Lju Lazarevic
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Graph databases are based on graph theory and use nodes and relationships to store data. They are a type of property graph database that can scale easily. Graph databases are well-suited for applications that rely on relationships between data, need to find patterns in behavioral data, or require linking disparate data sources. Specifically, they are effective for fraud detection by identifying patterns across entities, providing a 360-degree view of customers by linking their various profiles and activities, and making recommendations by leveraging connections in user data.
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Graph databases
Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.
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In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution.
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This, I'll explain, helped fuel the popularity of schema.org, which in turn has caused a demand for more schemas, growing the vocabulary and its capabilities. I'll make the case that schema.org has started to resemble exactly what everyone involved in the initiative declared it shouldn't be: an ontology of everything.
Whether or not that be the case, I'll say, the utility of having a relatively simple, well thought-out, well-understood and very broad vocabulary available has made schema.org (along with JSON-LD) a go-to tool for linked data modelers.
Finally, and with a look at the many ways Google, in particular, has made use of schema.org, I'll explore to what extent its utility extends past being a convenient starting for point for back-of-the napkin knowledge graph development, or whether it's making a significant contribution to realizing the promise of a web of data.
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Executing successfully a Knowledge Graph initiative in an organization requires a series of strategic decisions that need to be taken before and during the execution.
Issues like how to balance the (inevitable) knowledge quality trade-offs, how to prioritize knowledge evolution, or how to allocate resources between new knowledge delivery and technology improvement, are often not contemplated early or adequately enough, resulting into frictions and sub-optimal results.
In this talk, I describe some key strategic dilemmas that Architects and Executives face when designing and executing Knowledge Graph projects, and discuss potential ways to deal with them.
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Even though most RDF-based systems were performing data validation and quality assessment, there was no standardized way to define constraints. People were using ad-hoc solutions or schemas and languages that were not meant for validation.
Thankfully, since 2017 there are 2 additions to the RDF technology stack: SHACL & ShEx. Both provide a high level RDF constraint language that people can use to define data constraints (a.k.a. Shapes), each with different strengths.
This talk provides an outline of different types of RDF data quality issues and existing approaches to quality assessment. The goal is to give an overview of the existing RDF validation landscape and hopefully, inspire people on how to improve their RDF publishing workflows.
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SFScon19 - Grazia Cazzin - KNOWAGE the open source answer to the new needs in...South Tyrol Free Software Conference
Knowage is the open source suite for business analytics that satisfies traditional requirements as well as innovative and challenging informative domains, combining traditional data and big data sources into valuable and meaningful information. Knowage 7.0 comes with great and challenging features that really expand analytics capabilities. Just to mention few of them: data exploration through Solr/ElasticSearch index, native geographical exploration within cockpits and new wizards to faster explore your data.Chalitha Perera | Cross Media Concept and Entity Driven Search for Enterprise
This document discusses an enterprise content management solution called Sensefy that provides semantic search capabilities across heterogeneous data sources. It semantically enhances unstructured content using named entity recognition and linking to external knowledge bases. Sensefy uses the Media In Context (MICO) platform for cross-media analysis and metadata extraction. The system allows for federated search across different repositories as well as entity-driven search with disambiguation and suggestion capabilities. A demo is provided to showcase these semantic search features.
Stephen Buxton | Data Integration - a Multi-Model Approach - Documents and Tr...
This document discusses when to use documents versus triples in a database. It describes the pros and cons of relational databases, document databases, graph databases, and triple stores. It advocates using a hybrid approach that combines documents and triples for the benefits of both. Documents are well-suited for storing heterogeneous data while triples enable modeling relationships and inferring new information. The combination provides a unified platform for querying rich data through semantics.
Understanding voice of the member via text mining
The 14th Text Analytics Summit - June 15, 2015 in New York
Today, Businesses around the world are increasingly collecting tremendous amount of unstructured data in the form of text – from multiple channels such as product reviews, market research, customer care conversations, and social media. In this talk, we will share how LinkedIn has built a text-mining platform to derive insights and create value for our members from the massive amount of data we have within our ecosystem. We will cover the following topics in our talk:
1) Topic modeling
2) Text categorization using NLP features
3) Topic-based sentiment analysis and attribution
The talk will be appropriate for business leaders, researchers and practitioners.
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The Business Case for Semantic Web Ontology & Knowledge Graph
The Business Case for Semantic Web Ontology & Knowledge Graph
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQL
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Enterprise Search Summit Keynote: A Big Data Architecture for Search
Enterprise Search Summit Keynote: A Big Data Architecture for Search
Crafting a Knowledge Graph Strategy - What to think about
Crafting a Knowledge Graph Strategy - What to think about
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The Italian business graph: fueling innovation in finance
Neo4j GraphTalk Amsterdam - Next Generation Solutions using Neo4j
Neo4j GraphTalk Amsterdam - Next Generation Solutions using Neo4j
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RDF Data Quality Assessment - connecting the pieces
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Should a Graph Database Be in Your Next Data Warehouse Stack?
Smarter content with a Dynamic Semantic Publishing Platform
Smarter content with a Dynamic Semantic Publishing Platform
GraphDB Cloud: Enterprise Ready RDF Database on Demand
GraphDB Cloud: Enterprise Ready RDF Database on Demand
SFScon19 - Grazia Cazzin - KNOWAGE the open source answer to the new needs in...
SFScon19 - Grazia Cazzin - KNOWAGE the open source answer to the new needs in...
Chalitha Perera | Cross Media Concept and Entity Driven Search for Enterprise
Chalitha Perera | Cross Media Concept and Entity Driven Search for Enterprise
Stephen Buxton | Data Integration - a Multi-Model Approach - Documents and Tr...
Stephen Buxton | Data Integration - a Multi-Model Approach - Documents and Tr...
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Neo4j - graph database for recommendations
This document discusses using Neo4j, a graph database, for recommendations. It describes modeling data as graphs in Neo4j and developing recommendation algorithms and plugins for it, such as for document similarity, movie recommendations, and restricting recommendations to a subgraph. An example application called TeleVido.tv is also mentioned that provides media content recommendations using Neo4j.
Graph databases
Graph databases are a type of NoSQL database that is optimized for storing and querying connected data and relationships. A graph database represents data in graphs consisting of nodes and edges, where the nodes represent entities and the edges represent relationships between the entities. Graph databases are well-suited for applications that involve complex relationships and connected data, such as social networks, knowledge graphs, and recommendation systems. They allow for flexible querying of relationships and connections via graph traversal operations.
Converting Relational to Graph Databases
Graph Database Management Systems provide an effective
and efficient solution to data storage in current scenarios
where data are more and more connected, graph models are
widely used, and systems need to scale to large data sets.
In this framework, the conversion of the persistent layer of
an application from a relational to a graph data store can
be convenient but it is usually an hard task for database
administrators. In this paper we propose a methodology
to convert a relational to a graph database by exploiting
the schema and the constraints of the source. The approach
supports the translation of conjunctive SQL queries over the
source into graph traversal operations over the target. We
provide experimental results that show the feasibility of our
solution and the efficiency of query answering over the target
database.
Designing and Building a Graph Database Application – Architectural Choices, ...
Ian closely looks at design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.g
NoSQL: Why, When, and How
The document discusses NoSQL databases and CouchDB. It provides an overview of NoSQL, the different types of NoSQL databases, and when each type would be used. It then focuses on CouchDB, explaining its features like document centric modeling, replication, and fail fast architecture. Examples are given of how to interact with CouchDB using its HTTP API and tools like Resty.
Relational vs. Non-Relational
This document compares relational and non-relational databases. It discusses how in 2003 the main databases were relational, but by 2010 non-relational databases grew popular in the "NoSQL movement". However, the document argues that there are no truly new database designs and that relational and non-relational databases can be combined. It advises to choose a database based on the specific problem and features needed rather than general classifications. The document provides examples of which types of databases fit certain data and access needs.
Graph Database, a little connected tour - Castano
This document provides an introduction to graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for certain types of connected data. It uses social network and movie recommendation examples to demonstrate how to model and query data in a graph database using the Cypher query language.
Relational databases vs Non-relational databases
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Graph Based Recommendation Systems at eBay
Recommendation and personalization systems are an important part of many modern websites. Graphs provide a natural way to represent the behavioral data that is the core input to many recommendation algorithms. Thomas Pinckney and his colleagues at Hunch (recently acquired by eBay) built a large scale recommendation system, and then ported the technology to eBay. Thomas will be discussing how his team uses Cassandra to provide the high I/O storage of their fifty billion edge graphs and how they generate new recommendations in real time as users click around the site.
Relational to Graph - Import
Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database?
Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model.
In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.
Semantic Graph Databases: The Evolution of Relational Databases
In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.
An Introduction to Graph Databases
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
Introduction to graph databases GraphDays
This document discusses the rise of graph databases and their advantages over traditional databases. It notes that many major websites and consumer-facing technologies are built around graphs that model relationships in data. Graph databases allow for easier modeling of complex networks and queries involving relationships. They also offer vastly superior performance at scale for highly connected datasets compared to SQL and NoSQL alternatives. Adoption of graph databases is growing rapidly as they transform industries and unlock new competitive advantages for businesses through their ability to leverage relationships in data.
An Introduction to NOSQL, Graph Databases and Neo4j
Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.
Introduction to Graph Databases
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
Data Modeling with Neo4j
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
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Designing and Building a Graph Database Application – Architectural Choices, ...
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Semantic Graph Databases: The Evolution of Relational Databases
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An Introduction to NOSQL, Graph Databases and Neo4j
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Data Mining: Graph mining and social network analysis
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GraphTalks Rome - Selecting the right Technology
Dirk Möller discusses selecting the right database technology, with a focus on graph databases like Neo4j. He outlines the benefits of graph databases over relational and NoSQL databases for connected data, including high performance, easy maintenance, and seamless evolution. Möller also provides examples of common use cases where graph databases have business benefits in areas like recommendations, fraud detection, and network operations.
Keynote: Anything is Possible: Apply Graphs to Your Most Complex Data Problem...
During his presentation, Carl will examine the range of database usages and their corresponding database models. He’ll offer insight into the process of selecting a DBMS, based on purpose, and deployment. Carl will share the benefits of graph databases, consider potential advantages, and recommend approaches to adoption. You’ll learn how the various kinds of graphs serve different purposes and walk away with insights on the future of graph databases in general. If you can think of it, you can graph it.
Using graphs for recommendations
This document contains a presentation on using graph databases for recommendations. It begins with an introduction to graphs and graph theory, then discusses what graph databases are and how they are different from relational databases. It explains how graphs are well-suited for complex querying and representing connected data. The presentation describes how recommendation systems work and how graph algorithms and storing recommendation data in a graph structure provide benefits like real-time recommendations, navigating relationships between items, and efficient operations. It concludes with a demonstration, examples, and discussing future events.
Big data week 2018 - Graph Analytics on Big Data
Graph Analytics (or network analytics), is an area of analysis with numerous applications that increasingly draws more and more attention. From fraud detection and money laundering, illegal transactions and other forms of financial crime, to identify key influencers in social networks, communities of frequently interacting individuals and route optimisation or even bioinformatics; graph analytics offer a vast variety of solutions that keep on evolving on a daily basis; allowing for experts in various fields to tackle every day challenges, extract insights and drive decision making.
Mainly, Graph Analytics is divided into 4 categories:
* Path Analytics;
* Connectivity Analytics;
* Centrality Analytics, and;
* Community Detection analytics;
each of which relies on different algorithms and address different problems.
So, how does one apply these techniques effectively in order to drive hypothesis testing and, eventually, the extraction of actionable insights?
What are the steps that should be followed?
What is the impact of visualisation tools in this process?
How should we sample from graphs?
What tools should one use or be familiar with?
Furthermore, how can scalable and high-quality production-ready solutions be implemented that apply Graph Analytics, giving direct access to visualisations and on-demand analytics’ dashboards, which can serve as an intuitive and amenable means of information interpretation?
In this talk, we present and discuss the different categories of graph analytics and their areas of application. In addition, to address the above questions, we define a methodology for the application of these analytics technologies through example use-cases, studying the steps that need to be followed before assumptions can be confirmed and insights can be extracted.
Finally, we will discuss how distributed programming models, such as Spargel, have been developed to allow for the adoption of graph analytics algorithms by frameworks like Apache Spark and Apache Flink; the challenges and limitations that come with their adoption by these frameworks, and how one can build scalable distributed graph analytics solutions using them.
Neo4j GraphTalks Oslo - Next Generation Solutions built on Neoej
The document discusses next generation solutions built on Neo4j graph database. It provides an agenda for the talk including solutions using Neo4j, recommendations, GDPR, and conclusions. It discusses how graph-based solutions with Neo4j enable flexibility, intuitiveness, and high performance for connected data scenarios. It also provides examples of using Neo4j for recommendation engines in retail, logistics, fraud detection and more. Case studies describe how Walmart and eBay improved recommendations and routing with Neo4j.
Knowledge Graph for Machine Learning and Data Science
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn
This document provides an overview and agenda for a presentation on using graph databases like Neo4j for retail applications. The presentation covers introducing graph databases and Neo4j, discussing retail data types, and demonstrating use cases for customer 360 views, recommendations, supply chain management, and other areas. Case studies are presented on using Neo4j for real-time recommendations at a large retailer and real-time promotions at a top US retailer. The document concludes with an invitation for questions.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...
This webinar covered graph databases and how they can solve problems that were previously difficult for traditional databases. It included presentations on why graph databases are useful, common use cases like recommendations and network analysis, different types of graph databases, and a demonstration of the DataStax Enterprise graph database. There was also a question and answer session where attendees could ask about graph databases and DataStax Enterprise graph.
Top 5 Considerations When Evaluating NoSQL
This document discusses the top 5 considerations for evaluating NoSQL databases: data model, query model, consistency model, APIs, and ecosystem. It provides an overview of common NoSQL data models including key-value, wide-column, and document and describes how their features suit different use cases. The consistency models of eventual and strong consistency are also compared in terms of their tradeoffs for read/write throughput and risk of stale data.
Graph all the things - PRathle
This document provides an introduction to graph databases and their advantages over relational and NoSQL databases for modeling connected data. It discusses how graph databases can unlock value from data relationships in areas like recommendations, fraud detection, and identity management. The document explains that graph databases allow flexible modeling of nodes and relationships, powerful graph queries, and the ability to easily add new types of data over time. It presents the example of Neo4j as the leading graph database and discusses how early adopters were able to gain competitive advantages through new applications and insights leveraging their connected data in a graph model.
Large Scale Modeling Overview
This document discusses large scale modeling and data analysis. It defines large scale modeling as building models that can process very large datasets that are difficult for traditional tools. It provides examples of large scale recommendation models at LinkedIn and discusses how more data allows for better accuracy, deeper insights through exploration, and more flexible feature engineering. Challenges include ensuring infrastructure can handle the data volume and complexities of online versus offline modeling.
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...
This document discusses how Walmart uses Apache Solr as a "not-so-evil twin" to complement their source of truth database and help scale their data infrastructure. It describes how Walmart abstracts the complexity of managing databases, caches, search queries, and messaging to provide scalable querying across database shards. The use of Solr has allowed Walmart to offload queries, recurring reads, analytics
Return on Investment in Linking Content to CRM by Applying the Linked Data Stack
This document discusses using the Ontos Linked Data Information Workbench (OntosLDIW) to integrate data from various sources into a customer relationship management (CRM) system. It describes how OntosLDIW extracts, links, and stores data from text, spreadsheets, databases and other sources as linked data. This allows CRM users to access all required customer information from a variety of sources through a single system. The document estimates that using OntosLDIW to integrate this data into the CRM could provide a 186% return on investment within 3 years by reducing time spent on customer queries from 8 minutes to 1 minute and ensuring up-to-date information is available. It concludes by noting some lessons learned around
ROI in Linking Content to CRM by Applying the Linked Data Stack
Today, decision makers in enterprises have to rely more and more on a variety of data sets that are internally but also externally available in heterogeneous formats. Therefore, intelligent processes are required to build an integrated knowledge-base. Unfortunately, the adoption of the Linked Data lifecycle within enterprises, which targets the extraction, interlinking, publishing and analytics of distributed data, lags behind the public domain due to missing frameworks that are efficiently to deploy and ease to use. In this paper, we present our adoption of the lifecycle through our generic, enterprise-ready Linked Data workbench. To judge its benefits, we describe its application within a real-world Customer Relationship Management scenario. It shows (1) that sales employee could significantly reduce their workload and (2) that the integration of sophisticated Linked Data tools come with an obvious positive Return on Investment.
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Learn the tips and tricks how to handle Data Modeling in your Big Data environment. Mark will show how modeling will add value to the business and how to make your Big Data landscape transparent across the organization.
You will see the latest modeling techniques for Big Data and different types of modeling notations. Also you will learn how to integrate Data Modeling into your BI environment.
Using Graphs for Data Analysis
This document discusses using graphs for data analysis and provides examples of different types of graph algorithms and queries that can be performed on graph data. Some key points:
- Graphs can be used to represent relational datasets and enable different types of analysis than is possible on traditional relational models.
- Common graph algorithms discussed include centrality measures, community detection, pattern matching queries, and shortest path algorithms.
- Examples applications highlighted are fraud detection using financial transaction graphs and topic modeling on text data represented as a graph.
Choosing the Right Graph Database to Succeed in Your Project
The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
Mastering Customer Data on Apache Spark
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
Big data analytics presented at meetup big data for decision makers
Presentation on data science techniques as presented at the Washington DC meet-up on Big Data for Decision Makers.
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Keynote: Anything is Possible: Apply Graphs to Your Most Complex Data Problem...
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Neo4j GraphTalks Oslo - Next Generation Solutions built on Neoej
Neo4j GraphTalks Oslo - Next Generation Solutions built on Neoej
Knowledge Graph for Machine Learning and Data Science
Knowledge Graph for Machine Learning and Data Science
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...
Return on Investment in Linking Content to CRM by Applying the Linked Data Stack
Return on Investment in Linking Content to CRM by Applying the Linked Data Stack
ROI in Linking Content to CRM by Applying the Linked Data Stack
ROI in Linking Content to CRM by Applying the Linked Data Stack
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Choosing the Right Graph Database to Succeed in Your Project
Choosing the Right Graph Database to Succeed in Your Project
Big data analytics presented at meetup big data for decision makers
Big data analytics presented at meetup big data for decision makers
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Systems that learn and reason | Frank Van Harmelen
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.
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Graph Abstractions Matter by Ora Lassila
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.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...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.How to get started with Graph Machine Learning
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.
Graphs in sustainable finance
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
What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases.
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.
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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.
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In Search of the Universal Data Model
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.
Graph in Apache Cassandra. The World’s Most Scalable Graph Database
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...
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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 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.
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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
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.
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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
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 – Accelerating all your Graph needs
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
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.
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Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — 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
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.
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Lju Lazarevic
- 1. What is a graph database?
- 2. Agenda • Graph theory – The Seven Bridges of Königsberg • What is a property graph database? • Why graph databases are great • Where does a graph database dominate? Some examples • Summary
- 3. Graph Theory – The Seven Bridges of Königsberg Node (Vertex) Relationship (Edge)
- 4. What is a property graph database? 4
- 5. Why graph databases are great - buying trainers 5
- 6. Why graph databases are great - buying trainers 6
- 7. Graph use-cases • Fraud Detection • 360° customer view • Recommendations 7
- 8. Great graph use-cases – Fraud detection 8
- 9. Great graph use-cases – Fraud detection
- 10. Great graph use-cases – Fraud detection Pattern: Identical National Insurance Number related to different entities Pattern: Identical phone number related to different entities
- 11. Great graph use-cases – 360° customer view 11
- 12. Great graph use-cases – 360° customer view 12 External CRM Marketing Product Orders
- 13. Great graph use-cases - Recommendations 13
- 14. Summary • Property graph databases: • are based on graph theory • hold properties for the nodes and relationships • can scale and grow easily • Graph databases are a fantastic fit in applications where: • relationships are important as the data they link • we want to search for behavioural patterns • we want to join disparate data sources together 14
- 15. Questions? 15
Editor's Notes
- It all began with graph theory. One of the most popular examples of the use of graph theory application is the Seven Bridges of Konigsberg. Taking the problem, it can be simplified down to nodes (the islands) and relationships (the bridges). Wiki defn: he study of graphs, which are mathematical structures used to model pairwise relations between objects
- Can recommend anything, a product, a service, next best action… Use patterns within the graph to make suggestions/completions based on specific items/item groups bought by others