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講演抄録/キーワード
講演名 2008-09-22 12:45
[招待講演]Data Stream Processing Research at IMC of East China Normal University
Aoying ZhouCheqing JinWeining QianEast China Normal Univ.DE2008-49
抄録 (和) Data stream processing has been attracting more and more attention in research and industry communities due to its broad potential applications. In this talk, we would like to introduce briefly the research work which have been done in our group. Our research interests on data streams are frequent item(set)s mining, clustering, and burst detection over data streams. Some work on practical application and some consideration on future work will be introduced as well.
For the basic problem of mining frequent items over data streams, an algorithm, called hCount is proposed. It is of low space complexity, low per-tuple processing cost, and high recall and precision. Then, for mining of the frequent itemsets, we develop a new false-negative frequent itemset mining algorithm which can get a condensed representation of frequent itemsets in transactional data streams by discovering a false negative collection of some special itemsets that covers frequent itemsets with high probability with respect to set inclusion relationship among itemsets.
Our research on data stream mining was focusing on clustering of data streams. SWClustering is the algorithm we proposed to cluster data streams over sliding windows, and EHCF (Exponential Histogram of Cluster Features) is the synopsis to maintain the statistic information of clusters in sliding windows. With SWClustering, not only the changing distribution of clusters but also the evolving behaviors of individual clusters could be captured. CluDistream is for clustering distributed data streams, which can effectively handle a huge volume of data with noisy, corrupted or incomplete data records generated in distributed enviornment. In CluDistream, the EM-based (Expectation Maximization) algorithms, each data record is assigned to a cluster with certain degree of membership.
The other important piece of work is on burst detection or monitoring over data streams. The fractal analysis method is adapted to enable the monitoring of both monotonic and non-monotonic aggregates on time changing data stream. The monotony property of aggregate monitoring is revealed and monotonic search space is built to decrease the time overhead for detecting bursts from O(m) to O(log m), where m is the number of windows to be monitored. With the help of a novel piecewise fractal model, the statistical summary is compressed to be fit in limited main memory, so that high aggregates on windows of any length can be detected accurately and efficiently on-line.
A practical data stream processing system for telecommunication network flow data analysis will be also introduced in this talk. 
(英) Data stream processing has been attracting more and more attention in research and industry communities due to its broad potential applications. In this talk, we would like to introduce briefly the research work which have been done in our group. Our research interests on data streams are frequent item(set)s mining, clustering, and burst detection over data streams. Some work on practical application and some consideration on future work will be introduced as well.
For the basic problem of mining frequent items over data streams, an algorithm, called hCount is proposed. It is of low space complexity, low per-tuple processing cost, and high recall and precision. Then, for mining of the frequent itemsets, we develop a new false-negative frequent itemset mining algorithm which can get a condensed representation of frequent itemsets in transactional data streams by discovering a false negative collection of some special itemsets that covers frequent itemsets with high probability with respect to set inclusion relationship among itemsets.
Our research on data stream mining was focusing on clustering of data streams. SWClustering is the algorithm we proposed to cluster data streams over sliding windows, and EHCF (Exponential Histogram of Cluster Features) is the synopsis to maintain the statistic information of clusters in sliding windows. With SWClustering, not only the changing distribution of clusters but also the evolving behaviors of individual clusters could be captured. CluDistream is for clustering distributed data streams, which can effectively handle a huge volume of data with noisy, corrupted or incomplete data records generated in distributed enviornment. In CluDistream, the EM-based (Expectation Maximization) algorithms, each data record is assigned to a cluster with certain degree of membership.
The other important piece of work is on burst detection or monitoring over data streams. The fractal analysis method is adapted to enable the monitoring of both monotonic and non-monotonic aggregates on time changing data stream. The monotony property of aggregate monitoring is revealed and monotonic search space is built to decrease the time overhead for detecting bursts from O(m) to O(log m), where m is the number of windows to be monitored. With the help of a novel piecewise fractal model, the statistical summary is compressed to be fit in limited main memory, so that high aggregates on windows of any length can be detected accurately and efficiently on-line.
A practical data stream processing system for telecommunication network flow data analysis will be also introduced in this talk.
キーワード (和) Data stream processing / Frequent item / Clustering / Burst Detection / / / /  
(英) Data stream processing / Frequent item / Clustering / Burst Detection / / / /  
文献情報 信学技報, vol. 108, no. 211, DE2008-49, pp. 39-40, 2008年9月.
資料番号 DE2008-49 
発行日 2008-09-14 (DE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
著作権に
ついて
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
PDFダウンロード DE2008-49

研究会情報
研究会 DE  
開催期間 2008-09-21 - 2008-09-22 
開催地(和) 飯坂ホテル聚楽 
開催地(英)  
テーマ(和) 招待講演・ポスター英語ディスカッション 
テーマ(英)  
講演論文情報の詳細
申込み研究会 DE 
会議コード 2008-09-DE 
本文の言語 英語 
タイトル(和)  
サブタイトル(和)  
タイトル(英) Data Stream Processing Research at IMC of East China Normal University 
サブタイトル(英)  
キーワード(1)(和/英) Data stream processing / Data stream processing  
キーワード(2)(和/英) Frequent item / Frequent item  
キーワード(3)(和/英) Clustering / Clustering  
キーワード(4)(和/英) Burst Detection / Burst Detection  
キーワード(5)(和/英) /  
キーワード(6)(和/英) /  
キーワード(7)(和/英) /  
キーワード(8)(和/英) /  
第1著者 氏名(和/英/ヨミ) Aoying Zhou / Aoying Zhou /
第1著者 所属(和/英) East China Normal University (略称: East China Normal Univ.)
East China Normal University (略称: East China Normal Univ.)
第2著者 氏名(和/英/ヨミ) Cheqing Jin / Cheqing Jin /
第2著者 所属(和/英) East China Normal University (略称: East China Normal Univ.)
East China Normal University (略称: East China Normal Univ.)
第3著者 氏名(和/英/ヨミ) Weining Qian / Weining Qian /
第3著者 所属(和/英) East China Normal University (略称: East China Normal Univ.)
East China Normal University (略称: East China Normal Univ.)
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講演者 第1著者 
発表日時 2008-09-22 12:45:00 
発表時間 45分 
申込先研究会 DE 
資料番号 DE2008-49 
巻番号(vol) vol.108 
号番号(no) no.211 
ページ範囲 pp.39-40 
ページ数
発行日 2008-09-14 (DE) 


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