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Stream-based Data Management

Stream-based Data Management. IS698 Min Song. Characteristics of Data Streams. Data Streams Data streams — continuous, ordered, changing, fast, huge amount Traditional DBMS — data stored in finite, persistent data sets Characteristics Huge volumes of continuous data, possibly infinite

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Stream-based Data Management

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  1. Stream-based Data Management IS698 Min Song

  2. Characteristics of Data Streams • Data Streams • Data streams—continuous, ordered, changing, fast, huge amount • Traditional DBMS—data stored in finite, persistentdata sets • Characteristics • Huge volumes of continuous data, possibly infinite • Fast changing and requires fast, real-time response • Data stream captures nicely our data processing needs of today • Random access is expensive—single linear scan algorithm (can only have one look) • Store only the summary of the data seen thus far • Most stream data are at pretty low-level or multi-dimensional in nature, needs multi-level and multi-dimensional processing

  3. Stream Data Applications • Telecommunication calling records • Business: credit card transaction flows • Network monitoring and traffic engineering • Financial market: stock exchange • Engineering & industrial processes: power supply & manufacturing • Sensor, monitoring & surveillance: video streams • Security monitoring • Web logs and Web page click streams • Massive data sets (even saved but random access is too expensive)

  4. Data Sets:Data Streams: Updates infrequent Data Streams vs. Data Sets • Data changed constantly (sometimes additions only) • Old data required many times • Mostly only freshest data used • Example: employees personal data table • Examples: financial tickers, data feeds from sensors, network monitoring, etc

  5. Query Result Query … Result … Using Traditional Database User/Application Loader

  6. Register Query Result Data Streams Paradigm User/Application Stream Query Processor

  7. Register Query Data Stream Management System (DSMS) Scratch Space (Memory and/or Disk) Data Streams Paradigm User/Application Result Stream Query Processor

  8. Persistent relations Transient streams (and persistent relations) DBMS versus DSMS

  9. Persistent relations Transient streams (and persistent relations) DBMS versus DSMS • One-time queries • Continuous queries

  10. Persistent relations Transient streams (and persistent relations) DBMS versus DSMS • One-time queries • Continuous queries • Random access • Sequential access

  11. Persistent relations Transient streams (and persistent relations) DBMS versus DSMS • One-time queries • Continuous queries • Random access • Sequential access • Access plan determined by query processor and physical DB design • Unpredictable data arrival and characteristics

  12. Persistent relations Transient streams (and persistent relations) DBMS versus DSMS • One-time queries • Continuous queries • Random access • Sequential access • Access plan determined by query processor and physical DB design • Unpredictable data arrival and characteristics • “Unbounded” disk store • Bounded main memory

  13. Challenges of Stream Data Processing • Multiple, continuous, rapid, time-varying, ordered streams • Main memory computations • Queries are often continuous • Evaluated continuously as stream data arrives • Answer updated over time • Queries are often complex • Beyond element-at-a-time processing • Beyond relational queries (scientific, data mining, OLAP) • Multi-level/multi-dimensional processing and data mining • Most stream data are at pretty low-level or multi-dimensional in nature

  14. Processing Stream Queries • Query types • One-time query vs. continuous query (being evaluated continuously as stream continues to arrive) • Predefined query vs. ad-hoc query (issued on-line) • Unbounded memory requirements • For real-time response, main memory algorithm should be used • Memory requirement is unbounded if one will join future tuples • Approximate query answering • With bounded memory, it is not always possible to produce exact answers • High-quality approximate answers are desired • Data reduction and synopsis construction methods • Sketches, random sampling, histograms, wavelets, etc.

  15. Methods for Approximate Query Answering • Sliding windows • Only over sliding windows of recent stream data • Approximation but often more desirable in applications • Batched processing, sampling and synopses • Batched if update is fast but computing is slow • Compute periodically, not very timely • Sampling if update is slow but computing is fast • Compute using sample data, but not good for joins, etc. • Synopsis data structures • Maintain a small synopsis or sketch of data • Good for querying historical data • Blocking operators, e.g., sorting, avg, min, etc. • Blocking if unable to produce the first output until seeing the entire input

  16. Projects on DSMS (Data Stream Management System) • Research projects and system prototypes • STREAM (Stanford): A general-purpose DSMS • Cougar (Cornell): sensors • Aurora(Brown/MIT): sensor monitoring, dataflow • Hancock (AT&T): telecom streams • Niagara (OGI/Wisconsin): Internet XML databases • OpenCQ(Georgia Tech): triggers, incr. view maintenance • Tapestry (Xerox): pub/sub content-based filtering • Telegraph(Berkeley): adaptive engine for sensors • Tradebot(www.tradebot.com): stock tickers & streams • Tribeca (Bellcore): network monitoring • Streaminer (UIUC): new project for stream data mining

  17. Stream Data Mining vs. Stream Querying • Stream mining—A more challenging task • It shares most of the difficulties with stream querying • Patterns are hidden and more general than querying • It may require exploratory analysis • Not necessarily continuous queries • Stream data mining tasks • Multi-dimensional on-line analysis of streams • Mining outliers and unusual patterns in stream data • Clustering data streams • Classification of stream data

  18. Challenges for Mining Unusual Patterns in Data Streams • Most stream data are at pretty low-level or multi-dimensional in nature: needs ML/MD processing • Analysis requirements • Multi-dimensional trends and unusual patterns • Capturing important changes at multi-dimensions/levels • Fast, real-time detection and response

  19. Summary • Stream data analysis: A rich and largely unexplored field • Current research focus in database community: DSMS system architecture, continuous query processing, supporting mechanisms • Stream data mining and stream OLAP analysis • Powerful tools for finding general and unusual patterns • Largely unexplored: current studies only touched the surface • Lots of exciting issues in further study • A promising one: Multi-level, multi-dimensional analysis and mining of stream data

  20. What Is A Continuous Query ? Query which is issued once and logically run continuously.

  21. What is Continuous Query ? Query which is issued once and run continuously. Example: detect abnormalities in network traffic behavior in real-time and their cause -- like link congestion due to hardware failure.

  22. What is Continuous Query ? Query which is issued once and run continuously. More examples: Continues queries used to support load balancing, online automatic trading at Stock Exchange

  23. Special Challenges • Timely online answers even for rapid data streams • Ability of fast access to large portions of data • Processing of multiple streams simultaneously

  24. Central Office Central Office DSMS Making Things Concrete BOB ALICE Outgoing (call_ID, caller, time, event) Incoming (call_ID, callee, time, event) event = startorend

  25. Making Things Concrete • Database = two streams of mobile call records • Outgoing(connectionID, caller, start, end) • Incoming(connectionID, callee, start, end) • Query language = SQL FROM clauses can refer to streams and/or relations

  26. Query 1 (self-join) Find alloutgoing callslonger than2 minutes SELECT O1.call_ID, O1.caller FROM Outgoing O1, Outgoing O2 WHERE (O2.time – O1.time > 2 AND O1.call_ID = O2.call_ID AND O1.event = start AND O2.event = end) • Result requiresunbounded storage • Can provideresult as data stream • Can output after 2 min,without seeing end

  27. Query 2 (join) Pair upcallersand callees SELECT O.caller, I.callee FROM Outgoing O, Incoming I WHERE O.call_ID = I.call_ID • Can still provideresult as data stream • Requiresunbounded temporary storage … • … unless streams arenear-synchronized

  28. Query 3 (group-by aggregation) Total connection timefor each caller SELECT O1.caller, sum(O2.time – O1.time) FROM Outgoing O1, Outgoing O2 WHERE (O1.call_ID = O2.call_ID AND O1.event = start AND O2.event = end) GROUP BY O1.caller • Cannot provide result in (append-only) stream. Alternatives: • Output stream with updates • Provide current value on demand • Keep answer in memory

  29. Conclusions • Conventional DBMS technology is inadequate • We need reconsider all aspects of data management and processing in presence of data streams

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