Intro to Neo4j or why insurances should love graphs
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Transcript of Intro to Neo4j or why insurances should love graphs
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Introduction to Graph Databases
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@peterneubauer #neo4j
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What’s the plan?
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What’s the plan?
๏Why a graph?
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What’s the plan?
๏Why a graph?
๏Graph Database 101
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What’s the plan?
๏Why a graph?
๏Graph Database 101
๏a look at Neo4j
2
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What’s the plan?
๏Why a graph?
๏Graph Database 101
๏a look at Neo4j
๏the Real World
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Why a graph?
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Q: What are graphs good for?
4
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Q: What are graphs good for?
4
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
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Q: What are graphs good for?
4
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
• Real Use Cases:
• [A] ACL from Hell
• [B] Timely recommendations
• [C] Global collaboration
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Trends in BigData & NOSQL
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Trends in BigData & NOSQL
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๏1. increasing data size (big data)
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Trends in BigData & NOSQL
5
๏1. increasing data size (big data)
• “Every 2 days we create as much information as we did up to 2003” - Eric Schmidt
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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)
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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
Thursday, April 19, 12
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Trends in BigData & NOSQL
5
๏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
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4 Categories of NOSQL
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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
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Key-Value: Pros & Cons๏Strengths
• Simple data model
•Great at scaling out horizontally
• Scalable
•Available
๏Weaknesses:
• Simplistic data model
• Poor for complex data
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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
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Column-Family: Pros & Cons๏Strengths
•Data model supports semi-structured data
•Naturally indexed (columns)
•Good at scaling out horizontally
๏Weaknesses:
•Unsuited for interconnected data
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Document Database Category๏Data model
•Collections of documents
•A document is a key-value collection
• Index-centric, lots of map-reduce
๏Examples
•CouchDB, MongoDB
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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
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Graph Database Category๏Data model:
•Nodes & Relationships
•Hypergraph, sometimes (edges with multiple endpoints)
๏Examples:
•Neo4j (of course), OrientDB, InfiniteGraph, AllegroGraph
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14
Living in a NOSQL WorldCo
mpl
exity
Size
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RDBMS
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Living in a NOSQL WorldCo
mpl
exity
Size
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RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
Size
Key-ValueStore
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RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
Thursday, April 19, 12
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RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
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RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
GraphDatabases
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RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
GraphDatabases
90%of
usecases
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Graph Database: Pros & Cons
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Graph Database: Pros & Cons๏Strengths
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Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
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Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
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Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
• Easy to query
15
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Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
• Easy to query
๏Weaknesses:
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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)
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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
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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
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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
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Graph DB 101
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly star
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin robertson
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin hortonrobertson
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Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin horton hall-jankorobertson
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We’re talking about a Property Graph
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We’re talking about a Property Graph
๏Nodes
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We’re talking about a Property Graph
๏Nodes
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We’re talking about a Property Graph
๏Nodes
๏Relationships
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We’re talking about a Property Graph
๏Nodes
๏Relationships
18
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We’re talking about a Property Graph
๏Nodes
๏Relationships
18
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We’re talking about a Property Graph
๏Nodes
๏Relationships
๏Properties
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We’re talking about a Property Graph
๏Nodes
๏Relationships
๏Properties
18
name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
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We’re talking about a Property Graph
๏Nodes
๏Relationships
๏Properties
18
name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
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We’re talking about a Property Graph
๏Nodes
๏Relationships
๏Properties
18
+ Indexes
name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
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Compared to RDBMS
19
becomes
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A look at Graph Queries
20
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Query a graph with a traversal
21
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name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
Query a graph with a traversal
21
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// lookup starting point in an indexstart n=node:node_auto_index(name = ‘Andreas’)
name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
Query a graph with a traversal
21
n
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// lookup starting point in an indexstart n=node:node_auto_index(name = ‘Andreas’)// then traverse to find resultsstart n=node:People(name = ‘Andreas’)match (n)--()--(foaf) return foaf
name:Andreasjob: talking
name: Tobiasjob: coding
knowssince: 2008
knowssince: 2006
name: Peterjob: building
name: Emiljob: plumber
knowssince: 1992
name: Stephenjob: DJ
knowssince: 2002
knowssince: 2006
name: Deliajob: barking
knowssince: 2002
knowssince: 1998
name: Tiberiusjob: dancer
knowssince: 2000
name: Allisonjob: plumberknows
since: 2002
knowssince: 1998
knowssince: 1996
Query a graph with a traversal
21
n
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22
Cypher
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22
Cypher๏a pattern-matching query language
๏declarative grammar with clauses (like SQL)
๏aggregation, ordering, limits
๏tabular results
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22
Cypher๏a pattern-matching query language
๏declarative grammar with clauses (like SQL)
๏aggregation, ordering, limits
๏tabular results
// get node with id 0start a=node(0) return a// traverse from node 1start a=node(1) match (a)-->(b) return b// return friends of friendsstart a=node(1) match (a)--()--(c) return c
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Neo4j - the Graph Database
23
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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
24
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Neo4j is a Graph Database
25
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Neo4j is a Graph Database๏A Graph Database:
25
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Neo4j is a Graph Database๏A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
25
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Neo4j is a Graph Database๏A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
25
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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:
25
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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
25
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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
25
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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
25
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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)25
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the Real World
26
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Q: What are graphs good for?
27
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Q: What are graphs good for?
27
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
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Q: What are graphs good for?
27
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
• Real Use Cases:
• [A] ACL from Hell
• [B] Timely recommendations
• [C] Global collaboration
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[A] ACL from Hell
28
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[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
28
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[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
28
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and agreements
• Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements)
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[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
28
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and agreements
• Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements)
name: Andreas
subscription: sports
service: NFL
account: 9758352794
agreement: ultimate
owns
subscribes to
has plan
includes
provides group: graphistas
promotion: fall
member of
offered
discounts
company: Neo Technologyworks with
gets discount on
subscription: local
subscribes to
provides service: Ravens
includes
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[A] ACL from Hell
29
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[B] Timely Recommendations
30
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[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
30
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[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
30
๏ Problem:
• Real-time recommendation imperative to attract new users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendations
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[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
30
๏ Problem:
• Real-time recommendation imperative to attract new users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendationsname:Andreasjob: talking
name: Allisonjob: plumber
name: Tobiasjob: coding
knows
knows
name: Peterjob: building
name: Emiljob: plumber
knows
name: Stephenjob: DJ
knows
knows
name: Deliajob: barking
knows
knows
name: Tiberiusjob: dancer
knows
knows
knows
knows
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[C] Collaboration on Global Scale
31
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[C] Collaboration on Global Scale๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
31
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[C] Collaboration on Global Scale๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
31
• Massive amounts of data tied to members, user groups, member content, etc. all interconnected
• Infer collaborative relationships through user-generated content
• Worldwide Availability
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[C] Collaboration on Global Scale๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
31
• 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
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[C] Collaboration on Global Scale๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
31
• 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
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Insurance <3 Graphs?
32
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Q: Why should you care?
33
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Q: Why should you care?
33
A: because you have connected data.
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Q: Why should you care?
33
๏CRM, BI, social graphs
A: because you have connected data.
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Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
A: because you have connected data.
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Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
๏Fraud detection
A: because you have connected data.
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Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
๏Fraud detection
๏Network management
A: because you have connected data.
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A sample insurance domain setup
34
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A sample insurance domain setup
34
Home
Building A
sub_product
Coverage: Super
covered_by
Coverage: Fire
sub_cover
Size: 120m2
attribute
Risk: Building small
risk_has_attrcovers_risk
Quote: C12
includes
Customer: C12
is_offered
Policy: C12
is_offered
Agreement: C12
signed
User: U34
owns
based_on
contains
made
Questionaire: Q1
concerns
contains_question
owns
fills_in
includes
made
T&C: X
has
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Recommendations, BI, Social Computing
35
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Recommendations, BI, Social Computing
35
๏enrich your CRM with data from Facebook, Google, Twitter etc
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Recommendations, BI, Social Computing
35
๏enrich your CRM with data from Facebook, Google, Twitter etc
๏Recommender systems for products
Thursday, April 19, 12
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Recommendations, BI, Social Computing
35
๏enrich your CRM with data from Facebook, Google, Twitter etc
๏Recommender systems for products
๏Find influencers in your customer base for special treatment
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This is what your CRM sees
36http://inmaps.linkedinlabs.com/network
Customer1
Peter Neubauer
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This is what your CRM doesn’t see.
37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
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This is what your CRM doesn’t see.
37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
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Geospatial features
38
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Geospatial features
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๏Dynamic layers from different sources
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Geospatial features
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๏Dynamic layers from different sources
• domain data -> flood area layer + crime index + firestation + living standard index
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Geospatial features
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๏Dynamic layers from different sources
• domain data -> flood area layer + crime index + firestation + living standard index
๏routes of low insurance risks
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Geospatial features
39
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Geospatial features
40
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Geospatial features
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Configuration/Network Management
42
Thursday, April 19, 12
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Configuration/Network Management
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๏Model physical and logical networks
Thursday, April 19, 12
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Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
Thursday, April 19, 12
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Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
• configuration management
Thursday, April 19, 12
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Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
• configuration management
• network inventory
Thursday, April 19, 12
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Configuration/Network Management
43
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44
Questions!
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and, Thanks :)
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