intro to neo4j or why insurances should love graphs
DESCRIPTION
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.TRANSCRIPT
Introduction to Graph Databases
1
@peterneubauer #neo4j
Thursday, April 19, 12
What’s the plan?
2
Thursday, April 19, 12
What’s the plan?
๏Why a graph?
2
Thursday, April 19, 12
What’s the plan?
๏Why a graph?
๏Graph Database 101
2
Thursday, April 19, 12
What’s the plan?
๏Why a graph?
๏Graph Database 101
๏a look at Neo4j
2
Thursday, April 19, 12
What’s the plan?
๏Why a graph?
๏Graph Database 101
๏a look at Neo4j
๏the Real World
2
Thursday, April 19, 12
Why a graph?
3
Thursday, April 19, 12
Q: What are graphs good for?
4
Thursday, April 19, 12
Q: What are graphs good for?
4
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
Thursday, April 19, 12
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
Thursday, April 19, 12
Trends in BigData & NOSQL
5
Thursday, April 19, 12
Trends in BigData & NOSQL
5
๏1. increasing data size (big data)
Thursday, April 19, 12
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
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
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
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
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
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
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
Thursday, April 19, 12
4 Categories of NOSQL
6
Thursday, April 19, 12
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
7
Thursday, April 19, 12
Key-Value: Pros & Cons๏Strengths
• Simple data model
•Great at scaling out horizontally
• Scalable
•Available
๏Weaknesses:
• Simplistic data model
• Poor for complex data
8
Thursday, April 19, 12
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
9
Thursday, April 19, 12
Column-Family: Pros & Cons๏Strengths
•Data model supports semi-structured data
•Naturally indexed (columns)
•Good at scaling out horizontally
๏Weaknesses:
•Unsuited for interconnected data
10
Thursday, April 19, 12
Document Database Category๏Data model
•Collections of documents
•A document is a key-value collection
• Index-centric, lots of map-reduce
๏Examples
•CouchDB, MongoDB
11
Thursday, April 19, 12
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
12
Thursday, April 19, 12
Graph Database Category๏Data model:
•Nodes & Relationships
•Hypergraph, sometimes (edges with multiple endpoints)
๏Examples:
•Neo4j (of course), OrientDB, InfiniteGraph, AllegroGraph
13
Thursday, April 19, 12
14
Living in a NOSQL WorldCo
mpl
exity
Size
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
Size
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
Size
Key-ValueStore
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
GraphDatabases
Thursday, April 19, 12
RDBMS
14
Living in a NOSQL WorldCo
mpl
exity
ColumnFamily
Size
Key-ValueStore
DocumentDatabases
GraphDatabases
90%of
usecases
Thursday, April 19, 12
Graph Database: Pros & Cons
15
Thursday, April 19, 12
Graph Database: Pros & Cons๏Strengths
15
Thursday, April 19, 12
Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
15
Thursday, April 19, 12
Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
15
Thursday, April 19, 12
Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
• Easy to query
15
Thursday, April 19, 12
Graph Database: Pros & Cons๏Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
• Easy to query
๏Weaknesses:
15
Thursday, April 19, 12
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)
15
Thursday, April 19, 12
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
15
Thursday, April 19, 12
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
15
Thursday, April 19, 12
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
15
Thursday, April 19, 12
Graph DB 101
16
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphsThursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly star
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin robertson
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin hortonrobertson
Thursday, April 19, 12
Some well-known named graphs
17see http://en.wikipedia.org/wiki/Gallery_of_named_graphs
diamond butterfly bullstar
franklin horton hall-jankorobertson
Thursday, April 19, 12
We’re talking about a Property Graph
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
๏Relationships
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
๏Relationships
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
๏Relationships
18
Thursday, April 19, 12
We’re talking about a Property Graph
๏Nodes
๏Relationships
๏Properties
18
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
Compared to RDBMS
19
becomes
Thursday, April 19, 12
A look at Graph Queries
20
Thursday, April 19, 12
Query a graph with a traversal
21
Thursday, April 19, 12
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
Thursday, April 19, 12
// 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
Thursday, April 19, 12
// 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
Thursday, April 19, 12
22
Cypher
Thursday, April 19, 12
22
Cypher๏a pattern-matching query language
๏declarative grammar with clauses (like SQL)
๏aggregation, ordering, limits
๏tabular results
Thursday, April 19, 12
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
Thursday, April 19, 12
Neo4j - the Graph Database
23
Thursday, April 19, 12
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
Thursday, April 19, 12
Neo4j is a Graph Database
25
Thursday, April 19, 12
Neo4j is a Graph Database๏A Graph Database:
25
Thursday, April 19, 12
Neo4j is a Graph Database๏A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
25
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
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
Thursday, April 19, 12
the Real World
26
Thursday, April 19, 12
Q: What are graphs good for?
27
Thursday, April 19, 12
Q: What are graphs good for?
27
๏Recommendations
๏Business intelligence
๏Social computing
๏Geospatial
๏MDM
๏Systems management
๏Genealogy
A: highly connected data
Thursday, April 19, 12
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
Thursday, April 19, 12
[A] ACL from Hell
28
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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)
Thursday, April 19, 12
[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
Thursday, April 19, 12
[A] ACL from Hell
29
Thursday, April 19, 12
[B] Timely Recommendations
30
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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
Thursday, April 19, 12
[C] Collaboration on Global Scale
31
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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
Thursday, April 19, 12
[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
Thursday, April 19, 12
Insurance <3 Graphs?
32
Thursday, April 19, 12
Q: Why should you care?
33
Thursday, April 19, 12
Q: Why should you care?
33
A: because you have connected data.
Thursday, April 19, 12
Q: Why should you care?
33
๏CRM, BI, social graphs
A: because you have connected data.
Thursday, April 19, 12
Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
A: because you have connected data.
Thursday, April 19, 12
Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
๏Fraud detection
A: because you have connected data.
Thursday, April 19, 12
Q: Why should you care?
33
๏CRM, BI, social graphs
๏GeoSpatial analytics
๏Fraud detection
๏Network management
A: because you have connected data.
Thursday, April 19, 12
A sample insurance domain setup
34
Thursday, April 19, 12
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
Thursday, April 19, 12
Recommendations, BI, Social Computing
35
Thursday, April 19, 12
Recommendations, BI, Social Computing
35
๏enrich your CRM with data from Facebook, Google, Twitter etc
Thursday, April 19, 12
Recommendations, BI, Social Computing
35
๏enrich your CRM with data from Facebook, Google, Twitter etc
๏Recommender systems for products
Thursday, April 19, 12
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
Thursday, April 19, 12
This is what your CRM sees
36http://inmaps.linkedinlabs.com/network
Customer1
Peter Neubauer
Thursday, April 19, 12
This is what your CRM doesn’t see.
37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
This is what your CRM doesn’t see.
37http://inmaps.linkedinlabs.com/networkThursday, April 19, 12
Geospatial features
38
Thursday, April 19, 12
Geospatial features
38
๏Dynamic layers from different sources
Thursday, April 19, 12
Geospatial features
38
๏Dynamic layers from different sources
• domain data -> flood area layer + crime index + firestation + living standard index
Thursday, April 19, 12
Geospatial features
38
๏Dynamic layers from different sources
• domain data -> flood area layer + crime index + firestation + living standard index
๏routes of low insurance risks
Thursday, April 19, 12
Geospatial features
39
Thursday, April 19, 12
Geospatial features
40
Thursday, April 19, 12
Geospatial features
41
Thursday, April 19, 12
Configuration/Network Management
42
Thursday, April 19, 12
Configuration/Network Management
42
๏Model physical and logical networks
Thursday, April 19, 12
Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
Thursday, April 19, 12
Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
• configuration management
Thursday, April 19, 12
Configuration/Network Management
42
๏Model physical and logical networks
• impact analysis
• configuration management
• network inventory
Thursday, April 19, 12
Configuration/Network Management
43
Thursday, April 19, 12
44
Questions!
Thursday, April 19, 12
45
and, Thanks :)
Thursday, April 19, 12