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EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
EPTS Survey results
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EPTS Survey results

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Presentation of the Event Processing Survey prepared by the Use Cases Workgroup of the Event Processing Technical Society. Presented at the 6th EPTS Symposium at March 24, 2011

Presentation of the Event Processing Survey prepared by the Use Cases Workgroup of the Event Processing Technical Society. Presented at the 6th EPTS Symposium at March 24, 2011

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  • (In)validate assumptionsTypes of users, actions, rules, queries, eventsCompare, constrast use cases
  • Transcript

    • 1. Results of the Survey on Event Processing Use CasesMarch 24, 2011
      Pedro Bizarro on behalf of the
      Use Case Working Group
    • 2. 2
      How are eventprocessingtechnologiesbeingused?
      Classify scenarios,helpothersselect solutions
      Inspire use ofevent processing
      Food for thought forresearchers & engineers
    • 3. Earlier versions
      2007: Kick-off – Problem: world of superficial use cases
      2008: v1: 6 use cases, 54-questions (@4th epts)
      2009: v2: 5 use cases  9 lessonslearned (@debs2009)
      2011: v3: 30 use cases plentyofstatistics (today!)
      3
    • 4. 4
      24 questions
      ~13 minutes
      30 use cases
      EPTS, DEBS community
    • 5. Large Variety of Use Cases
      5
      Document workflow
      Grid
      Home energy
      Patient discharge
      Revenues and expenses
      ETL in Telcos
      Gas station networks
      NYC transportation
      Emergency management
      Content authoring
      Testing algorithms

    • 6. Industry background
      6
      Computer Software
      Healthcare
      Manufacturing
      Retail and distribution
      etc
      <4% each
    • 7. Functional area
      7
    • 8. Maturity level
      8
      52%
    • 9. Primary project drivers
      9
    • 10. Data sources
      10
      Non“streaming”
      “streaming”
    • 11. Destinations and actions
      11
      someautomation
      but peoplestill stronglyin the loop
    • 12. Desired features
      12
    • 13. Data models and data types
      13
    • 14. Performance – input events per second
      14
      Not that much!
    • 15. Number of data sources
      15
      Data comes fromfew sources
    • 16. Number of “AI” models
      16
      3 in 4 don’t answeror don’t use
    • 17. Expected data size growth rate/year (in MB)
      17
      47% increase lessthan 10Gb/year
      or not growing much
      Hard to forecast
    • 18. Why did we use log scales?
      18
      Because we didn’t knowwhat to expect
    • 19. Enterprise capabilities
      19
      Cannot stop!
      Not a big concern
    • 20. Implementation constraints
      20
      surprise
    • 21. The typical use case
      In production to improve banking/utilities user services
      Gets data from databases, files and message queues
      Notifies people and other applications
      Does correlations, joins and aggregations
      Handles less than 1000 events/second (<10 sources)
      Must run a specific CEP engine
      Cannot stop
      21
    • 22. The surprises (personal take)
      Few telcos, transportation and logistics
      Not from IT department
      Not about lower TCO, faster deployments
      “Non-streaming” dominate
      Low throughput, few sources, little growth
      Not about forecasting, predictions
      Very few AI models
      Not about security, encryption
      22
    • 23. Membersandacknowledgements
      Pedro Bizarro <bizarro@dei.uc.pt>
      ChristophEmmersberger <christoph.emmersberger@citt-online.com>
      Thomas Ertlmaier <thomas.ertlmaier@citt-online.com>
      Matthew Cooper <M.Cooper@eventzero.com>
      Tina Groves <Tina.Groves@ca.ibm.com>
      Dieter Gawlick <dieter.gawlick@oracle.com>
      Brian Connell <brian@westglobal.com>
      23
    • 24. Q&A?
      24

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