Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. [...]... Management of Data (SIGMOD), 2003 [2] B Babcock, S Babu, M Datar, R Motwani, and J Widom Models and issues in data stream systems In Proceedings of the Symposium on Principles of Database Systems (PODS), 2002 [3] B Babcock and C Olston Distributed top k monitoring In Proceedings of the International Conference on Management of Data (SIGMOD), 2003 [4] V Barnett and T Lewis Outliers in Statistical Data John... Wiley and Sons, 1994 [5] J Beringer and E Hullermeier Online clustering of parallel data streams Data and Knowledge Engineering, 2005 [6] A Bulut and A Singh SWAT: Hierarchical stream summarization in large networks In Proceedings of the International Conference on Data Engineering (ZCDE), 2003 [7] L Chen, K Reddy, and G Agrawal GATES: A grid-based middleware for processing distributed data streams. .. Mining o Data Streams f f 305 [12] Y Chi, P Yu, H Wang, and R Muntz Loadstar: A load shedding scheme f for classifying data streams In Proceedings o the SIAM International Conference on Data Mining (SDM), 2005 [13] M Ester, H Kriegel, J Sander, M Wirnmer, and X Xu Incremental clustering for mining in a data warehousing environment In Proceedings o the International Conference on Yey Large Data Bases... Sarkar, M Klein, M Vasa, and D Handy Vedas: A mobile and distributed data stream mining system for real-time vehicle monitoring In Proceedings o the SIAM International Conference on Data Mining f (SDM), 2004 [26] H Kargupta and B Park A fourier spectrum based approach to represent decision trees for mining data streams in mobile environments IEEE Transactions on Knowledge and Data Engineering, 2004 [27]... successful stream mining system must adapt to the dynamics of the data and best use the available set of resources and components In this section, we will briefly summarize efforts that target the building of system support for resource-aware distributed processing of streams When processing continuous data streams, data arrival can be bursty, and the data rate may fluctuate over time Systems that seek to give... Chen, D Sivakumar, and H Kargupta An approach to online bayesian network learning from multiple data streams In Proceedings of the International Conference on Principles of Data Mining and Knowledge Discovery, 200 1 [9] A Chervenak, I Foster, C Kesselrnan, C Salisbury, and S Tuecke The data grid: Towards an architecture for the distributed management and analysis of large scientific data sets, 2001 [lo]... 304 DATA STREAMS: MODELS AND ALGORITHMS that can be efficiently maintained in a distributed and incremental setting should be designed and then specific solutions that use these statistics should be devised Such a design strategy will facilitate distributed stream mining algorithm design References [I] B Babcock, S Babu, M Datar, and R Motwani Chain: Operator scheduling for memory minimization in data. .. bandwidth The Vehicle Data Stream Mining System (VEDAS) [25], is a mobile and distributed data stream mining/monitoring application that taps into the continuous stream of data generated by most modern vehicles It allows continuous on-board monitoring of the data streams generated by the moving vehicles, identifying the emerging patterns, and reporting them to a remote control tenter over a low-bandwidth... Proceedings o the International Conference on Data Engineering (ICDE), 2005 [35] G Manku and R Motwani Approximate frequency counts over data f streams In Proceedings o the International Conference on Very Large Data Bases (VLDB), 2002 [36] M Otey, A Ghoting, and S Parthasarathy Fast distributed outlier detection in mixed attribute data sets Data Mining and Knowledge Discovery Journal, 2006 lease purchase... Survey o Distributed Mining o Data Streams f f 307 [37] M Otey, S Parthasarathy, A Ghoting, G Li, S Narravula, and D Panda f Towards nic-based instrusion detection In Proceedings o the International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2003 [38] M Otey, C Wang, S Parthasarathy, A Veloso, and W Meira Mining frequent itemsets in distributed and dynamic databases In Proceedings o the