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Data Streams Models and Algorithms- P10

Data Streams Models and Algorithms- P10: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time. | Indexing and Querying Data Streams 259 24 S. Papadimitriou A. Brockwell and C. Faloutsos. AWSOM Adaptive hands-off stream mining. In VLDB pages 560-571 2003. 25 N. Roussopoulos S. Kelley and F. Vincent. Nearest neighbor queries pages 71-79 1995. 26 H. Wu B. Salzberg and D. Zhang. Online event-driven subsequence matching over financial data streams. In SIGMOD pages 23-34 2004. 27 B. Wyman and D. Werner. Content-based Publish-Subscribe over APEX. In Internet-Draft April 2002. 28 B. Yi N. Sidiropoulos T. Johnson H. Jagadish C. Faloutsos and A. Biliris. Online data mining for co-evolving time sequences. In ICDE 2000. 29 P. Young. Recursive Estimation and Time-Series Analysis An Introduction. Springer-Verlag 1984. 30 Y. Zhu and D. Shasha. Statstream Statistical monitoring of thousands of data streams in real time. In VLDB pages 358-369 2002. 31 Y. Zhu and D. Shasha. Efficient elastic burst detection in data streams. In SIGKDD pages 336 - 345 2003. lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Chapter 12 DIMENSIONALITY REDUCTION AND FORECASTING ON STREAMS Spiros Papadimitriou 1 Jimeng Sun 2 and Christos Faloutsos2 IBM Watson Research Center Hawthorne NY USA spapadim @ us.ibm.com 9 Carnegie Mellon University Pittsburgh PA USA jimeng@cs.cmu.edu christos@cs.cmu.edu Abstract We consider the problem of capturing correlations and finding hidden variables corresponding to trends on collections of time series streams. Our proposed method SPIRIT can incrementally find correlations and hidden variables which summarise the key trends in the entire stream collection. It can do this quickly with no buffering of stream values and without comparing pairs of streams. Moreover it is any-time single pass and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies to do efficient forecasting and more generally to dramatically simplify further data processing. Introduction In this chapter we consider the .