Thu, 21 Aug 2008 15:23:38 BST CiteULike: bigbossman's streams http://www.citeulike.org/user/bigbossman/tag/streams CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/bigbossman/article/1511921 <i>e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on (2005), pp. 347-352.</i><br /><br />This paper presents a formal method of detecting emerging and changing interests that appear in document streams arriving continuously over time. Examples of such document streams include email, news articles, and Web logs (or blogs). We utilize the temporal information associated with documents in the streams and discover emerging issues and topics of interest and their change by detecting buzzwords in the documents. Buzzwords are terms that occur with strong momentum for a relatively short period of time. Our approach for buzz detection is based on the notion of "burst of activities" proposed by Kleinberg [2002]. The burst of activities is modeled using a weighted automaton. We propose an algorithm to discover buzzwords of high intensity measured by their momentum and relative duration of the bursts. The method is applied and validated on a stream of blog postings and we report the experiment results. Detecting buzz from time-sequenced document streams Jeonghee Yi e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on (2005), pp. 347-352. 2007-07-29T19:40:04-00:00 2005 e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on 347 352 burst buzz document streams http://www.citeulike.org/user/bigbossman/article/1469541 <i>(2003)</i><br /><br />Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly di#erent from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma... Efficient elastic burst detection in data streams Y Zhu D Shasha (2003) 2007-07-20T14:28:29-00:00 2003 burst data detection streams http://www.citeulike.org/user/bigbossman/article/1198518 <i>(2006), pp. 407-418.</i> High-performance complex event processing over streams Eugene Wu Yanlei Diao Shariq Rizvi doi:10.1145/1142473.1142520 (2006), pp. 407-418. 2007-03-30T15:49:20-00:00 2006 407 418 ACM Press complex event processing streams http://www.citeulike.org/user/bigbossman/article/1369090 <i>(1992), pp. 115-126.</i> SVP: A Model Capturing Sets, Lists, Streams, and Parallelism Douglas Parker Eric Simon Patrick Valduriez (1992), pp. 115-126. 2007-06-06T22:13:16-00:00 1992 115 126 Morgan Kaufmann Publishers Inc. lists sets streams http://www.citeulike.org/user/bigbossman/article/1015219 <i>Discrete & Computational Geometry, Vol. 36, No. 4. (December 2006), pp. 633-655.</i> Range Counting over Multidimensional Data Streams Suri Subhash Toth D Csaba Zhou Yunhong doi:10.1007/s00454-006-1269-4 Discrete & Computational Geometry, Vol. 36, No. 4. (December 2006), pp. 633-655. 2006-12-26T17:24:17-00:00 2006 Discrete & Computational Geometry 0179-5376 36 4 633 655 Springer data streams summary http://www.citeulike.org/user/bigbossman/article/1126986 <i>Data Mining and Knowledge Discovery, Vol. V7, No. 4. (1 October 2003), pp. 373-397.</i> Bursty and Hierarchical Structure in Streams Jon Kleinberg doi:10.1023/A:1024940629314 Data Mining and Knowledge Discovery, Vol. V7, No. 4. (1 October 2003), pp. 373-397. 2007-02-27T11:50:48-00:00 2003 Data Mining and Knowledge Discovery V7 4 373 397 burst streams textmining