Graph All The Things! - Andreas Kollegger @ Neo4j GraphDays: #AllYouCanGraph Palo Alto 2014
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Graph all the Things A story of data and relationships Andreas Kollegger Product Designer @akollegger
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Recent years have seen an explosion of technologies for managing, processing and analyzing graphs. While the most well known users of graph technologies have been social web properties such as Facebook and LinkedIn, a quiet revolution has been steadily spreading across other industries. In this last 18 months, more than 30 of the Global 2000, and many times as many startups, have quietly been working to apply graphs to a wide array of business-critical use cases. For example: one of the world’s top parcel delivery carriers wasn’t going to be able to handle Christmas volumes last year because of numerous challenges stemming from online ordering. The solution? Replace the legacy routing system with a graph database, which now routes 5M packages per day in real time: faster and more efficiently than its relational cousins ever could. One of the top investment banks now onboards traders using an identity & access management system based on graphs. Media metadata turns out to be best represented as a graph; and consumers respond well to the opportunity to visually navigate the graph (such as is done by the app Discovr Music). Similar trends are developing in telecommunications, healthcare, human resources, gaming, and many more. We are entering an era of connected data: where those companies that can master the connections between their data – the lines and patterns linking the dots, and not just the dots – will outperform the companies that fail to recognize connectedness.
Transcript of Graph All The Things! - Andreas Kollegger @ Neo4j GraphDays: #AllYouCanGraph Palo Alto 2014
Graph all the ThingsA story of data and relationships
Andreas Kollegger Product Designer
@akollegger
2005: Lusaka, Zambia
2005: Lusaka, Zambia
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
SNOMED?
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
SNOMED? LOINC?
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
SNOMED? LOINC? Non-English terms and local idioms?
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
2005: Lusaka, Zambia
SNOMED? LOINC? Non-English terms and local idioms?
ALS Lou Gehrig's Disease
Amyotrophic lateral sclerosismotor neurone disease
Charcot disease
MND
ICD-10G12.2
ICD-9335.20
OMIM105400
Categories and Indicators and Related Conditions and Treatments and Drugs?
Hey, who wants to come to Sweden to hack on Neo4j for 6 months?
2000: Sweden
2000: Sweden
2000: Sweden
2000: Sweden
2000: Sweden
Graphs are not a new idea, just the right idea right now.
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Graphs are everywhere!
Neo Technology, Inc Confidential
NOSQL == Not Only SQL
Hint: Relationships Matter
Put all of us into a database. !
Ask an RDBMS: “What's the average age of everyone here?”
!Ask Neo4j:
“Who should I get to know better?”
Member Group
Member Group
143 Andreas
Member Group
143 Andreas326 Stockholm
725 San Francisco
981 Boston
Member GroupMember_Group
143 Andreas326 Stockholm
725 San Francisco
981 Boston
Member GroupMember_Group
143 Andreas326 Stockholm
725 San Francisco
981 Boston143 981
143 725
143 326
Andreas
India
San Francisco
Boston
143 326
725
981
143 981
143 725
143 326
Andreas
India
San Francisco
Boston
Property Graph
Nodes
Property Graph
Nodes
Property Graph
Nodes
Member
Group
Group
Group
with Labels
Property Graph
Nodes
Member
Group
Group
Group
with LabelsRelationships
Property Graph
Nodes
uid: ABK name: Andreas
uid: STK where: Stockholm
uid: SFO where: San Francisco
uid: BOS where: Boston
Member
Group
Group
Group
with LabelsRelationships with Type
MEMBER_OF
MEMBER_OF
MEMBER_O
F
since: 2009
since: 2013
since: 2012
Property Graph
Nodes
uid: ABK name: Andreas
uid: STK where: Stockholm
uid: SFO where: San Francisco
uid: BOS where: Boston
Member
Group
Group
Group
with LabelsRelationships with Type
MEMBER_OF
MEMBER_OF
MEMBER_O
F
since: 2009
since: 2013
since: 2012
Properties on both
Property Graph
What about the wolves?!? Tell me about the wolves!
How Wolves Change Rivers: Research Results
How Wolves Change Rivers: Known Influences Modeled as a Graph
How Wolves Change Rivers: Known Influences Modeled as a Graph
How Wolves Change Rivers: Query for Trophic Cascades
!MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"})WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS numberRETURN factor AS How_Wolves_Affect_RiverStabilityORDER BY numberLIMIT 5
How Wolves Change Rivers: Query for Trophic Cascades
!MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"})WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS numberRETURN factor AS How_Wolves_Affect_RiverStabilityORDER BY numberLIMIT 5
Thank you!Think about relationships,
Follow paths, Find your graph
