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Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
Active Insight - Event Stream Processing In The Cloud
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Active Insight - Event Stream Processing In The Cloud

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The activeInsight open source event stream processing platforms enables cloud based event processing, correlation, aggregation and reaction to events and patterns

The activeInsight open source event stream processing platforms enables cloud based event processing, correlation, aggregation and reaction to events and patterns

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    • 1. ACTIVE INSIGHT
      Event Stream Processing in the Cloud
      Mike Telem
      Business Development
    • 2. Table of Contents
      • Background: The Digital Era
      • 3. Processing, Correlating and Aggregating Events
      • 4. Use Cases: From Behavioral Targeting to Electrical Smart Grids
      • 5. ESP in the Cloud
      • 6. Roadmap: Where is ActiveInsight headed
    • The Digital Era
      • Our world is becoming digital…
      • 7. Cell phones, web sites, GPS devices, cars, ads, Financial transactions,…
      • 8. RFID, industrial eq., security sensors, border controls, medical eq.,…
      • 9. Utilities, pipelines, meters, digital signage, home appliances, entertainment devices, cars, …
      • 10. Applications, infrastructure, web-services, customer data,…
      • 11. Markets, stocks, currencies, news, wiki’s, blogs, tweets,…
      • 12.
      • 13. Multiple events share various perspectives
      • 14. Event stream quantity and frequency will fluctuate
      • 15. Effective time window for reactions is minimal
      • 16. Reaction channels may vary
      • 17. Events should be correlated with historical data
    • Event Stream Processing (ESP)
      • Event Stream Processing:
      • 18. Processing application level events in a distributed environment
      • 19. Event Correlation – Directing multiple event streams based on their context to the corresponding ESP containers
      • 20. Complex Event Processing:
      • 21. Processing multiple events to detect meaningful patterns using correlation, aggregation and time-frames
      • 22. Pattern detection: Detecting specific event combinations and patterns in contexts
      • 23. Cross-Context Correlation: Processing multiple streams into multiple contexts / perspectives (fraud / marketing)
      • 24. Aggregation: Accumulating correlated events into time-based contexts, support for “event state machine” aggregation.
      • 25. Data Integration: Caching data sources as “reference data” for processing
      • 26. Reaction: Invoking an action after a successful event or pattern match
    • Different Use-cases > Similar Challenges
      • Online Gaming : Real-time BI, money laundering, local compliance, application offload
      • 27. Online Advertisement: Behavioral targeting, multiple site click-stream correlation
      • 28. Ecommerce : Identifying customer interests (up-sell/cross—sell) , Improving conversion rates, anonymous user hooking, campaign management
      • 29. Online Self-Service : Identifying customer turnover or dissatisfaction, Monitor user experience and assist in transaction completion
      • 30. Algo-Trading : performance and availability improvements and HW cost reduction
      • 31. Auditing: Feeding “Who” did “What” and “When” to auditing and SIEM systems
      • 32. Fraud detection: Fraudulent behavior pattern detection, Bot detection, alongside fraud detection systems
      • 33. Electrical smart-grid: Detecting misuse, mal-functions, on-demand supply
      • 34. Home Land Security: Enhance airport and border security, correlate multiple events, intelligence data and incoming alerts
      • 35. Traffic management: Vehicle location management for Insurers, authorities and drivers
      • 36. …Similar Challenges
    • Different Use-cases > Similar Challenges
      Process
      Correlate
      Aggregate
      Match
      React
    • 37. ESP in the Cloud
      • Elastic ESP
      • 38. On-demand usage
      • 39. Scaling up and out to varying event frequencies
      • 40. ESP as a service
      • 41. Offloading event processing
      • 42. Dynamic Stream Sources
      • 43. Dynamic event sources
      • 44. Handling remote event sources
      • 45. SaaS Enabler
      • 46. Porting event-oriented applications to the cloud
      • 47. SaaScomponent
      • 48. Enhance SaaS applications
      • 49. Offload the core application
      • 50. Comply to regional regulations
      • 51. Provide SaaS Application integration
      • 52. IaaS/Hosting
      • 53. Value Added Services (Security, Auditing, BI)
      • 54. Customer Experience Management
    • ActiveInsight.org
      Distributed Event Stream Processing Framework
      • Real-time event processing
      • 55. Multi-source event stream processing
      • 56. Event correlation and aggregation
      • 57. Pattern matching
      • 58. Integrated data caching
      • 59. Embeddable framework
      • 60. Scalable, elastic cloud run-time
    • Typical Architecture
      Mobile Device
      Web App
      Distributed Cache
      Distributed Cache
      Distributed Cache
      Reference Data
      Reference Data
      Reference Data
      Context
      Context
      Context
      Context
      Context
      Context
      Car GPS
      AI Server Node
      AI Server Node
      AI Server Node
      Process
      Process
      Process
      Match
      React
      Match
      React
      Match
      React
      Contexts
      Marketing
      Security
    • 61. Roadmap
      • Cloud ESP
      • 62. Amazon Public Image, Devpay
      • 63. SaaSconnectivity (Sales Force, etc.)
      • 64. First SaaS OEM’s
      • 65. AI in additional IaaS
      • 66. Enhanced ESP
      • 67. Integrating mathematical/statistical engines (R-server)
      • 68. In-memory data-grid integration
      • 69. Clustered ESP container
      • 70. Pattern Recognition
    • Q&A
      Thank you!
      http://www.activeinsight.org
      mike.telem@activeinsight.net

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