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Adoption of a Graph Database in the Insurance Sector - Jan-Frederik Wilhelm & Dr. Andreas Wandelt @ GraphConnect London 2013


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Jan-Frederik Wilhelm and Dr. Andreas Wandelt’s current project aims to replace an existing sales-portal of a german insurance company by reimplementing existing functionality in a maintainable fashion using current technology, with a core theme of the project trying to save, display and search data which is structured according to the VAA data model of the insurance industry. This talk is about their experience regarding the transition of a legacy model into a graph model using the latest version of Neo4J and some of its new features.

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Adoption of a Graph Database in the Insurance Sector - Jan-Frederik Wilhelm & Dr. Andreas Wandelt @ GraphConnect London 2013

  1. 1. Adoption of a GraphDatabase in the Insurance Sector Jan-Frederik Wilhelm / Andreas Wandelt
  2. 2. Intelligent Solution Services AG  ~ 50 employees  Base of operations: Marzling (near Munich)  Project teams: at HQ or onsite at customer location  Latest success: inSign (innovation award) Dr. Andreas Wandelt Jan-Frederik Wilhelm Head of Department Technical Consulting Software Architect
  3. 3. Project „Bay4all“ What is this project dealing with? – Critical sales system of an (bavarian) insurance company – 10 years old – Weak spots:  Poor Performance  High maintenance cost  Redevelopment of system/architecture
  4. 4. Redesign architecture Main tasks: – Renewal of data layer which is used for replication – Usage of former customer project: regarded to be nearly ready
  5. 5. 10 months later … – Remainings ??  no valid evaluation possible – Quality of data ?  may be poorer than expected  Extensive Review needed
  6. 6. Role of Neo4j Neo4j – first experiences: – – – – Whiteboard-friendly Flexible Easy „first use“ Graphical representation in Web-interface
  7. 7. Proof of Concept Two candidates: – Neo4j – ObjectDB Several obstacles for Neo4j: – Existing IBM DB2 landscape – Conservative insurance industry vs. Cutting edge-technology – Unknown world of NoSQL-databases – Startup
  10. 10. meta data extracted data ID TASKID OBJECTDESCID USERNAME CHANGED VALIDFROM VALIDTO GENDATE ROOTID ROOTCLASSDESCID PARENTID PARENTCLASSDESCID CHANGESTATE BOSID BOSOBJECTID BOSHISTORY REPLICATED BBVPLANER_PARTNERID DATA PARTNERNR NAME VORNAME GESCHLECHT FAMILIENSTAND GEBURTSDATUM STAATSANGEHOERIGKEIT BERUF BERUFSSTATUS SCHEMALESS 3611 139982 GUC0000BBV PKI5965 27.04.2010 09:48 23.04.2010 00:00 <null> 23.04.2010 3611 T000000DLV <null> <null> 0 AFIS.BP 102558199 <null> 27.04.2010 09:48 <null> <?xml version="1.0" ...?><value-list><value id="Name"><![CDATA[Wilhelm]]></value>...</value-list> 102558199 WILHELM JAN-FREDERIK 2 2 20.03.1958 <null> 921299 70
  11. 11. Data-Model  Used throughout the system (replication, persistence, UI-code, …)  Separate data-model for faster selections (by nightly replication)  Authorization extraction into tables – used by joins (by nightly replication)
  12. 12. First Approach  Identified culprit: inadequate data model  Revival of abandoned project – Relational approach  Failure – Mapping at replication time
  13. 13. Basic Idea  Don‘t map (too) early  Don‘t lose content  Don‘t lose info about structure  Simplify the model where possible  Create a copy of the host-data?  Use the PM-data and map later
  14. 14. Basic Idea – Graph Database?  Four levels of abstraction in model – One graph per abstraction layer – Connections between the layers! (IS_A) – Using levels like classes in the OO-mindset  Authorization data – In same database – Linked to domain nodes  Reuse of existing replication mechanisms
  15. 15. Initial Data Import  Extensive updates once a year  Fast replication needed  BatchInserter   Spring Data Neo4J not usable
  16. 16. Introduction of Schema  Labels in Neo4J 2.0  Classification of nodes  Simplification of cypher queries  Automatic indexing of label-properties
  17. 17. Domain Mapping  No 1:1 mapping of domain-model to database-model   Spring Data not usable  Blueprints Frames unknown  not used  Custom mapping mechanism developed – Annotations – Generics – Java Reflection API
  18. 18. Indexing P M P M messages in PM-datamodel transformation messages in graph-form basic mapping summary (i.e. key properties of a contract) property collections TransactionEventHandler label indexing single properties Lucene Name node-Adresse Auer 81517 Zöller 58999 Vertragsnummer node-Adresse 0000001 23114 9990000 44539 Name Vorname m/w node-Adresse m Auer Karl 81517 Zöller Eva w read/write data, navigate graph nodes, relations, properties Data Store Neo4J exact search, combined search, (phonetic search) keys, node-adresses indexes 58999
  19. 19. Search  Queries use – Authorization data – Labels – Indexes – structural information  Performance-Requirements fullfilled  All requests manageable/solvable so far
  20. 20. What‘s next?  Fast summary of an agent‘s portfolio Wishlist für Neo4J  More properties in schema  Validation of PMmessages  Analysis of graph, schema and indexes  Analysis of the whole portfolio  (even) more tutorials (Future) Benefits for project  Reduction of work in Host-System  Further commitment to new Web-UI
  21. 21. Overall results  Fast system  The performance for search requests is better than demanded  Solution is accepted by the customer – project is saved  First productive usage of Neo4j within an insurance company  Usage of technology which really fits the domain-/technical problem  Discussions in an „interdisciplinarily“ team  Very good support by consulting team of Neo Technology
  22. 22. Thank you for attending…
  23. 23. What‘s useful in Neo4J 2.0 (for us)  Labels  Try with ressources (AutoClosable Transactions)  New Cypher Syntax  New Web-UI  Not sure if 2.0: ServerBuilder, LifeCycleStuff like TransactionEventHandler, Unmanaged Extensions, …
  24. 24. Surrounding technologies  Apache Camel for replication  Apache Jackrabbit for storing file-data  Jersey for REST – Unmanaged extension – Client