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Introduction to Stream Computing


11 December 2013

Stream Computing - Examples

General Examples

  • Monitoring streams of low-level sensor inputs and emitting high-level interpretations of the raw data
  • Responding to streams emanating from sensor inputs by emitting actuator commands
  • Monitoring streams of data from Intrusion Detection sensor inputs and emitting alerts
  • Monitoring streams of video data and interpreting scenes, scene changes, etc.

InfoSphere Streams Examples

  • Traffic control system which takes in GPS information from public vehicles and caculates deviations from normal to recommend alternative routes
  • Real-time correlation of information from multiple neonatal ICU monitors to detect potential life threatening conditions up to 24 hours earlier than an experienced ICU nurse

The Need for Stream Computing

A need for real-time analytics on BIG data

  • Volume
    Millions/Billions of events per second
    Terabytes/Petabytes per day

  • Variety
    All kinds of data
    All kinds of analytics

  • Velocity
    Insights in microseconds

  • Agility
    Dynamically responsive
    Rapid application development

###Traditional Computing

  • Historical fact finding
  • Find and analyse information stored on disk
  • Batch paradigm, pull model
  • Query-driven: submit queries to static data

###Stream Computing

  • Current fact finding
  • Analyze data in motion - before it is stored
  • Low latency paradigm, push model
  • Data-driven: bring data to the analytics

What is Stream Processing?

  • Relational databases and warehouses find information stored on disk
  • Streams analyses data before you store it
  • Databases find the needle in the haystack
  • Streams finds the needle as it’s blowing by


[1] Brian Williams, IBM InfoSphere Streams Developer Education Section 1, October 2013.

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