Intro to Neo4j or why insurances should love graphs

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This talk covers a basic intro of graphs, NOSQL and graph databases, followed b a number of domain examples and case studies, and a section on how graph databases can be interesting in the domain of …

This talk covers a basic intro of graphs, NOSQL and graph databases, followed b a number of domain examples and case studies, and a section on how graph databases can be interesting in the domain of insurance companies.

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  • 1. Introduction to Graph Databases @peterneubauer #neo4j 1Thursday, April 19, 12
  • 2. What’s the plan? 2Thursday, April 19, 12
  • 3. What’s the plan? ๏Why a graph? 2Thursday, April 19, 12
  • 4. What’s the plan? ๏Why a graph? ๏Graph Database 101 2Thursday, April 19, 12
  • 5. What’s the plan? ๏Why a graph? ๏Graph Database 101 ๏a look at Neo4j 2Thursday, April 19, 12
  • 6. What’s the plan? ๏Why a graph? ๏Graph Database 101 ๏a look at Neo4j ๏the Real World 2Thursday, April 19, 12
  • 7. Why a graph? 3Thursday, April 19, 12
  • 8. Q: What are graphs good for? 4Thursday, April 19, 12
  • 9. Q: What are graphs good for? A: highly connected data ๏ Recommendations ๏ Business intelligence ๏ Social computing ๏ Geospatial ๏ MDM ๏ Systems management ๏ Genealogy 4Thursday, April 19, 12
  • 10. Q: What are graphs good for? A: highly connected data ๏ Recommendations • Real Use Cases: ๏ Business intelligence • [A] ACL from Hell ๏ Social computing • [B] Timely recommendations • [C] Global collaboration ๏ Geospatial ๏ MDM ๏ Systems management ๏ Genealogy 4Thursday, April 19, 12
  • 11. Trends in BigData & NOSQL 5Thursday, April 19, 12
  • 12. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) 5Thursday, April 19, 12
  • 13. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt 5Thursday, April 19, 12
  • 14. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) 5Thursday, April 19, 12
  • 15. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) • for example, text documents to html 5Thursday, April 19, 12
  • 16. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) • for example, text documents to html ๏ 3. semi-structured data 5Thursday, April 19, 12
  • 17. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) • for example, text documents to html ๏ 3. semi-structured data • individualization of data, with common sub-set 5Thursday, April 19, 12
  • 18. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) • for example, text documents to html ๏ 3. semi-structured data • individualization of data, with common sub-set ๏ 4. architecture - a facade over multiple services 5Thursday, April 19, 12
  • 19. Trends in BigData & NOSQL ๏ 1. increasing data size (big data) • “Every 2 -days we create as much information as we did up to 2003” Eric Schmidt ๏ 2. increasingly connected data (graph data) • for example, text documents to html ๏ 3. semi-structured data • individualization of data, with common sub-set ๏ 4. architecture - a facade over multiple services • from monolithic to modular, distributed applications 5Thursday, April 19, 12
  • 20. 4 Categories of NOSQL 6Thursday, April 19, 12
  • 21. Key-Value Category ๏ “Dynamo: Amazon’s Highly Available Key-Value Store” (2007) ๏ Data model: • Global key-value mapping • Big scalable HashMap • Highly fault tolerant (typically) ๏ Examples: • Riak, Redis,Voldemort 7Thursday, April 19, 12
  • 22. Key-Value: Pros & Cons ๏ Strengths • Simple data model • Great at scaling out horizontally • Scalable • Available ๏ Weaknesses: • Simplistic data model • Poor for complex data 8Thursday, April 19, 12
  • 23. Column-Family Category ๏ Google’s “Bigtable: A Distributed Storage System for Structured Data” (2006) • Column-Family are essentially Big Table clones ๏ Data model: • A big table, with column families • Map-reduce for querying/processing ๏ Examples: • HBase, HyperTable, Cassandra 9Thursday, April 19, 12
  • 24. Column-Family: Pros & Cons ๏ Strengths • Data model supports semi-structured data • Naturally indexed (columns) • Good at scaling out horizontally ๏ Weaknesses: • Unsuited for interconnected data 10Thursday, April 19, 12
  • 25. Document Database Category ๏ Data model • Collections of documents • A document is a key-value collection • Index-centric, lots of map-reduce ๏ Examples • CouchDB, MongoDB 11Thursday, April 19, 12
  • 26. Document Database: Pros & Cons ๏ Strengths • Simple, powerful data model (just like SVN!) • Good scaling (especially if sharding supported) ๏ Weaknesses: • Unsuited for interconnected data • Query model limited to keys (and indexes) • Map reduce for larger queries 12Thursday, April 19, 12
  • 27. Graph Database Category ๏ Data model: • Nodes & Relationships • Hypergraph, sometimes (edges with multiple endpoints) ๏ Examples: • Neo4j (of course), OrientDB, InfiniteGraph, AllegroGraph 13Thursday, April 19, 12
  • 28. Living in a NOSQL World Complexity Size 14Thursday, April 19, 12
  • 29. Living in a NOSQL World Complexity RDBMS Size 14Thursday, April 19, 12
  • 30. Living in a NOSQL World Complexity RDBMS Key-Value Store Size 14Thursday, April 19, 12
  • 31. Living in a NOSQL World Complexity Column Family RDBMS Key-Value Store Size 14Thursday, April 19, 12
  • 32. Living in a NOSQL World Complexity Document Databases Column Family RDBMS Key-Value Store Size 14Thursday, April 19, 12
  • 33. Living in a NOSQL World Complexity Graph Databases Document Databases Column Family RDBMS Key-Value Store Size 14Thursday, April 19, 12
  • 34. Living in a NOSQL World Complexity Graph Databases Document Databases Column Family RDBMS Key-Value Store 90% Size of use cases 14Thursday, April 19, 12
  • 35. Graph Database: Pros & Cons 15Thursday, April 19, 12
  • 36. Graph Database: Pros & Cons ๏ Strengths 15Thursday, April 19, 12
  • 37. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS 15Thursday, April 19, 12
  • 38. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data 15Thursday, April 19, 12
  • 39. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query 15Thursday, April 19, 12
  • 40. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query ๏ Weaknesses: 15Thursday, April 19, 12
  • 41. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query ๏ Weaknesses: • Sharding (though they can scale reasonably well) 15Thursday, April 19, 12
  • 42. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query ๏ Weaknesses: • Sharding (though they can scale reasonably well) ‣also, stay tuned for developments here 15Thursday, April 19, 12
  • 43. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query ๏ Weaknesses: • Sharding (though they can scale reasonably well) ‣also, stay tuned for developments here • Requires conceptual shift 15Thursday, April 19, 12
  • 44. Graph Database: Pros & Cons ๏ Strengths • Powerful data model, as general as RDBMS • Fast, for connected data • Easy to query ๏ Weaknesses: • Sharding (though they can scale reasonably well) ‣also, stay tuned for developments here • Requires conceptual shift ‣though graph-like thinking becomes addictive 15Thursday, April 19, 12
  • 45. Graph DB 101 16Thursday, April 19, 12
  • 46. Some well-known named graphs 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 47. Some well-known named graphs diamond 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 48. Some well-known named graphs diamond butterfly 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 49. Some well-known named graphs diamond butterfly star 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 50. Some well-known named graphs diamond butterfly star bull 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 51. Some well-known named graphs diamond butterfly star bull franklin 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 52. Some well-known named graphs diamond butterfly star bull franklin robertson 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 53. Some well-known named graphs diamond butterfly star bull franklin robertson horton 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 54. Some well-known named graphs diamond butterfly star bull franklin robertson horton hall-janko 17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
  • 55. We’re talking about a Property Graph 18Thursday, April 19, 12
  • 56. We’re talking about a Property Graph ๏ Nodes 18Thursday, April 19, 12
  • 57. We’re talking about a Property Graph ๏ Nodes 18Thursday, April 19, 12
  • 58. We’re talking about a Property Graph ๏ Nodes ๏ Relationships 18Thursday, April 19, 12
  • 59. We’re talking about a Property Graph ๏ Nodes ๏ Relationships 18Thursday, April 19, 12
  • 60. We’re talking about a Property Graph ๏ Nodes ๏ Relationships 18Thursday, April 19, 12
  • 61. We’re talking about a Property Graph ๏ Nodes ๏ Relationships ๏ Properties 18Thursday, April 19, 12
  • 62. We’re talking about a Property Graph name:Andreas ๏ Nodes job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding ๏ Relationships name: Stephen knows job: DJ knows since: 2006 since: 2002 knows ๏ Properties since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 18Thursday, April 19, 12
  • 63. We’re talking about a Property Graph name:Andreas ๏ Nodes job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding ๏ Relationships name: Stephen knows job: DJ knows since: 2006 since: 2002 knows ๏ Properties since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 18Thursday, April 19, 12
  • 64. We’re talking about a Property Graph name:Andreas ๏ Nodes job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding ๏ Relationships name: Stephen knows job: DJ knows since: 2006 since: 2002 knows ๏ Properties since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 + Indexes knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 18Thursday, April 19, 12
  • 65. Compared to RDBMS becomes 19Thursday, April 19, 12
  • 66. A look at Graph Queries 20Thursday, April 19, 12
  • 67. Query a graph with a traversal 21Thursday, April 19, 12
  • 68. Query a graph with a traversal name:Andreas job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding name: Stephen knows job: DJ knows since: 2006 since: 2002 knows since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 21Thursday, April 19, 12
  • 69. Query a graph with a traversal // lookup starting point in an index start n=node:node_auto_index(name = ‘Andreas’) n name:Andreas job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding name: Stephen knows job: DJ knows since: 2006 since: 2002 knows since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 21Thursday, April 19, 12
  • 70. Query a graph with a traversal // lookup starting point in an index then traverse to find results start n=node:node_auto_index(name = ‘Andreas’) n=node:People(name = ‘Andreas’) match (n)--()--(foaf) return foaf n name:Andreas job: talking knows since: 2002 knows name: Tobias since: 2008 job: coding name: Stephen knows job: DJ knows since: 2006 since: 2002 knows since: 2006 knows name: Peter since: 2000 job: building name: Delia name: Tiberius job: barking knows job: dancer since: 1992 knows knows since: 1998 since: 1998 name: Emil job: plumber name: Allison knows job: plumber since: 2002 knows since: 1996 21Thursday, April 19, 12
  • 71. Cypher 22Thursday, April 19, 12
  • 72. Cypher ๏ a pattern-matching query language ๏ declarative grammar with clauses (like SQL) ๏ aggregation, ordering, limits ๏ tabular results 22Thursday, April 19, 12
  • 73. Cypher ๏ a pattern-matching query language ๏ declarative grammar with clauses (like SQL) ๏ aggregation, ordering, limits ๏ tabular results // get node with id 0 start a=node(0) return a // traverse from node 1 start a=node(1) match (a)-->(b) return b // return friends of friends start a=node(1) match (a)--()--(c) return c 22Thursday, April 19, 12
  • 74. Neo4j - the Graph Database 23Thursday, April 19, 12
  • 75. Background of Neo4j ๏ 2001 - Windh Technologies, a media asset management company • CTO Peter with Emil, Johan prototyped a proper graph interface • first SQL-backed, then revised as a full-stack implementation • (just like Amazon-Dynamo, Facebook-Cassandra) ๏ 2003 Neo4j went into 24/7 production ๏ 2006-2007 - Neo4j was spun off as an open source project ๏ 2009 seed funding for the company ๏ 2010 Neo4j Server was created (previously only an embedded DB) ๏ 2011 Fully funded silicon valley start-up - Neo Technology 24Thursday, April 19, 12
  • 76. Neo4j is a Graph Database 25Thursday, April 19, 12
  • 77. Neo4j is a Graph Database ๏ A Graph Database: 25Thursday, April 19, 12
  • 78. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both 25Thursday, April 19, 12
  • 79. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data 25Thursday, April 19, 12
  • 80. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data ๏ A Graph Database: 25Thursday, April 19, 12
  • 81. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data ๏ A Graph Database: • reliable with real ACID Transactions 25Thursday, April 19, 12
  • 82. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data ๏ A Graph Database: • reliable with real ACID Transactions • scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties 25Thursday, April 19, 12
  • 83. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data ๏ A Graph Database: • reliable with real ACID Transactions • scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Server with REST API, or Embeddable on the JVM 25Thursday, April 19, 12
  • 84. Neo4j is a Graph Database ๏ A Graph Database: • a Property Graph with Nodes, Relationships and Properties on both • perfect for complex, highly connected data ๏ A Graph Database: • reliable with real ACID Transactions • scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Server with REST API, or Embeddable on the JVM • high-performance with High-Availability (read scaling) 25Thursday, April 19, 12
  • 85. the Real World 26Thursday, April 19, 12
  • 86. Q: What are graphs good for? 27Thursday, April 19, 12
  • 87. Q: What are graphs good for? A: highly connected data ๏ Recommendations ๏ Business intelligence ๏ Social computing ๏ Geospatial ๏ MDM ๏ Systems management ๏ Genealogy 27Thursday, April 19, 12
  • 88. Q: What are graphs good for? A: highly connected data ๏ Recommendations • Real Use Cases: ๏ Business intelligence • [A] ACL from Hell ๏ Social computing • [B] Timely recommendations • [C] Global collaboration ๏ Geospatial ๏ MDM ๏ Systems management ๏ Genealogy 27Thursday, April 19, 12
  • 89. [A] ACL from Hell 28Thursday, April 19, 12
  • 90. [A] ACL from Hell ๏ Customer: leading consumer utility company with tons and tons of users ๏ Goal: comprehensive access control administration for customers ๏ Benefits: • Flexible and dynamic architecture • Exceptional performance • Extensible data model supports new applications and features • Low cost 28Thursday, April 19, 12
  • 91. [A] ACL from Hell ๏ Customer: leading consumer utility company with tons and tons of users • A Reliable access control administration system for 5 million customers, subscriptions and agreements ๏ Goal: comprehensive access control administration for customers • Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and ๏ Benefits: agreements • Flexible and dynamic architecture • Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements) • Exceptional performance • Extensible data model supports new applications and features • Low cost 28Thursday, April 19, 12
  • 92. [A] ACL from Hell ๏ Customer: leading consumer utility company with tons and tons of users • A Reliable access control administration system for 5 million customers, subscriptions and agreements ๏ Goal: comprehensive access control administration for customers • Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and ๏ Benefits: agreements • Flexible and dynamic architecture • Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements) • Exceptional performance • Extensible data model supports new applications name: Andreas works with company: Neo Technology and features owns member of gets discount on • Low cost account: 9758352794 subscribes to has plan agreement: ultimate includes subscription: sports provides group: graphistas discounts service: NFL promotion: fall includes offered subscribes to provides service: Ravens subscription: local 28Thursday, April 19, 12
  • 93. [A] ACL from Hell 29Thursday, April 19, 12
  • 94. [B] Timely Recommendations 30Thursday, April 19, 12
  • 95. [B] Timely Recommendations ๏ Customer: a professional social network • 35 millions users, adding 30,000+ each day ๏ Goal: up-to-date recommendations • Scalable solution with real-time end-user experience • Low maintenance and reliable architecture • 8-week implementation 30Thursday, April 19, 12
  • 96. [B] Timely Recommendations ๏ Problem: ๏ Customer: a professional social network • 35 millions users, adding 30,000+ each day • Real-time recommendation imperative to attract new users and maintain positive user retention ๏ Goal: up-to-date recommendations • Clustered MySQL solution not scalable or fast enough • Scalable solution with real-time end-user to support real-time requirements experience ๏ Upgrade from running a batch job • Low maintenance and reliable architecture • initial hour-long batch job • 8-week implementation • but then success happened, and it became a day • then two days ๏ With Neo4j, real time recommendations 30Thursday, April 19, 12
  • 97. [B] Timely Recommendations ๏ Problem: ๏ Customer: a professional social network • 35 millions users, adding 30,000+ each day • Real-time recommendation imperative to attract new users and maintain positive user retention ๏ Goal: up-to-date recommendations • Clustered MySQL solution not scalable or fast enough • Scalable solution with real-time end-user to support real-time requirements experience ๏ Upgrade from running a batch job • Low maintenance and reliable architecture • initial hour-long batch job • 8-week implementation • but then success happened, and it became a day • then two days name:Andreas job: talking ๏ With Neo4j, real time recommendations knows knows name: Tobias job: coding name: Stephen knows job: DJ knows knows knows name: Peter job: building name: Delia name: Tiberius job: barking knows job: dancer knows name: Emil knows job: plumber name: Allison job: plumber knows knows 30Thursday, April 19, 12
  • 98. [C] Collaboration on Global Scale 31Thursday, April 19, 12
  • 99. [C] Collaboration on Global Scale ๏ Customer: a worldwide software leader • Highly flexible data analysis • highly collaborative end-users • Sub-second results for large, densely-connected data ๏ Goal: offer an online platform for global collaboration • User experience - competitive advantage 31Thursday, April 19, 12
  • 100. [C] Collaboration on Global Scale ๏ Customer: a worldwide software leader • Highly flexible data analysis • highly collaborative end-users • Sub-second results for large, densely-connected data ๏ Goal: offer an online platform for global collaboration • User experience - competitive advantage • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability 31Thursday, April 19, 12
  • 101. [C] Collaboration on Global Scale ๏ Customer: a worldwide software leader • Highly flexible data analysis • highly collaborative end-users • Sub-second results for large, densely-connected data ๏ Goal: offer an online platform for global collaboration • User experience - competitive advantage • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability Asia North America Europe 31Thursday, April 19, 12
  • 102. [C] Collaboration on Global Scale ๏ Customer: a worldwide software leader • Highly flexible data analysis • highly collaborative end-users • Sub-second results for large, densely-connected data ๏ Goal: offer an online platform for global collaboration • User experience - competitive advantage • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability Asia North America Europe Asia North America Europe 31Thursday, April 19, 12
  • 103. Insurance <3 Graphs? 32Thursday, April 19, 12
  • 104. Q: Why should you care? 33Thursday, April 19, 12
  • 105. Q: Why should you care? A: because you have connected data. 33Thursday, April 19, 12
  • 106. Q: Why should you care? A: because you have connected data. ๏ CRM, BI, social graphs 33Thursday, April 19, 12
  • 107. Q: Why should you care? A: because you have connected data. ๏ CRM, BI, social graphs ๏ GeoSpatial analytics 33Thursday, April 19, 12
  • 108. Q: Why should you care? A: because you have connected data. ๏ CRM, BI, social graphs ๏ GeoSpatial analytics ๏ Fraud detection 33Thursday, April 19, 12
  • 109. Q: Why should you care? A: because you have connected data. ๏ CRM, BI, social graphs ๏ GeoSpatial analytics ๏ Fraud detection ๏ Network management 33Thursday, April 19, 12
  • 110. A sample insurance domain setup 34Thursday, April 19, 12
  • 111. A sample insurance domain setup Home sub_product concerns owns Building A Questionaire: fills_in Customer: C12 attribute covered_by Q1 contains_question Size: 120m2 Coverage: Super T&C: X is_offered risk_has_attr covers_risk sub_cover has Risk: Building small is_offered includes signed Coverage: Fire includes Quote: C12 based_on made Policy: C12 made User: U34 contains owns Agreement: C12 34Thursday, April 19, 12
  • 112. Recommendations, BI, Social Computing 35Thursday, April 19, 12
  • 113. Recommendations, BI, Social Computing ๏ enrich your CRM with data from Facebook, Google, Twitter etc 35Thursday, April 19, 12
  • 114. Recommendations, BI, Social Computing ๏ enrich your CRM with data from Facebook, Google, Twitter etc ๏ Recommender systems for products 35Thursday, April 19, 12
  • 115. Recommendations, BI, Social Computing ๏ enrich your CRM with data from Facebook, Google, Twitter etc ๏ Recommender systems for products ๏ Find influencers in your customer base for special treatment 35Thursday, April 19, 12
  • 116. This is what your CRM sees Customer1 Peter Neubauer 36http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
  • 117. This is what your CRM doesn’t see. 37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
  • 118. This is what your CRM doesn’t see. 37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
  • 119. Geospatial features 38Thursday, April 19, 12
  • 120. Geospatial features ๏ Dynamic layers from different sources 38Thursday, April 19, 12
  • 121. Geospatial features ๏ Dynamic layers from different sources • domainstandardflood area layer + crime index + firestation + living data -> index 38Thursday, April 19, 12
  • 122. Geospatial features ๏ Dynamic layers from different sources • domainstandardflood area layer + crime index + firestation + living data -> index ๏ routes of low insurance risks 38Thursday, April 19, 12
  • 123. Geospatial features 39Thursday, April 19, 12
  • 124. Geospatial features 40Thursday, April 19, 12
  • 125. Geospatial features 41Thursday, April 19, 12
  • 126. Configuration/Network Management 42Thursday, April 19, 12
  • 127. Configuration/Network Management ๏ Model physical and logical networks 42Thursday, April 19, 12
  • 128. Configuration/Network Management ๏ Model physical and logical networks • impact analysis 42Thursday, April 19, 12
  • 129. Configuration/Network Management ๏ Model physical and logical networks • impact analysis • configuration management 42Thursday, April 19, 12
  • 130. Configuration/Network Management ๏ Model physical and logical networks • impact analysis • configuration management • network inventory 42Thursday, April 19, 12
  • 131. Configuration/Network Management 43Thursday, April 19, 12
  • 132. Questions! 44Thursday, April 19, 12
  • 133. and, Thanks :) 45Thursday, April 19, 12