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(1707 - 1783)
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![MATCH
(customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category
{name:'Baby'}),
(customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category
{name:'Beer'})
WHERE
NOT
(customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category
{name:'Console'})
RETURN
customer](https://image.slidesharecdn.com/graphyourbusiness-jimwebber-141117084607-conversion-gate01/85/Graph-Your-Business-GraphDay-JimWebber-30-320.jpg)
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Graph Your Business - GraphDay JimWebber
This document discusses using graph databases to represent business data and provides examples of how different industries are using graph databases. It introduces the labeled property graph data model and provides examples of graph queries. It also lists some common uses of graph databases and resources for learning more about graph databases and Neo4j.
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- 2. roadmap • wherewe are (mostly) • imprisoned data • a little history • labeled property graph model • connected data • using a whiteboard http://flickr.com/photos/23465812@N00/7898643042 by davecito, CC BY
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- 8. “There is asignificant downside - the whole approach works really well when data access is aligned with the aggregates, but what if you want to look at the data in a different way? Order entry naturally stores orders as aggregates, but analyzing product sales cuts across the aggregate structure. The advantage of not using an aggregate structure in the database is that it allows you to slice and dice your data different ways for different audiences. This is why aggregate-oriented stores talk so much about map-reduce.” – Martin Fowler, http://martinfowler.com/bliki/AggregateOrientedDatabase.html
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- 20. % of PeopleWho Hate Michael Jackson by Date 100 75 50 25 0 Not a Graph June 25, 2009 June 26,2009
- 21. Can’t my relationaldatabase can already do this? No, actually.
- 22. 143 143 964 326 725 725 964 981 326 $100 725 $632 981 $212 143 Alice 964 Bob Customers Customer_Accounts Accounts
- 23. name: Alice bal:$100 bal: $632 owns bal: $212 owns Nodes owns Customer Account Account Account 964 Bob Customer owns Relationships
- 24. Labeled Property GraphModel • Nodes with optional labels and optional properties • Named, directed Relationships with optional properties (Relationships have exactly one start and one end node, which may be the same node)
- 30. MATCH (customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category {name:'Baby'}), (customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category {name:'Beer'}) WHERE NOT (customer)-‐[:BOUGHT]-‐>()-‐[:IN*]-‐>(:Category {name:'Console'}) RETURN customer
- 31. A Demo? UsingNeo4j 2.1.5 for Retail Analytics in Minutes
- 32. Who’s Graphing theirbusiness?
- 37. Aviation Software FinancialTelecoms Health Web / Social Media Education, Finance Logistics, Government Accenture Energy Aerospace Network & Data Center MDM / System Of Record Social Geo Recommendations Identity & Access Control Content Management Business Intel., Audit, CRM
- 38. What’s Neo4j goodfor? • Recommendations • Meta-index/360 degree view • Social networks • Content Management • MDM / System-of-Record • Financial Audit / Modeling / Fraud • Business intelligence • Data centre management • Supply chain/provenance • Product Catalogue • Web of things • Time series/event data • Web analytics, user journeys • Scientific computing • Spatial • Geo/Seismic/Meteorological • Bio/Pharma • And much, much more…
- 39. There’s a graphfor your business whatever your business
- 40. Resources • GraphDatabases Book (O’Reilly) • Full, free eBook available: http://graphdatabases.com • Learning Neo4j Book (Packt) • Full, free eBook available: http://neo4j.com/book-learning-neo4j/ • Free online training: • http://www.neo4j.org/learn/online_course
- 41.