Tài liệu Data Streams Models and Algorithms- P10 docx

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. [...]... Motwani, Rajeev, and OYCallaghan, Liadan (2003b) Clustering data streams: Theory and practice IEEE TKDE, 15(3):515-528 [1 1] Haykin, Simon (1992) Adaptive Filter Theory Prentice Hall lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark 288 DATA STREAMS: MODELS AND ALGORITHMS [12] Hulten, Geoff, Spencer, Laurie, and Domingos, Pedro (2001) Mining time-changing data streams In KDD... purchase PDF Split-Merge on www.verypdf.com to remove this watermark 280 DATA STREAMS: MODELS AND ALGORITHMS (a) Original measurements vs reconstruction (b) Hidden variables Filplrre 12.4 Mote dataset: (a) shows the measurements (bold) and reconstruction (thin) on nodes 3 1 and 32 (b) the third and fourth hidden variables are intermittent and indicate anomalous behaviour (axes limits are different in each... remove this watermark DATA STREAMS: MODELS AND ALGORITHMS Table 12.2 Description of datasets n Dataset Chlorine Critter River Motes 7 lc 166 8 3 54 2 1-2 1 2-4 Description Chlorine concentrations from EPANET Temperature sensor measurements ~ i v egauge data from USACE i Light sensor measurements Experimental case studies In this section we present case studies on real and realistic datasets to demonstrate... river gauge data lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark References [I] Aggarwal, Cham C Han, Jiawei, and Yu, Philip S (2003) A framework for clustering evolving data streams In VLDB [2] Ali, M.H Mokbel, Mohamed F Aref, Walid, and Kamel, Ibrahim (2005) Detection and tracking of discrete phenomena in sensor network databases In SSDBM [3] Brockwell, Peter J and Davis,... Mining high-speed data streams In KDD [7] Fukunaga, Keinosuke (1990) Introduction to Statistical Pattern Recognition Academic Press [8] Ganti, Venkatesh, Gehrke, Johannes, and Ramakrishnan, Raghu (2002) Mining data streams under block evolution SIGKDD Explorations, 3(2): 1-10 [9] Guha, Sudipto, Gunopulos, Dimitrios, and Koudas, Nick (2003a) Correlating synchronous and asynchronous data streams In KDD [lo]... see that, at the beginning, most streams exhibit a slow dip and then ascent (e.g., see 2, 4 and 5 and, to a lesser extent, 3, 7 and 8) However, a number of them start fluctuating more quickly and violently when the second hidden variable is added lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark Dimensionality Reduction and Forecasting on Streams - River Hiddenvariable (a)... ICDE [25] Young, Peter (1984) Recursive Estimation and Time-Series Analysis: An Introduction Springer-Verlag [26] Zhang, Tian, Ramakrishnan, Raghu, and Livny, Miron (1996) BIRCH: An efficient data clustering method for very large databases In SIGMOD [27] Zhu, Yunyue and Shasha, Dennis (2002) Statstream: Statistical monitoring of thousands of data streams in real time In VLDB lease purchase PDF Split-Merge... data sources Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate In this chapter, we survey the current state of the field and identify potential directions of future research Introduction Advances in technology have enabled us to collect vast amounts of data from various sources,... MINING OF DATA STREAMS Srinivasan Parthasarathy Department of Computer Science and Engineering The Ohio State University srini@cse.ohio-state.edu Am01 Ghoting Department of Computer Science and Engineering The Ohio State University Matthew Eric Otey Department of Computer Science and Engineering The Ohio State University otey Ocse.ohio-state.edu Abstract 1 With advances in data collection and generation... organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets Environments that produce streaming sources of data are becoming common place Examples include stock market, sensor, web click stream, and network data In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources