22nd International Conference on Data Engineering (ICDE'06)最新文献_第5页

R-trees with Update Memos 带有更新备忘录的r树

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.125

Xiaopeng Xiong, Walid G. Aref

引用次数: 79

Segmentation of Publication Records of Authors from the Web 网络作者出版记录的分割

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.137

Wei Zhang, Clement T. Yu, N. Smalheiser, Vetle I. Torvik

引用次数: 4

Composition and Disclosure of Unlinkable Distributed Databases 不可链接分布式数据库的组成与披露

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.41

B. Malin, L. Sweeney

引用次数: 5

Efficient Discovery of Emerging Frequent Patterns in ArbitraryWindows on Data Streams 数据流任意窗口中频繁模式的有效发现

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.57

Xiaoming Jin, Xinqiang Zuo, K. Lam, Jianmin Wang, Jiaguang Sun

{"title":"Efficient Discovery of Emerging Frequent Patterns in ArbitraryWindows on Data Streams","authors":"Xiaoming Jin, Xinqiang Zuo, K. Lam, Jianmin Wang, Jiaguang Sun","doi":"10.1109/ICDE.2006.57","DOIUrl":"https://doi.org/10.1109/ICDE.2006.57","url":null,"abstract":"This paper proposes an effective data mining technique for finding useful patterns in streaming sequences. At present, typical approaches to this problem are to search for patterns in a fixed-size window sliding through the stream of data being collected. The practical values of such approaches are limited in that, in typical application scenarios, the patterns are emerging and it is difficult, if not impossible, to determine a priori a suitable window size within which useful patterns may exist. It is therefore desirable to devise techniques that can identify useful patterns with arbitrary window sizes. Attempts to this problem are challenging, however, because it requires a highly efficient searching in a substantially bigger solution space. This paper presents a new method which includes firstly a pruning strategy to reduce the search space and secondly a mining strategy that adopts a dynamic index structure to allow efficient discovery of emerging patterns in a streaming sequence. Experimental results on real data and synthetic data show that the proposed method outperforms other existing schemes both in computational efficiency and effectiveness in finding useful patterns.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"18 1","pages":"113-113"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87089894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

What’s Different: Distributed, Continuous Monitoring of Duplicate-Resilient Aggregates on Data Streams 不同之处:数据流上重复弹性聚合的分布式、连续监控

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.173

Graham Cormode, S. Muthukrishnan, W. Zhuang

{"title":"Whats Different: Distributed, Continuous Monitoring of Duplicate-Resilient Aggregates on Data Streams","authors":"Graham Cormode, S. Muthukrishnan, W. Zhuang","doi":"10.1109/ICDE.2006.173","DOIUrl":"https://doi.org/10.1109/ICDE.2006.173","url":null,"abstract":"Emerging applications in sensor systems and network-wide IP traffic analysis present many technical challenges. They need distributed monitoring and continuous tracking of events. They have severe resource constraints not only at each site in terms of per-update processing time and archival space for highspeed streams of observations, but also crucially, communication constraints for collaborating on the monitoring task. These elements have been addressed in a series of recent works. A fundamental issue that arises is that one cannot make the \"uniqueness\" assumption on observed events which is present in previous works, since widescale monitoring invariably encounters the same events at different points. For example, within the network of an Internet Service Provider packets of the same flow will be observed in different routers; similarly, the same individual will be observed by multiple mobile sensors in monitoring wild animals. Aggregates of interest on such distributed environments must be resilient to duplicate observations. We study such duplicate-resilient aggregates that measure the extent of the duplication―how many unique observations are there, how many observations are unique―as well as standard holistic aggregates such as quantiles and heavy hitters over the unique items. We present accuracy guaranteed, highly communication-efficient algorithms for these aggregates that work within the time and space constraints of high speed streams. We also present results of a detailed experimental study on both real-life and synthetic data.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"7 1","pages":"57-57"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85632651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 74

SIPPER: Selecting Informative Peers in Structured P2P Environment for Content-Based Retrieval 基于内容检索的结构化P2P环境中信息对等点的选择

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.139

Shuigeng Zhou, Zhengjie Zhang, Weining Qian, Aoying Zhou

引用次数: 11

ACXESS - Access Control for XML with Enhanced Security Specifications 访问控制与增强的安全规范的XML

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.12

Sriram Mohan, Jonathan Klinginsmith, Arijit Sengupta, Yuqing Wu

引用次数: 16

C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking C-Cubing:基于聚合检查的封闭立方体的高效计算

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.31

Dong Xin, Zheng Shao, Jiawei Han, Hongyan Liu

引用次数: 54

Mondrian Multidimensional K-Anonymity 蒙德里安多维k -匿名

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.101

K. LeFevre, D. DeWitt, R. Ramakrishnan

引用次数: 1209

Continuous Reverse Nearest Neighbor Monitoring 连续反向最近邻监控

22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.43

Tian Xia, Donghui Zhang

{"title":"Continuous Reverse Nearest Neighbor Monitoring","authors":"Tian Xia, Donghui Zhang","doi":"10.1109/ICDE.2006.43","DOIUrl":"https://doi.org/10.1109/ICDE.2006.43","url":null,"abstract":"Continuous spatio-temporal queries have recently received increasing attention due to the abundance of location-aware applications. This paper addresses the Continuous Reverse Nearest Neighbor (CRNN) Query. Given a set of objects O and a query set Q, the CRNN query monitors the exact reverse nearest neighbors of each query point, under the model that both the objects and the query points may move unpredictably. Existing methods for the reverse nearest neighbor (RNN) query either are static or assume a priori knowledge of the trajectory information, and thus do not apply. Related recent work on continuous range query and continuous nearest neighbor query relies on the fact that a simple monitoring region exists. Due to the unique features of the RNN problem, it is non-trivial to even define a monitoring region for the CRNN query. This paper defines the monitoring region for the CRNN query, discusses how to perform initial computation, and then focuses on incremental CRNN monitoring upon updates. The monitoring region according to one query point consists of two types of regions. We argue that the two types should be handled separately. In continuous monitoring, two optimization techniques are proposed. Experimental results prove that our proposed approach is both efficient and scalable.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"5 1","pages":"77-77"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76392151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 97