Graphs & Big Data - Philip Rathle and Andreas Kollegger @ Big Data Science Meetup, Fremont, CA

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More information on the meetup is available here: http://www.meetup.com/Big-Data-Science/events/88472522/

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Graphs & Big Data - Philip Rathle and Andreas Kollegger @ Big Data Science Meetup, Fremont, CA

  1. 1. Graphs & Big Data The Power of Graphs & TheTechnology Ecosystem Around Graphs Philip Rathle Sr. Director of Products philip@neotechnology.com @prathle Andreas Kollegger Product Experience Manager andreas@neotechnology.com @akollegger
  2. 2. V V V
  3. 3. 300 m
  4. 4. V V V
  5. 5. VVValue!
  6. 6. Early Adopters of Graph Tech
  7. 7. Evolution of Web Search Survival of the Fittest Pre-1999 WWW Indexing Discrete Data
  8. 8. Evolution of Web Search Survival of the Fittest Pre-1999 WWW Indexing Discrete Data 1999 - 2012 Google Invents PageRank Connected Data (Simple)
  9. 9. Evolution of Web Search Survival of the Fittest Pre-1999 WWW Indexing Discrete Data 1999 - 2012 Google Invents PageRank Connected Data (Simple) 2012-? Google Knowledge Graph, Facebook Graph Search Connected Data (Rich)
  10. 10. Evolution of Online Recruiting 1999 Keyword Search Discrete Data Survival of the Fittest
  11. 11. Evolution of Online Recruiting 1999 Keyword Search Discrete Data Survival of the Fittest 2011-12 Social Discovery Connected Data
  12. 12. Emergent Graph in Other Industries (Actual Neo4j Graphs)
  13. 13. Content Management & Access Control Emergent Graph in Other Industries (Actual Neo4j Graphs)
  14. 14. Content Management & Access Control Emergent Graph in Other Industries (Actual Neo4j Graphs) Insurance Risk Analysis
  15. 15. Content Management & Access Control Geo Routing (Public Transport) Emergent Graph in Other Industries (Actual Neo4j Graphs) Insurance Risk Analysis
  16. 16. Content Management & Access Control Network Cell Analysis Geo Routing (Public Transport) Emergent Graph in Other Industries (Actual Neo4j Graphs) Insurance Risk Analysis
  17. 17. Content Management & Access Control Network Asset Management Network Cell Analysis Geo Routing (Public Transport) Emergent Graph in Other Industries (Actual Neo4j Graphs) Insurance Risk Analysis
  18. 18. Content Management & Access Control Network Asset Management Network Cell Analysis Geo Routing (Public Transport) BioInformatics Emergent Graph in Other Industries (Actual Neo4j Graphs) Insurance Risk Analysis
  19. 19. Emergent Graph in Other Industries (Actual Neo4j Graphs)
  20. 20. Web Browsing Emergent Graph in Other Industries (Actual Neo4j Graphs)
  21. 21. Web Browsing Portfolio Analytics Emergent Graph in Other Industries (Actual Neo4j Graphs)
  22. 22. Web Browsing Portfolio Analytics Gene Sequencing Emergent Graph in Other Industries (Actual Neo4j Graphs)
  23. 23. Web Browsing Portfolio Analytics Mobile Social ApplicationGene Sequencing Emergent Graph in Other Industries (Actual Neo4j Graphs)
  24. 24. What’s a Graph?
  25. 25. LIVES WITH LOVES OWNS DRIVES LOVES name:“James” age: 32 twitter:“@spam” name:“Mary” age: 35 property type:“car” brand:“Volvo” model:“V70” Graph data model
  26. 26. AUTHENTICATES TRANSMITS_DATA ASSIGNED_TO CONNECTS_TO AUTHORIZES type:“Mobile” model:“iPhone 5” IMEI: 99 000107 765315 1 type:“BTS” Height_m: 9.8 Power: 400A Backup_Generator:”Y” device type:“Test” type:“Trunk” mbps_capacity:5000 type:“Central Office” CLLI Code:“PTLEORTEDS0” Graph data model
  27. 27. Member Group
  28. 28. Member Group 143 Andreas
  29. 29. Member Group 143 Andreas 326 Big Data Fremont 725 Big Data San Francisco 981 Big Data Boston
  30. 30. Member GroupMember_Group 143 Andreas 326 Big Data Fremont 725 Big Data San Francisco 981 Big Data Boston
  31. 31. Member GroupMember_Group 143 Andreas 326 Big Data Fremont 725 Big Data San Francisco 981 Big Data Boston143 981 143 725 143 326
  32. 32. Andreas Big Data Fremont Big Data San Francisco Big Data Boston 143 326 725 981 143 981 143 725 143 326
  33. 33. Andreas Big Data Fremont Big Data San Francisco Big Data Boston
  34. 34. uid: ABK name: Andreas uid: FRE where: Fremont uid: SFO where: San Francisco uid: BOS where: Boston Nodes A Property Graph
  35. 35. uid: ABK name: Andreas uid: FRE where: Fremont uid: SFO where: San Francisco uid: BOS where: Boston Nodes Relationships member member member A Property Graph
  36. 36. What CanYou Do With Graphs?
  37. 37. * Cypher query language examplehttp://maxdemarzi.com/?s=facebook
  38. 38. MATCH (me:Person)-[:IS_FRIEND_OF]->(friend), (friend)-[:LIKES]->(restaurant), (restaurant)-[:LOCATED_IN]->(city:Location), (restaurant)-[:SERVES]->(cuisine:Cuisine) WHERE me.name = 'Philip' AND city.location='New York' AND cuisine.cuisine='Sushi' RETURN restaurant.name * Cypher query language examplehttp://maxdemarzi.com/?s=facebook
  39. 39. What drugs will bind to protein X and not interact with drugY? Of course.. a graph is a graph is a graph
  40. 40. What drugs will bind to protein X and not interact with drugY? Of course.. a graph is a graph is a graph
  41. 41. Real-Time/ OLTP Offline/ Batch
  42. 42. Real-Time/ OLTP Offline/ Batch
  43. 43. Real-Time/ OLTP Offline/ Batch
  44. 44. Real-Time/ OLTP Offline/ Batch Connected Data
  45. 45. The Zone of SQL Adequacy Connectedness of Data Set Performance SQL database Requirement of application
  46. 46. The Zone of SQL Adequacy Connectedness of Data Set Performance SQL database Requirement of application
  47. 47. The Zone of SQL Adequacy Connectedness of Data Set Performance SQL database Requirement of application Salary List ERP CRM
  48. 48. The Zone of SQL Adequacy Connectedness of Data Set Performance SQL database Requirement of application Salary List ERP CRM Network / Data Center Management Social Master Data Management Geo
  49. 49. The Zone of SQL Adequacy Connectedness of Data Set Performance SQL database Requirement of application Salary List ERP CRM Network / Data Center Management Social Master Data Management Geo Graph Database Optimal Comfort Zone
  50. 50. Graph Technology Ecosystem
  51. 51. #1: Graph Local Queries e.g. Recommendations, Friend-of-Friend, Shortest Path
  52. 52. #1: Graph Local Queries e.g. Recommendations, Friend-of-Friend, Shortest Path
  53. 53. How many restaurants, on average, has each person liked? #2: Graph Global Queries
  54. 54. How many restaurants, on average, has each person liked? #2: Graph Global Queries
  55. 55. Key Graph Analytic Technologies
  56. 56. Data Storage & Processing • Graph Databases • Graph Compute Engines Key Graph Analytic Technologies
  57. 57. Data Storage & Processing • Graph Databases • Graph Compute Engines Programming: • Graph-Centric APIs & Languages • Graph Algorithms Key Graph Analytic Technologies
  58. 58. Data Storage & Processing • Graph Databases • Graph Compute Engines Programming: • Graph-Centric APIs & Languages • Graph Algorithms Tools: • Visualization Tools & Libraries • Other Key Graph Analytic Technologies
  59. 59. What is a Graph Database 1] Robinson,Webber, Eifrem. Graph Databases. O’Reilly, 2013. p. 5. ISBN-10: 1449356265
  60. 60. What is a Graph Database “A graph database... is an online database management system with CRUD methods that expose a graph data model”1 1] Robinson,Webber, Eifrem. Graph Databases. O’Reilly, 2013. p. 5. ISBN-10: 1449356265
  61. 61. What is a Graph Database “A graph database... is an online database management system with CRUD methods that expose a graph data model”1 • Two important properties: 1] Robinson,Webber, Eifrem. Graph Databases. O’Reilly, 2013. p. 5. ISBN-10: 1449356265
  62. 62. What is a Graph Database “A graph database... is an online database management system with CRUD methods that expose a graph data model”1 • Two important properties: • Native graph storage engine: written from the ground up to manage graph data 1] Robinson,Webber, Eifrem. Graph Databases. O’Reilly, 2013. p. 5. ISBN-10: 1449356265
  63. 63. What is a Graph Database “A graph database... is an online database management system with CRUD methods that expose a graph data model”1 • Two important properties: • Native graph storage engine: written from the ground up to manage graph data • Native graph processing, including index-free adjacency to facilitate traversals 1] Robinson,Webber, Eifrem. Graph Databases. O’Reilly, 2013. p. 5. ISBN-10: 1449356265
  64. 64. Neo Technology, Inc Confidential Graph Databases are Designed to: 1. Store inter-connected data 2. Make it easy to make sense of that data 3. Enable extreme-performance operations for: • Discovery of connected data patterns • Relatedness queries > depth 1 • Relatedness queries of arbitrary length 4. Make it easy to evolve the database
  65. 65. Neo Technology, Inc Confidential Top Reasons People Use Graph Databases 1. Problems with Join performance. 2. Continuously evolving data set (often involves wide and sparse tables) 3. The Shape of the Domain is naturally a graph 4. Open-ended business requirements necessitating fast, iterative development.
  66. 66. Graph Compute Engine Processing engine that enables graph global computational algorithms to be run against large data sets
  67. 67. Graph Compute Engine Processing engine that enables graph global computational algorithms to be run against large data sets Graph Mining Engine (Working Storage) In-Memory Processing System(s) of Record Graph Compute Engine Data extraction, transformation, and load
  68. 68. Graph Database Deployment Application
  69. 69. Graph Database Deployment Application Graph Database Cluster Data Storage & Business Rules Execution
  70. 70. Graph Database Deployment Application Graph Database Cluster Data Storage & Business Rules Execution Graph Visualization End User Ad-hoc visual navigation & discovery
  71. 71. Graph Database Deployment Application Graph Database Cluster Data Storage & Business Rules Execution Reporting Graph- Dashboards & Ad-hoc Analysis Graph Visualization End User Ad-hoc visual navigation & discovery
  72. 72. Graph Database Deployment Application Other Databases ETL Graph Database Cluster Data Storage & Business Rules Execution Reporting Graph- Dashboards & Ad-hoc Analysis Graph Visualization End User Ad-hoc visual navigation & discovery
  73. 73. Graph Database Deployment Application Other Databases ETL Graph Database Cluster Data Storage & Business Rules Execution Reporting Graph- Dashboards & Ad-hoc Analysis Graph Visualization End User Ad-hoc visual navigation & discovery Data Scientist Ad-Hoc Analysis
  74. 74. Graph Database Deployment Application Other Databases ETL Graph Database Cluster Data Storage & Business Rules Execution Reporting Graph- Dashboards & Ad-hoc Analysis Graph Visualization End User Ad-hoc visual navigation & discovery Bulk Analytic Infrastructure (e.g. Graph Compute Engine) ETL Graph Mining & Aggregation Data Scientist Ad-Hoc Analysis
  75. 75. Neo Technology, Inc Confidential Graph Dashboards
  76. 76. Fraud Detection & Money Laundering
  77. 77. IT Service Dependencies
  78. 78. Network Cell Analysis
  79. 79. Philip Rathle Sr. Director of Products philip@neotechnology.com @prathle Andreas Kollegger Product Experience Manager andreas@neotechnology.com @akollegger Graphs in the Real World Case Study Examples &Working with Graphs
  80. 80. Cypher
  81. 81. Cypher LOVES A B Graph PatternsASCII art
  82. 82. Cypher LOVES A B Graph PatternsASCII art MATCH (A) -[:LOVES]-> (B)
  83. 83. Cypher LOVES A B Graph PatternsASCII art MATCH (A) -[:LOVES]-> (B) WHERE A.name = "A"
  84. 84. Cypher LOVES A B Graph PatternsASCII art MATCH (A) -[:LOVES]-> (B) WHERE A.name = "A" RETURN B as lover
  85. 85. Social Example
  86. 86. Neo Technology, Inc Confidential Social Graph - Create Practical Cypher CREATE ! (joe:Person {name:"Joe"}), ! (bob:Person {name:"Bob"}), ! (sally:Person {name:"Sally"}), ! (anna:Person {name:"Anna"}), ! (jim:Person {name:"Jim"}), ! (mike:Person {name:"Mike"}), ! (billy:Person {name:"Billy"}), ! ! (joe)-[:KNOWS]->(bob), ! (joe)-[:KNOWS]->(sally), ! (bob)-[:KNOWS]->(sally), ! (sally)-[:KNOWS]->(anna), ! (anna)-[:KNOWS]->(jim), ! (anna)-[:KNOWS]->(mike), ! (jim)-[:KNOWS]->(mike), ! (jim)-[:KNOWS]->(billy)
  87. 87. Neo Technology, Inc Confidential MATCH (person)-[:KNOWS]-(friend), (friend)-[:KNOWS]-(foaf) WHERE person.name = "Joe" AND NOT(person-[:KNOWS]-foaf) RETURN foaf Social Graph - Friends of Joe's Friends Practical Cypher foaf {name:"Anna"}
  88. 88. Neo Technology, Inc Confidential MATCH (person1)-[:KNOWS]-(friend), (person2)-[:KNOWS]-(friend) WHERE person1.name = "Joe" AND person2.name = "Sally" RETURN friend Social Graph - Common Friends Practical Cypher friend {name:"Bob"}
  89. 89. Neo Technology, Inc Confidential MATCH path = shortestPath( (person1)-[:KNOWS*..6]-(person2) ) WHERE person1.name = "Joe" ! AND person2.name = "Billy" RETURN path Social Graph - Shortest Path Practical Cypher path {start:"13759", nodes:["13759","13757","13756","13755","13753"], length:4, relationships:["101407","101409","101410","101413"], end:"13753"}
  90. 90. Industry: Online Job Search Use case: Social / Recommendations • Online jobs and career community, providing anonymized inside information to job seekers Neo Technology Confidential Background Sausalito, CA
  91. 91. Industry: Online Job Search Use case: Social / Recommendations • Online jobs and career community, providing anonymized inside information to job seekers Business problem • Wanted to leverage known fact that most jobs are found through personal & professional connections • Needed to rely on an existing source of social network data. Facebook was the ideal choice. • End users needed to get instant gratification • Aiming to have the best job search service, in a very competitive market Person Company KNOW S Person Person KNOWS Company KNOWS WORKS_AT WORKS_AT Neo Technology Confidential Background Sausalito, CA
  92. 92. Industry: Online Job Search Use case: Social / Recommendations • Online jobs and career community, providing anonymized inside information to job seekers Business problem • Wanted to leverage known fact that most jobs are found through personal & professional connections • Needed to rely on an existing source of social network data. Facebook was the ideal choice. • End users needed to get instant gratification • Aiming to have the best job search service, in a very competitive market Solution & Benefits • First-to-market with a product that let users find jobs through their network of Facebook friends • Job recommendations served real-time from Neo4j • Individual Facebook graphs imported real-time into Neo4j • Glassdoor now stores > 50% of the entire Facebook social graph • Neo4j cluster has grown seamlessly, with new instances being brought online as graph size and load have increased Person Company KNOW S Person Person KNOWS Company KNOWS WORKS_AT WORKS_AT Neo Technology Confidential Background Sausalito, CA
  93. 93. Network Management Example
  94. 94. Neo Technology, Inc Confidential Industry: Communications Use case: Network Management Background • Second largest communications company in France • Part ofVivendi Group, partnering withVodafone Paris, France
  95. 95. Neo Technology, Inc Confidential Industry: Communications Use case: Network Management Background • Second largest communications company in France • Part ofVivendi Group, partnering withVodafone Business problem • Infrastructure maintenance took one full week to plan, because of the need to model network impacts • Needed rapid, automated “what if” analysis to ensure resilience during unplanned network outages • Identify weaknesses in the network to uncover the need for additional redundancy • Network information spread across > 30 systems, with daily changes to network infrastructure • Business needs sometimes changed very rapidly Router Service DEPENDS_O N Switch Switch Router Fiber Link Fiber Link Fiber Link Oceanfloor Cable DEPENDS_ON DEPENDS_ON DEPEN DS_O N DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON LINKED LINKED LIN KED DEPENDS_ON Paris, France
  96. 96. Neo Technology, Inc Confidential Industry: Communications Use case: Network Management Background • Second largest communications company in France • Part ofVivendi Group, partnering withVodafone Business problem • Infrastructure maintenance took one full week to plan, because of the need to model network impacts • Needed rapid, automated “what if” analysis to ensure resilience during unplanned network outages • Identify weaknesses in the network to uncover the need for additional redundancy • Network information spread across > 30 systems, with daily changes to network infrastructure • Business needs sometimes changed very rapidly Solution & Benefits • Flexible network inventory management system, to support modeling, aggregation & troubleshooting • Single source of truth (Neo4j) representing the entire network • Dynamic system loads data from 30+ systems, and allows new applications to access network data • Modeling efforts greatly reduced because of the near 1:1 mapping between the real world and the graph • Flexible schema highly adaptable to changing business requirements Router Service DEPENDS_O N Switch Switch Router Fiber Link Fiber Link Fiber Link Oceanfloor Cable DEPENDS_ON DEPENDS_ON DEPEN DS_O N DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON LINKED LINKED LIN KED DEPENDS_ON Paris, France
  97. 97. Background • World’s largest provider of IT infrastructure, software & services • HP’s Unified Correlation Analyzer (UCA) application is a key application inside HP’s OSS Assurance portfolio • Carrier-class resource & service management, problem determination, root cause & service impact analysis • Helps communications operators manage large, complex and fast changing networks Industry: Web/ISV, Communications Use case: Network Management Global (U.S., France)
  98. 98. Background • World’s largest provider of IT infrastructure, software & services • HP’s Unified Correlation Analyzer (UCA) application is a key application inside HP’s OSS Assurance portfolio • Carrier-class resource & service management, problem determination, root cause & service impact analysis • Helps communications operators manage large, complex and fast changing networks Business problem • Use network topology information to identify root problems causes on the network • Simplify alarm handling by human operators • Automate handling of certain types of alarms Help operators respond rapidly to network issues • Filter/group/eliminate redundant Network Management System alarms by event correlation Industry: Web/ISV, Communications Use case: Network Management Global (U.S., France)
  99. 99. Background • World’s largest provider of IT infrastructure, software & services • HP’s Unified Correlation Analyzer (UCA) application is a key application inside HP’s OSS Assurance portfolio • Carrier-class resource & service management, problem determination, root cause & service impact analysis • Helps communications operators manage large, complex and fast changing networks Business problem • Use network topology information to identify root problems causes on the network • Simplify alarm handling by human operators • Automate handling of certain types of alarms Help operators respond rapidly to network issues • Filter/group/eliminate redundant Network Management System alarms by event correlation Solution & Benefits • Accelerated product development time • Extremely fast querying of network topology • Graph representation a perfect domain fit • 24x7 carrier-grade reliability with Neo4j HA clustering • Met objective in under 6 months Industry: Web/ISV, Communications Use case: Network Management Global (U.S., France)
  100. 100. Neo Technology, Inc Confidential CREATE ! (crm {name:"CRM"}), ! (dbvm {name:"Database VM"}), ! (www {name:"Public Website"}), ! (wwwvm {name:"Webserver VM"}), ! (srv1 {name:"Server 1"}), ! (san {name:"SAN"}), ! (srv2 {name:"Server 2"}), ! (crm)-[:DEPENDS_ON]->(dbvm), ! (dbvm)-[:DEPENDS_ON]->(srv2), ! (srv2)-[:DEPENDS_ON]->(san), ! (www)-[:DEPENDS_ON]->(dbvm), ! (www)-[:DEPENDS_ON]->(wwwvm), ! (wwwvm)-[:DEPENDS_ON]->(srv1), ! (srv1)-[:DEPENDS_ON]->(san) Network Management - Create Practical Cypher
  101. 101. Neo Technology, Inc Confidential // Server 1 Outage MATCH (n)<-[:DEPENDS_ON*]-(upstream) WHERE n.name = "Server 1" RETURN upstream Network Management - Impact Analysis Practical Cypher upstream {name:"Webserver VM"} {name:"Public Website"}
  102. 102. Neo Technology, Inc Confidential // Public website dependencies MATCH (n)-[:DEPENDS_ON*]->(downstream) WHERE n.name = "Public Website" RETURN downstream Network Management - Dependency Analysis Practical Cypher downstream {name:"Database VM"} {name:"Server 2"} {name:"SAN"} {name:"Webserver VM"} {name:"Server 1"}
  103. 103. Neo Technology, Inc Confidential // Most depended on component MATCH (n)<-[:DEPENDS_ON*]-(dependent) RETURN n, count(DISTINCT dependent) AS dependents ORDER BY dependents DESC LIMIT 1 Network Management - Statistics Practical Cypher n dependents {name:"SAN"} 6
  104. 104. Logistics ~ Package Routing ~
  105. 105. Background •One of the world’s largest logistics carriers •Projected to outgrow capacity of old system •New parcel routing system •Single source of truth for entire network •B2C & B2B parcel tracking •Real-time routing: up to 5M parcels per day Industry: Logistics Use case: Parcel Routing
  106. 106. Background •One of the world’s largest logistics carriers •Projected to outgrow capacity of old system •New parcel routing system •Single source of truth for entire network •B2C & B2B parcel tracking •Real-time routing: up to 5M parcels per day Business problem •24x7 availability, year round •Peak loads of 2500+ parcels per second •Complex and diverse software stack •Need predictable performance & linear scalability •Daily changes to logistics network: route from any point, to any point Industry: Logistics Use case: Parcel Routing
  107. 107. Background •One of the world’s largest logistics carriers •Projected to outgrow capacity of old system •New parcel routing system •Single source of truth for entire network •B2C & B2B parcel tracking •Real-time routing: up to 5M parcels per day Business problem •24x7 availability, year round •Peak loads of 2500+ parcels per second •Complex and diverse software stack •Need predictable performance & linear scalability •Daily changes to logistics network: route from any point, to any point Solution & Benefits •Neo4j provides the ideal domain fit: •a logistics network is a graph •Extreme availability & performance with Neo4j clustering •Hugely simplified queries, vs. relational for complex routing •Flexible data model can reflect real-world data variance much better than relational •“Whiteboard friendly” model easy to understand Industry: Logistics Use case: Parcel Routing
  108. 108. Industry: Communications Use case: Recommendations •Cisco.com serves customer and business customers with Support Services •Needed real-time recommendations, to encourage use of online knowledge base •Cisco had been successfully using Neo4j for its internal master data management solution. •Identified a strong fit for online recommendations Neo Technology Confidential Background San Jose, CA Cisco.com
  109. 109. Industry: Communications Use case: Recommendations •Cisco.com serves customer and business customers with Support Services •Needed real-time recommendations, to encourage use of online knowledge base •Cisco had been successfully using Neo4j for its internal master data management solution. •Identified a strong fit for online recommendations Neo Technology Confidential Background Business problem •Call center volumes needed to be lowered by improving the efficacy of online self service •Leverage large amounts of knowledge stored in service cases, solutions, articles, forums, etc. •Problem resolution times, as well as support costs, needed to be lowered Support Case Support Case Knowledge Base Article Solution Knowledge Base Article Knowledge Base Article Message San Jose, CA Cisco.com
  110. 110. Industry: Communications Use case: Recommendations •Cisco.com serves customer and business customers with Support Services •Needed real-time recommendations, to encourage use of online knowledge base •Cisco had been successfully using Neo4j for its internal master data management solution. •Identified a strong fit for online recommendations Solution & Benefits •Cases, solutions, articles, etc. continuously scraped for cross-reference links, and represented in Neo4j •Real-time reading recommendations via Neo4j •Neo4j Enterprise with HA cluster •The result: customers obtain help faster, with decreased reliance on customer support Neo Technology Confidential Background Business problem •Call center volumes needed to be lowered by improving the efficacy of online self service •Leverage large amounts of knowledge stored in service cases, solutions, articles, forums, etc. •Problem resolution times, as well as support costs, needed to be lowered Support Case Support Case Knowledge Base Article Solution Knowledge Base Article Knowledge Base Article Message San Jose, CA Cisco.com
  111. 111. Consumer Web Giants Depends on Five Graphs Gartner’s “5 Graphs” Social Graph Ref: http://www.gartner.com/id=2081316 Interest Graph Payment Graph Intent Graph Mobile Graph
  112. 112. Questions ?
  113. 113. Innovate. Share. Connect. San Francisco October 3 - 4 www.graphconnect.com (graphs)-[:ARE]->(everywhere) www.neo4j.org Recommended Reading & Next Steps for Learning About Graphs... www.graphdatabases.com Get the free ebook!

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