Graphs in space: a guide to visualizing geospatial networks
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Many of today's geospatial graph projects deal with distance queries or path-finding problems such as, 'what¡¯s my fastest route to the airport?¡¯, or, 'where¡¯s the nearest 5* restaurant?¡¯. Geospatial graphs are good at providing answers fast, but their value doesn¡¯t stop there. They can solve many more complex problems too. Drawing from Cambridge Intelligence¡¯s rich experience in applied graph visualization, this talk looks at how to exploit the rich geographic insight in your graph data through compelling, interactive visualizations. The talk deals with questions such as: What types of map are available? Which ones work well with connected data? Should maps be restricted to high level ¡®dashboards¡¯? Can they convey complex connected data to any depth? What do those analysts who rely on geofencing, chloropleths and coordinate reference systems expect from their interactive maps? Where does connected data fit in? When combining the busy worlds of big connected data and interactive maps, how do we avoid overwhelming users with unnecessary clutter? Join us to explore use case studies in areas as diverse as IT network topology management, car insurance fraud, building planning, and law enforcement. We¡¯ll highlight the variety of ways you can exploit connected geospatial views.
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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).
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"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.
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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.
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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.Graphs in sustainable finance
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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.The years of the graph: The future of the future is here
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Talk by George Anadiotis. Connected Data London Meetup June 29th 2020.
Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the ¡°best tool for the job¡± mindset.
After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years.
The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish.
The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience.
The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle.
The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media.
To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018.
The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We¡¯ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session.From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2
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Parts 1 & 2From Taxonomies and Schemas to Knowledge Graphs: Part 3
From Taxonomies and Schemas to Knowledge Graphs: Part 3Connected Data World
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Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Part 3In Search of the Universal Data Model
In Search of the Universal Data ModelConnected Data World
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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 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.Graph in Apache Cassandra. The World¡¯s Most Scalable Graph Database
Graph in Apache Cassandra. The World¡¯s Most Scalable Graph DatabaseConnected Data World
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1. Building a graph database requires modeling the data, choosing a query language, and providing storage.
2. Existing distributed databases like Cassandra can be used for storage due to their scalability and reliability, though a native graph database provides more functionality.
3. Solving complex graph problems requires capabilities beyond basic queries, including search, analytics, and integration with machine learning, which graph databases are designed to support at scale.Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...Connected Data World
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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?Graph Realities
Graph RealitiesConnected Data World
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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 ¡ª 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 ¡ª along with metadata about authors, their published research, methods used, data providers, data stewards, and so on ¡ª 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.Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...
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Samiul Hasan discusses using question-driven problem solving and data analytics to improve drug discovery at GSK. He outlines aspirations to efficiently organize hypotheses and ensure scientists have access to relevant data and knowledge. Inconsistent language use can cause problems, so self-learning questionnaires could help tag metadata and search literature to improve consistency. Examples of possible algorithms include named entity recognition to determine context, document classification to present similar content, and trigger event detection to alert authors. A pilot found the approach uncovered missed evidence and hypotheses. Hasan concludes technology can help questions drive problem solving if applied persistently and patiently.Semantic similarity for faster Knowledge Graph delivery at scale
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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.
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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.Schema, Google & The Future of the Web
Schema, Google & The Future of the WebConnected Data World
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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.RAPIDS cuGraph ¨C Accelerating all your Graph needs
RAPIDS cuGraph ¨C Accelerating all your Graph needsConnected Data World
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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.Elegant and Scalable Code Querying with Code Property Graphs
Elegant and Scalable Code Querying with Code Property GraphsConnected Data World
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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 ¨C a multi-layered graph representation of code that captures properties of code across different abstractions ¨C (application code, libraries and frameworks) ¨C 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.From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...Connected Data World
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Do you want to learn how to use the low-hanging fruit of knowledge graphs ¡ª schema.org and JSON-LD ¡ª to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.Graph for Good: Empowering your NGO
Graph for Good: Empowering your NGOConnected Data World
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This document discusses empowering NGOs with graph technology. It describes a pilot project between Action Against Hunger Spain and Graphs for Good to use graph databases and analytics. The goals are to improve knowledge management by modeling complex relationships between data, enable qualitative analysis, and democratize data access to enhance decision making, accountability, and stakeholder engagement while addressing challenges around resources, security, and long-term planning.¦ªnowledge Architecture: Combining Strategy, Data Science and Information Arch...
¦ªnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
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Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...
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- 2. In this talk ¡ñ Visualizing data as a graph ¡ñ Visualizing geospatial data as a graph
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- 12. The problem with maps ? Layouts? ? Missing data? ? Interaction?
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- 17. Handling missing geospatial data
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