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Knowledge Graphs Webinar- 11/7/2017
The document discusses knowledge graphs and provides examples of how Neo4j has been used by customers for knowledge graph and graph database applications. Specifically, it discusses how Neo4j has helped organizations like Itau Unibanco, UBS, Airbnb, Novartis, Columbia University, Telia, Scripps Networks, and Pitney Bowes with fraud detection, master data management, content management, smart home applications, investigative journalism, and other use cases by building knowledge graphs and connecting diverse data sources.
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Knowledge Graphs Webinar- 11/7/2017
- 1. Knowledge Graphs #1 Databasefor Connected Data Jeff Morris Head of Product Marketing jeff@neo4j.com 11/7/17
- 2. Agenda • Introduction toNeo4j • Neo4j Definition of Knowledge Graph • Examples
- 3. Who We Are:The Graph Platform for Connected Data Neo4j is an enterprise-grade native graph platform that enables you to: • Store, reveal and query data relationships • Traverse and analyze any levels of depth in real-time • Add context and connect new data on the fly • Performance • ACID Transactions • Agility • Graph Algorithms 3 Designed, built and tested natively for graphs from the start for: • Developer Productivity • Hardware Efficiency • Global Scale • Graph Adoption
- 4. CONSUMER DATA PRODUCT DATA PAYMENT DATA SOCIAL DATA SUPPLIER DATA The next waveof competitive advantage will be all about using connections to identify and build knowledge Knowledge Graphs in The Age of Connections
- 5. Discrete Data ProblemsConnected Data Problems Perspective SELECT foo FROM emp SQL (Ann)-[:LOVES]->(Dan) CypherQuery Language RDBMS GRAPH DB DBMS Architectur e
- 6. Neoj4’s Amazing Customers NASAexplores graph database for deep insights into space International Consortium of Investigative Journalists Wins Pulitzer Prize
- 7. Business Problem • Findrelationships between people, accounts, shell companies and offshore accounts • Journalists are non-technical • Biggest “Snowden-Style” document leak ever; 11.5 million documents, 2.6TB of data Solution and Benefits • Pulitzer Prize winning investigation resulted in robust coverage of fraud and corruption • PM of Iceland & Pakistan resigned, exposed Putin, Prime Ministers, gangsters, celebrities (Messi) • Led to assassination of journalist in Malta Background • International Consortium of Investigative Journalists (ICIJ), small team of data journalists • International investigative team specializing in cross-border crime, corruption and accountability of power • Works regularly with leaks and large datasets ICIJ Panama Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph7
- 8. Business Problem • Findrelationships between people, accounts, shell companies and offshore accounts • Journalists are non-technical • 2017 Leak from Appleby tax sheltering law firm matched 13.4 million account records with public business registrations data from across Caribbean Solution and Benefits • Exposed tax sheltering practices of Apple, Nike • Revealed hidden connections among politicians and nations, like Wilbur Ross & Putin’s son in law • Triggered government tax evasion investigations in US, UK, Europe, India, Australia, Bermuda, Canada and Cayman Islands within 2 days. Background • International Consortium of Investigative Journalists (ICIJ), Pulitzer Prize winning journalists • Fourth blockbuster investigation using Neo4j to reveal connections in text-based, and account- based data leaked from offshore law firms and government records about the “1% Elite” ICIJ Paradise Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph8
- 9. “Graph analysis ispossibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017” IT Market Clock for Database Management Systems, 2014 https://www.gartner.com/doc/2852717/it-market-clock-database-management TechRadar™: Enterprise DBMS, Q1 2014 http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801 Making Big Data Normal with Graph Analysis for the Masses, 2015 http://www.gartner.com/document/3100219 Analyst Expectations Three Years Ago 9
- 10. The Largest GraphInnovation Network 10,000,000+ Downloads & Docker pulls Neo4j Downloads 250+ customers, 500+ startups 50% from Global 2000 100+ Technology and Services Partners 450+ annual events & 10k attendees Graph and Neo4j awareness and training 1,000+ Neo4j GraphConnect NYC Attendees 100,000+ Online and Classroom Education Registrants & Meetup Members
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- 13. 13 “Neo4j continues todominate the graph database market.” Noel Yuhanna Forrester Market Overview: Graph Database Vendors October, 2017
- 14. Why is Neo4jSucceeding? Focus on Simplifying the Adoption, Awareness and Success of Graphs Open Source business model • Commitment to developers – DevRel, Training, Events, etc. • Commitment to sharing Cypher, the SQL for graphs, on Apache Native Graph Technology Leadership • Commitment to data integrity, scale and performance • Expanding User Communities to Data Scientists, IT, Analysts & Business Users Highest Investment in Customer Success • Applications offer real impact, and we spread these success stories
- 15. 15 Neo4j Graph Platform Development& Administration Analytics Tooling BUSINESS USERS DEVELOPERS ADMINS Graph Analytics Graph Transactions Data Integration Discovery & Visualization DATA ANALYSTS DATA SCIENTISTS Drivers & APIs APPLICATIONS AI BIG DATA IT
- 16. 16 Grow Graphs by reachingdeeper into the enterprise with support for more users, roles and use cases
- 17. Connecting Roles inthe Enterprise Data Scientists Real-time Graph traversal Application Data Lake & DWHS Big Data IT & Architecture Developers & Prod Mgrs AI Analysts and Business Users Chief Officers of … Knowledge Graphs Digital Transformation Initiatives Compliance, Data, Digital, Information, Innovation, Marketing, Operations, Risk & Security…
- 18. Real-Time Recommendations Dynamic Pricing Artificial Intelligence &IoT-applications Fraud Detection Network Management Customer Engagement Supply Chain Efficiency Identity and Access Management Relationship-Driven Applications
- 19. Sample of ConnectedGraphs Organization Identity & Access Network & IT Ops
- 20. The Knowledge GraphProblem Organizations have difficulty maintaining their corporate memory due to a variety of reasons: • Growth which drives need for new and continuous education • Digitalization / Digital Transformation initiatives to identify new markets • Turnover where long term knowledge is lost • Aging infrastructures and siloed information
- 21. Negative Consequences • Lackof knowledge sharing slows project progress, and creates inconsistencies even among team members. • Organizations don’t know what they don’t know, nor do they know what they know. • Data Scientists, and therefore the organization, are slow to recognize or react to changing market conditions, therefore they miss opportunities to innovate • Bad information is spread inadvertently which erodes corporate trust • Brand damage when using this info in front of customers
- 22. Purchases RELATIONAL DB WIDECOLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Product Catalogue DOCUMENT STORE Category Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory LocationCategory Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory Products Customers / Users Location Data Lives Across the Enterprise
- 23. Data Lake Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDECOLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Recommendations require an operational workload — it’s in the moment, real-time! Good for Analytics, BI, Map Reduce Non-Operational, Slow Queries
- 24. Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDE COLUMN STORE Views DOCUMENT STORE UserReview RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Connector Apps and Systems Real-Time Queries
- 25. Customer Adress Store Phone Customer Email EmailAdress Phone Product Product Category Y Street Region Product Store Street Category X Simple Enterprise KnowledgeGraphs Customer Graph Product Graph Supply Graph
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- 27. Customer Graph Customer Adress Store Phone Customer Email EmailAdress Phone Product Product Category Y Street Region Product Store Street Category X Product Graph SupplyGraph Unlock the Institutional Memory Real-time product recommendations Fraud Detection Real-time supply chain management Risk Management
- 28. How it shouldbe • Information, especially in Analytics, Research departments and customer service should have a searchable, consistent repository, or representation of a repository, from which to store and draw institutional knowledge. • Corporations who maintain a knowledge graph will develop higher degrees of consistency across all areas of business. • Improving long term corporate memory should be a mandate from the C- suite
- 29. What’s required toget there • Institutional memory requires a solution that can integrate diverse data sets, often in text due to the legacy nature of that information and return “Context” as a result. • Connections and relationships, cause and effect correlation needs to be materialized and persisted permanently. • All information must be indexed, searchable and shareable. • The solution must be agile, easily expandable and adaptable to changing business conditions • The solution needs to be a combination of text-based NLP, ElasticSearch and Graphs. • Information must be easy to visualize and leverage in your processes and workflows
- 30. Money Transferring Purchases Bank Services Neo4j powers 360°view and update of information in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification SETS Context for Traversals Relational database ElasticSearch & Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data Data-set used to explore new insights and develop new algorithms as graph expands Neo4j In Action
- 31. 31 Graph Boosted ArtificialIntelligence Knowledge Graphs Provide Rich Context for AI AI Visibility Human-Friendly Graph Visualization Graph Enhanced AI Models Faster, More Accurate Development Graph Execution of AI Operationalize Real-Time OLAP and Monitoring Graph Analytics Enrich AI Inputs with Graph Algorithms Graph System of Record Maintain a Source of Connected AI Truth
- 32. Case Studies Neo4j CaseStudies
- 33. Background • Brazil's largestbank, #38 on Forbes G2000 • $61B annual sales 95K employees • Most valuable brand in Brazil • 28.9M credit card & 25.6M debit card accounts • High integrity, customer-centric values Business Problem • Data silos made assessing credit worthiness hard • High sensitivity to fraud activity • 73% of all transactions over internet and mobile • Needed real-time detection for 2,000 analysts • Scale to trillions of relationships Solution and Benefits • Credit monitoring and fraud detection application • 4.2M nodes & 4B relationships for 100 analysts • Grow to 93T relationships for 2000 analysts by 2021 • Real time visibility into money flow across multiple customers Itau Unibanco FINANCIAL SERVICES Fraud Detection / Credit Monitoring33 CE Customer since 2016 Q1EE Customer since Q2 2017
- 34. Background • Large globalbank • Deploying Reference Data to users and systems • 12 data domains, 18 datasets, 400+ integrations • Complex data management infrastructure Business Problem • Master data silos were inflexible and hard to consume • Needed simplification to reduce redundancy • Reduce risk when data is in consumers’ hands • Dramatically improve efficiency Solution and Benefits • Data distribution flows improved dramatically • Knowledge Base improves consumer access • Ad-hoc analytics improved • Governance, lineage and trust improved • Better service level from IT to data consumers UBS FINANCIAL SERVICES Master Data Management / Knowledge Graph34 CE Customer since 2016 Q1EE Customer since 2015
- 35. Background • SF-based C2Crental platform • Dataportal democratizes data access for growing number of employees while improving discoverability and trust • Data strewn everywhere—in silos, in segmented departments, nothing was universally accessible Business Problem • Data-driven culture hampered by variety and dependability of data, tribal knowledge and word-of-mouth distribution • Needed visibility into information usage, context, lineage and popularity across company of 3,000+ Solution and Benefits • Offers search with context & metadata, user & team-centric pages for origin & lineage • Nodes are resources: data tables, dashboards, reports, users, teams, business outcomes, etc. • Relationships reflect consumption, production, association, etc. • Neo4j, Elasticsearch, Python Airbnb Dataportal TRAVEL TECHNOLOGY Knowledge Graph, Metadata Management35 CE users since 2017
- 36. Background • 5 yearlong drug discovery research • Parse & Navigate over 25 Million scientific papers • Sourced from National Library of Research and tagging of “Medical Subject Headers” (MeSH tags) Business Problem • Seeking to automate phenotype, compound and protein cell behavior research by using previously documented research more effectively • Text mining for research elements like DNA strings, proteins, RNA, chemicals and diseases Solution and Benefits • Found ways to identify compound interaction behavior from millions of research documents • Relations between biological entities can be identified and validated by biologic experts • Still very challenging to keep up-to-date, add genomics data, and find a breakthrough Novartis PHARMACEUTICAL RESEARCH Content Management / Biomedical Research36 CE Customer since 2016 Q1CE Customer since 2012
- 37. Background • How Neo4jis used in investigations • Non-technical reporters manually gather data • “Low-tech” data curation • Journalists want to model data as a story, not as data Business Problem • Identify repeated business relationships among individuals and their holdings and accounts • Scan documents and identify possible entities, then create relationships between people and documents. • Names and alias variances Solution and Benefits • Uses Neo4j in “story discovery” phase • Uncovers shortest paths for leads for reporters • Many investigations underway now Columbia University EDUCATION Investigative Journalism / Fraud Detection37 CE Customer since 2016 Q1EE Customer since 2015 Q4
- 38. Background • Large NordicTelecom Provider • 1M Broadband routers deployed in Sweden • Half of subscribership are over 55yrs old • Each household connects 10 devices • Goal to improve customer experience Business Problem • Broadband router enhancement to improve customer experience • Context-based in home services • How to build smart home platform that allows vendors to build new “home-centric” apps Solution and Benefits • New Features deployed to 1M homes • API-based platform for easy apps that: • Automatically assemble Spotify playlists based on who is in the house • Notify parents when children get home • Build smart shopping lists TELIA ZONE TELECOMMUNICATIONS Smart Home / Internet of Things38 EE Customer since 2016 Q4
- 39. Business Problem • Needednew asset management backbone to handle scheduling, ads, sales and pushing linear streams to satellites • Novell LDAP content hierarchy not flexible enough to store graph-based business content Solution and Benefits • Neo4j selected for performance and domain fit • Flexible, native storage of content hierarchy • Graph includes metadata used by all systems: TV series-->Episodes-->Blocks with Tags--> Linked Content, tagged with legal rights, actors, dubbing et al Background • Nashville-based developer of lifestyle- oriented content for TV, digital, mobile and publishing • Web properties generate tens of millions of unique visitors per month Scripps Networks MEDIA AND ENTERTAINMENT Knowledge Graph / Asset Management39
- 40. Business Problem • Neededto reimagine existing system to beat competition and provide 360-degree view of customers • Channel complexity necessitated move to graph database • Needed an enterprise-ready solution Solution and Benefits • Leapfrogged competition and increased digital business by 23% • Handles new data from mobile, social networks, experience and governance sources • After launch of new Neo4j MDM, Pitney Bowes stock declared a Buy Background • Connecticut-based leader in digital marketing communications • Helps clients provide omni-channel experience with in-context information Pitney Bowes MARKETING COMMUNICATIONS Master Data Management40
- 41. Background • Large PublicUniversity – “U-Dub” • IT staff for 80K+ students and employees • Transforming IT systems from mainframe to cloud • Providing IT & data warehousing services to 3 campuses, 6 hospitals, and 6,300 EDW users Business Problem • Old Sharepoint metadata was too complicated for users, not flexible and not transparent • $1B project to migrate HR system from mainframe to Workday needed to be smooth • Future projects needed repeatable predictability • Needed new glossary, impact analysis, analytics Solution and Benefits • Consulted with NDU peers, built simple model • Built Visualizer with Elasticsearch, Neo4j & D3.js • Improved predictability, lineage, and impact understanding for over 6,300 users University of Washington EDUCATION & RESEARCH Metadata Management, IT & Network Operations41 CE Customer since 2016 Q1
- 42. Background • World's largesthospitality / hotel company • 7th largest web site on internet • 1.5 M hotel rooms offered online by 2018 • Revenue Management System that allows property managers to update their pricing rates Business Problem • Provide the right room & price at the right time • Old rate program was inflexible and bogged down as they increased the pricing options per property per day • Lay the path to be an innovator in the future Solution and Benefits • 2016-era rate program embeds Neo4j as "cache" • Created a graph per hotel for 4500 properties in 3 clusters • 1000% increase in volume over 4 years • 50% decrease in infrastructure costs • "Use Neo4j Support!" MARRIOTT TRAVEL & HOSPITALITY SERVICES Pricing Recommendations Engine42 EE Customer since 2014 Q2
- 43. Case Studies forKnowledge Graphs and Recommendation Engines eBay ShopBot
- 44. Background • Personal shoppingassistant • Converses with buyer via text, picture and voice to provide real-time recommendations • Combines AI and natural language understanding (NLU) in Neo4j Knowledge Graph • First of many apps in eBay's AI Platform Business Problem • Improve personal context in online shopping • Transform buyer-provided context into ideal purchase recommendations over social platforms • "Feels like talking to a friend" Solution and Benefits • 3 developers, 8M nodes, 20M relationships • Needed high-performance traversals to respond to live customer requests • Easy to train new algorithms and grow model • Generating revenue since launch eBay ShopBot ONLINE RETAIL Knowledge Graph powers Real-Time Recommendations44 EE Customer since 2016 Q3
- 45. Case Study: KnowledgeGraphs at eBay
- 46. Case Study: KnowledgeGraphs at eBay
- 47. Case Study: KnowledgeGraphs at eBay
- 48. Case Study: KnowledgeGraphs at eBay
- 49. Bags Case Study: KnowledgeGraphs at eBay
- 50. Men’s Backpack Handbag Case Study:Knowledge Graphs at eBay
- 51. https://shopbot.ebay.com/ Try it outat: Case Study: Knowledge Graphs at eBay
- 52. Case Studies forKnowledge Graphs and Recommendation Engines eBay ShopBot