Multimedia Data Stream Management System

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.