point of view forwards, I will use this talk to establish a set of principles for ontologists.

What are we Talking About, When we Talk About Ontology?

Connected Data World

Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease. Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles. At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things. These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable). EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets. The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide: – An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies – Services for automating the annotation of metadata and learning from previous annotations (Zooma) – An ontology mapping and alignment service (OXO) – Tools for working with metadata and ontologies in spreadsheets (Webulous) – Software for enriching documents in search engines to support “semantic” query expansion I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.

Ontology Services for the Biomedical Sciences

Connected Data World

Recommendation systems deliver more ROI than any other investment in Data Analytics. This talk will introduce the most basic but effective Recommendation System called Collaborative Filtering and show how to implement it using the Cypher Graph Query Language. The mathematics behind collaborative filtering will be explained as will the usefulness of Graph in implementing such an engine. We will use AgensGraph, which adds graph capability to PostgreSQL, for the talk. This talk is aimed at those who are new to either Cypher or basic recommendation system theory.

Develop A Basic Recommendation System using Cypher

Connected Data World

Mais de Connected Data World (20)

Systems that learn and reason | Frank Van Harmelen

Graph Abstractions Matter by Ora Lassila

Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...

How to get started with Graph Machine Learning

Graphs in sustainable finance

From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2

From Taxonomies and Schemas to Knowledge Graphs: Part 3

In Search of the Universal Data Model

Graph in Apache Cassandra. The World’s Most Scalable Graph Database

Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...

Graph Realities

Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...

Semantic similarity for faster Knowledge Graph delivery at scale

Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at...

Schema, Google & The Future of the Web

RAPIDS cuGraph – Accelerating all your Graph needs

Graph for Good: Empowering your NGO

What are we Talking About, When we Talk About Ontology?

Ontology Services for the Biomedical Sciences

Develop A Basic Recommendation System using Cypher

Último

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Martijn de Jong

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Overkill Security

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Manulife - Insurer Transformation Award 2024

The Digital Insurer

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Exploring Multimodal Embeddings with Milvus

Zilliz

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Victor Rentea

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Graph intelligence: the future of data-driven investigations

1. SAS founded in 2013 in Paris | http://linkurio.us | @linkurious Graph intelligence: the future of data-driven investigations.

2. Graphs are eating the world.

3. Graph-powered investigations.

4. Challenge #1 : how to build the graph.

5. Challenge #2 : where to look for insights in the graph.

6. Challenge #3 : how to empower your team.

7. Graph intelligence with Linkurious.

8. Turning your data into a graph. Graph DBStructured data ETL One single structured data source.

9. Turning your data into a graph. Data sources NLP Processing Enrichment ML Processing Knowledge Graph Example of a data pipeline for unstructured data.

10. Finding the relevant nodes.

11. Finding the relevant nodes. Centrality metrics (PageRank, degree centrality, closeness centrality, betweenness centrality).

12. Finding the relevant nodes. Community detection (Louvain, label propagation).

13. Finding the relevant nodes. Domain-specific algorithms.

14. Using the graph to make decisions. Trans action ID Amount From To 1 $1,500 3751-5710-8 92-2316 6761-0851-36 55-5908 2 $500 3751-5710-8 92-2316 3607-6992-76 8-869 3 $2,000 6761-0851-3 655-5908 3607-6992-76 8-869 Phone from Phone to Duratio n 585-787-8889 123-458-896 4 00:55s 123-458-8964 585-787-888 9 01:21s 356-589-0312 123-458-896 4 55:00s Name Phone # Bank # John Smith 123-458-89 64 3607-6992-768-8 69 Paul Simon 585-787-88 89 6761-0851-3655- 5908 Jane Doe 356-589-03 12 3751-5710-892-2 316

15. Using the graph to make decisions. Trans action ID Amount From To 1 $1,500 3751-5710-8 92-2316 6761-0851-36 55-5908 2 $500 3751-5710-8 92-2316 3607-6992-76 8-869 3 $2,000 6761-0851-3 655-5908 3607-6992-76 8-869 Phone from Phone to Duratio n 585-787-8889 123-458-896 4 00:55s 123-458-8964 585-787-888 9 01:21s 356-589-0312 123-458-896 4 55:00s Name Phone # Bank # John Smith 123-458-89 64 3607-6992-768-8 69 Paul Simon 585-787-88 89 6761-0851-3655- 5908 Jane Doe 356-589-03 12 3751-5710-892-2 316

16. Using the graph to make decisions. Trans action ID Amount From To 1 $1,500 3751-5710-8 92-2316 6761-0851-36 55-5908 2 $500 3751-5710-8 92-2316 3607-6992-76 8-869 3 $2,000 6761-0851-3 655-5908 3607-6992-76 8-869 Phone from Phone to Duratio n 585-787-8889 123-458-896 4 00:55s 123-458-8964 585-787-888 9 01:21s 356-589-0312 123-458-896 4 55:00s Name Phone # Bank # John Smith 123-458-89 64 3607-6992-768-8 69 Paul Simon 585-787-88 89 6761-0851-3655- 5908 Jane Doe 356-589-03 12 3751-5710-892-2 316

17. Using the graph to make decisions. 123-458-8964 3607-6992-768 -869 John Smith Paul Simon Jane Doe 6761-0851-365 5-5908 3751-5710-892 -2316 585-787-8889 356-589-0312

18. Using the graph to make decisions.

19. Panama Papers demo.

20. Panama Papers pipeline. Graph DB Analysis Structured data ETLUnstructured data Enrichment

21. The compliance team of BforBank uses Linkurious Enterprise for fraud detection.

22. The analyst team of AEI uses Linkurious Enterprise for intelligence analysis.

23. Questions? www.linkurio.us contact@linkurio.us

Graph intelligence: the future of data-driven investigations

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Graph intelligence: the future of data-driven investigations

Semelhante a Graph intelligence: the future of data-driven investigations (20)

Mais de Connected Data World

Mais de Connected Data World (20)

Último

Último (20)

Graph intelligence: the future of data-driven investigations