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Multimedia Data Stream Management System

Multimedia Data Stream Management System. By David Kleinman. Outline. Definition Motivating Examples Nine Requirements Current Systems Comparison Brief Overview of current Stream Systems Preview of My Project. What is it?. Stream of multimedia data from a source (video camera)

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Multimedia Data Stream Management System

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  1. Multimedia Data Stream Management System By David Kleinman

  2. Outline • Definition • Motivating Examples • Nine Requirements • Current Systems • Comparison • Brief Overview of current Stream Systems • Preview of My Project

  3. What is it? • Stream of multimedia data from a source (video camera) • Query stored in a system (This query may itself change • Process high volumes of data in real-time

  4. Motivating Examples • Security Surveillance • Crowd Security • Air Security • Burglary • Baby Sitting • Traffic Reports • Science • Animal behavior • Ocean

  5. Reqirement #1 - Process Quickly • Low latency • Messages Processed “In-Stream” • No Storage to perform operation • Active System • Avoid Polling

  6. Requirement #2 – Query using SigmaQL for Streams (StreamSigmaQL) • Querying Mechanism • Based on SQL • Express Continuous Streams of Data • Window Construct • Time • Frames • Breakpoints • Merge Operator

  7. Requirement # 3 –Handle Imperfections • Data might be late delayed, missing, or out-of sequence • Time out individual calculations or computations • Challenges with Dealing with out-of-order data • Mechanism for additional time

  8. Requirement #4 – Generate Predictable Outcomes • Generate deterministic and repeatable results • Time-ordered deterministic processing throughout entire pipeline • Important for fault tolerance and recovery

  9. Requirement #5 – Integrate Stored and Streaming Data • Comparing present with past • Capability to efficiently store, access, and modify state information

  10. Requirement #6 – Guarantee Data Safety • Must use a high-availability solution • Secondary System • Synchronizes with primary frequently • Takes over in case of failure

  11. Requirement #7 – Partition and Scale Automatically • Take advantage of distributed computing • Support multi-threading • Takes advantage of multi-processor • Avoids blocking • Load Balance across machines • Automatic process • Transparent

  12. Requirement #8 – Process and Respond Instantaneously • Needs to respond in real – time • Highly optimized, minimal overhead execution path • All system components have high performance

  13. Requirement #9 - Adaptability • Change queries without restarting • Accept all different types of multimedia streams • Allow for custom configuration • Work with different systems • API

  14. DBMS • Widely used • Use SQL – but not equipped for Streams • Passive • Do not keep data moving • Difficult to handle out of order data • Difficulty with predictable out comes • Incur latency with seamless integration

  15. Rule Engine • Example – Prolog • Active • Handle imperfections • Troubles with seamless integration

  16. Stream Processing Engine • Handle all the requirements • Not specifically designed to handle multimedia constraints • Not Specifically designed to handle streams of multimedia

  17. Chart

  18. Aurora • DSMS developed at MIT and Brown

  19. QoS . . . . . . QoS QoS Aurora Query Network

  20. Stream Management System • Developed at Stanford

  21. Simple Query Plan Q1 Q2  State3 ⋈ State4 Scheduler State1 ⋈ State2 Stream3 Stream1 Stream2

  22. NiagaraCQ • Developed at Wisconsin • First DSMS • Uses a grouping strategy • Not as complete as other two

  23. System Architecture

  24. TelegraphCQ • Developed at Berkeley • Stem – storage point • Eddy – route tuples • Good at handling multiple queries • Adaptive

  25. R S T Adaptivity (Telegraph) Output Queues STeMs for join grouped filter (R.A) R EDDY S grouped filter (S.B) R x S x T T Input Streams • Runtime Adaptivity • Multi-query Optimization • Framework – implements arbitrary schemes

  26. My Project • Design a multimedia streaming database • Outline the specifications • The Scheduling algorithm • The query structure • The operators • Etc.

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