2. 1 Stream-based Data Management IS698 Min Song

3. 2 Characteristics of Data Streams  Data Streams Data streams — continuous, ordered, changing, fast, huge amount data sets Traditional DBMS — data stored in finite, persistent data 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

4. 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)

5. 4 Data Streams vs. Data Sets Data Sets:Data Streams:  Updates infrequent  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

6. 5 Using Traditional Database User/ApplicationUser/Application LoaderLoader QueryResult Result…Query…

7. 6 Data Streams Paradigm User/ApplicationUser/Application Register Query Stream Query Processor Result

8. 7 Data Streams Paradigm User/ApplicationUser/Application Register Query Stream Query Processor Result Scratch Space (Memory and/or Disk) Data Stream Management System (DSMS)

9. 8 DBMS versus DSMS Persistent relations Transient streams (and persistent relations)

10. 9 DBMS versus DSMS Persistent relationsTransient streams (and persistent relations) One-time queriesOne-time queries Continuous queriesContinuous queries

11. 10 DBMS versus DSMS Persistent relations Transient streams (and persistent relations) One-time queriesOne-time queries Continuous queriesContinuous queries Random accessRandom access Sequential accessSequential access

12. 11 DBMS versus DSMS Persistent relations Transient streams (and persistent relations) One-time queriesOne-time queries Continuous queriesContinuous queries Random accessRandom access Sequential accessSequential access Access plan determined by query processor and physical DB designAccess plan determined by query processor and physical DB design Unpredictable data arrival and characteristicsUnpredictable data arrival and characteristics

13. 12 DBMS versus DSMS Persistent relations Transient streams (and persistent relations) One-time queriesOne-time queries Continuous queriesContinuous queries Random accessRandom access Sequential accessSequential access Access plan determined by query processor and physical DB designAccess plan determined by query processor and physical DB design Unpredictable data arrival and characteristicsUnpredictable data arrival and characteristics “Unbounded” disk store“Unbounded” disk store Bounded main memoryBounded main memory

14. 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

15. 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.

16. 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

17. 16 Projects on DSMS (Data Stream Management System)  Research projects and system prototypes STREAM STREAM (Stanford): A general-purpose DSMS Cougar Cougar (Cornell): sensors Aurora Aurora (Brown/MIT): sensor monitoring, dataflow Hancock Hancock (AT&T): telecom streams Niagara Niagara (OGI/Wisconsin): Internet XML databases OpenCQ OpenCQ (Georgia Tech): triggers, incr. view maintenance Tapestry Tapestry (Xerox): pub/sub content-based filtering Telegraph Telegraph (Berkeley): adaptive engine for sensors Tradebot Tradebot (www.tradebot.com): stock tickers & streamswww.tradebot.com Tribeca Tribeca (Bellcore): network monitoring Streaminer Streaminer (UIUC): new project for stream data mining

18. 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

19. 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

20. 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

21. 20 What Is A Continuous Query ? Query which is issued once and logically run continuously.

22. 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.

23. 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

24. 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

25. 24 Making Things Concrete Outgoing (call_ID, caller, time, event) Incoming (call_ID, callee, time, event) event = start or end Central Office Central Office DSMS BOBALICE

26. 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

27. 26 Query 1 (self-join) Find all outgoing calls longer than 2 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 requires unbounded storage  Can provide result as data stream  Can output after 2 min, without seeing end

28. 27 Query 2 (join) Pair up callers and callees SELECT O.caller, I.callee FROM Outgoing O, Incoming I WHERE O.call_ID = I.call_ID  Can still provide result as data stream  Requires unbounded temporary storage …  … unless streams are near-synchronized

29. 28 Query 3 (group-by aggregation) Total connection time for 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

30. 29 Conclusions  Conventional DBMS technology is inadequate  We need reconsider all aspects of data management and processing in presence of data streams