Temporal Graph Analytics with GRADOOP Christopher Rost and Kevin - - PowerPoint PPT Presentation

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Temporal Graph Analytics with GRADOOP Christopher Rost and Kevin - - PowerPoint PPT Presentation

Oct 20, 2022 182 likes •504 views

Temporal Graph Analytics with GRADOOP Christopher Rost and Kevin Gomez Leipzig University About the speakers and the team Kevin, Christopher, Prof. Dr. Erhard Rahm, PhD Student PhD Student Database Chair Timo, Lucas, M.Sc. Student

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Temporal Graph Analytics with GRADOOP

Christopher Rost and Kevin Gomez Leipzig University

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About the speakers and the team

Kevin, PhD Student Christopher, PhD Student

  • Prof. Dr. Erhard Rahm,

Database Chair Timo, M.Sc. Student Lucas, B.Sc. Student

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We’re hiring!

CENTER FOR SCALABLE DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE

  • Visit us at www.scads.de
  • Open positions and projects (Developer and PhD Students)
  • Various topics:
  • Machine Learning (Graph-ML, Privacy)
  • Image Recognition
  • Blockchain (Distributed-Ledger)
  • Graph Analytics
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Motivation

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“Graphs are everywhere”

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“Graphs are everywhere”

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“Graphs are everywhere”

friendOf f r i e n d O f f r i e n d O f friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf f r i e n d O f Anna Bob Chris Sally Dutch Arthur Jack Sheldon Raj Mike James John

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“Graphs are everywhere”

friendOf f r i e n d O f f r i e n d O f friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf f r i e n d O f Anna Bob Chris Sally Dutch Arthur Jack Sheldon Raj Mike James John age:29 sex: f yob: 1991

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“Graphs are everywhere”

friendOf f r i e n d O f f r i e n d O f friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf friendOf f r i e n d O f Anna Bob Chris Sally Dutch Arthur Jack Sheldon Raj Mike James John age:29 sex: f yob: 1991 since: 5Y

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“A open-source framework and research platform for efficient, distributed and domain independent management and analytics of heterogeneous and temporal graph data.“

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Architecture

HDFS / YARN

Distributed Operator Implementation TPGM EPGM G raph A nalytical L anguage (GrALa)

Java 8 45k LOC ALv 2.0

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Graphs and collections of graphs

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Operators

Operators Unary Binary Logical Graph Graph Collection Algorithms Frequent Subgraph

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Temporal extension

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Temporal extension

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Temporal extension

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Temporal extension

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Time dependent operators

Operators Unary Binary Logical Graph Graph Collection Algorithms Frequent Subgraph

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Time dependent operators

Operators Unary Binary (Temp-)Logical Graph (Temp-)Graph Collection Algorithms Frequent Subgraph

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Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units Surgery Accident Cardiology Oncology Contact tracking Sensors capturing who is close to whom at what time. {empID1, empID2, t_from, t_to}

In case of an infection, which hospital services are at risk of contracting the virus X?

Use case: spread of airborne pathogens

Images by Vecteezy.com.

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time t0 t1 t5 ... t10 t15 t20 TemporalGraph contacts = mySource.getTemporalGraph(); TemporalGraph contacts = mySource.getTemporalGraph();

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time Employee [-,-) id: 32432 srv: Oncology Employee [-,-) id: 99934 srv: Oncology contact [ t2 ,t6 ) t0 t1 t5 ... t10 t15 t20

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time t0 t1 t5 ... t10 t15 t20 Breaking news: Employee of oncology infected by VirusX. Detected at t20.

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time t0 t1 t5 ... t10 t15 t20 Breaking news: Employee of oncology infected by VirusX. Detected at t20. Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units

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time t0 t1 t5 ... t10 t15 t20 Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot snapshot( new FromTo(t15, t20)); TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot snapshot( new FromTo(t15, t20));

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time t0 t1 t5 ... t10 t15 t20 Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot snapshot( new FromTo(t15, t20)); TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot snapshot( new FromTo(t15, t20));

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time t0 t1 t5 ... t10 t15 t20 Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);
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time t0 t1 t5 ... t10 t15 t20 Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

contact maxDur: 40 min contact maxDur: 122 min contact maxDur: 124 min contact maxDur: 146 min contact maxDur: 2 min contact maxDur: 9 min

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time t0 t1 t5 ... t10 t15 t20 Virus X Symptoms: 3rd eye growing Transmission: if contact > 5 min Incubation period: 5 time units TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

contact maxDur: 40 min contact maxDur: 122 min contact maxDur: 124 min contact maxDur: 146 min contact maxDur: 2 min contact maxDur: 9 min

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time t0 t1 t5 ... t10 t15 t20

contact maxDur: 40 min contact maxDur: 122 min contact maxDur: 124 min contact maxDur: 146 min contact maxDur: 2 min contact maxDur: 9 min

Breaking news: Employees of oncology and surgery quarantined because of VirusX. TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);

TemporalGraph contacts = mySource.getTemporalGraph(); contacts = contacts.snapshot( new FromTo(t15, t20)); contacts = contacts.groupBy groupBy( (v v[‘srv’]),

  • [],

(e e.getLabel()),

  • [new MaxDuration()]);
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Conclusion

  • Distributed graph analysis system
  • Temporal property graph model

– Bitemporal support – Logical graphs and graph collections – Composing operators and algorithms

  • Declarative workflow creation
  • Visit Gradoop: http://gradoop.com
  • Read our Wiki: https://github.com/dbs-leipzig/gradoop/wiki
  • Start: https://github.com/dbs-leipzig/gradoop/wiki/Getting-started
  • Try the examples: https://github.com/dbs-leipzig/gradoop/wiki/Examples