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22nd International Conference on Data Engineering (ICDE'06)最新文献

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Learning from Aggregate Views 从聚合视图中学习
22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.86
Bee-Chung Chen, Lei Chen, R. Ramakrishnan, D. Musicant
{"title":"Learning from Aggregate Views","authors":"Bee-Chung Chen, Lei Chen, R. Ramakrishnan, D. Musicant","doi":"10.1109/ICDE.2006.86","DOIUrl":"https://doi.org/10.1109/ICDE.2006.86","url":null,"abstract":"In this paper, we introduce a new class of data mining problems called learning from aggregate views. In contrast to the traditional problem of learning from a single table of training examples, the new goal is to learn from multiple aggregate views of the underlying data, without access to the un-aggregated data. We motivate this new problem, present a general problem framework, develop learning methods for RFA (Restriction-Free Aggregate) views defined using COUNT, SUM, AVG and STDEV, and offer theoretical and experimental results that characterize the proposed methods.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"492 1","pages":"3-3"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76724219","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: 34
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
SaveRF: Towards Efficient Relevance Feedback Search SaveRF:迈向高效的相关反馈搜索
22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.132
Heng Tao Shen, B. Ooi, K. Tan
{"title":"SaveRF: Towards Efficient Relevance Feedback Search","authors":"Heng Tao Shen, B. Ooi, K. Tan","doi":"10.1109/ICDE.2006.132","DOIUrl":"https://doi.org/10.1109/ICDE.2006.132","url":null,"abstract":"In multimedia retrieval, a query is typically interactively refined towards the ‘optimal’ answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. In this paper, we introduce a new approach called SaveRF (Save random accesses in Relevance Feedback) for iterative relevance feedback search. SaveRF predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses can be saved, hence reducing the number of random accesses. In addition, efficient scan on the overlap before the search starts also tightens the search space with smaller pruning radius. We implemented SaveRF and our experimental study on real life data sets show that it can reduce the I/O cost significantly.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"151 1","pages":"110-110"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76739217","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: 1
Declarative Network Monitoring with an Underprovisioned Query Processor 使用未充分配置的查询处理器的声明性网络监控
22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.46
Frederick Reiss, J. Hellerstein
{"title":"Declarative Network Monitoring with an Underprovisioned Query Processor","authors":"Frederick Reiss, J. Hellerstein","doi":"10.1109/ICDE.2006.46","DOIUrl":"https://doi.org/10.1109/ICDE.2006.46","url":null,"abstract":"Many of the data sources used in stream query processing are known to exhibit bursty behavior. We focus here on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio. Provisioning a stream query processor to handle peak rates in such a setting can be prohibitively expensive. In this paper, we propose to solve this problem by provisioning the query processor for typical data rates instead of much higher peak data rates. To enable this strategy, we present mechanisms and policies for managing the tradeoffs between the latency and accuracy of query results when bursts exceed the steady-state capacity of the query processor. We describe the current status of our implementation and present experimental results on a testbed network monitoring application to demonstrate the utility of our approach","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"744 1","pages":"56-56"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76879152","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: 31
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
{"title":"ACXESS - Access Control for XML with Enhanced Security Specifications","authors":"Sriram Mohan, Jonathan Klinginsmith, Arijit Sengupta, Yuqing Wu","doi":"10.1109/ICDE.2006.12","DOIUrl":"https://doi.org/10.1109/ICDE.2006.12","url":null,"abstract":"We present ACXESS (Access Control for XML with Enhanced Security Specifications), a system for specifying and enforcing enhanced security constraints on XML via virtual \"security views\" and query rewrites. ACXESS is the first system that bears the capability to specify and enforce complicated security policies on both subtrees and structural relationships.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"23 1","pages":"171-171"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79696932","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: 16
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
{"title":"Mondrian Multidimensional K-Anonymity","authors":"K. LeFevre, D. DeWitt, R. Ramakrishnan","doi":"10.1109/ICDE.2006.101","DOIUrl":"https://doi.org/10.1109/ICDE.2006.101","url":null,"abstract":"K-Anonymity has been proposed as a mechanism for protecting privacy in microdata publishing, and numerous recoding \"models\" have been considered for achieving ��anonymity. This paper proposes a new multidimensional model, which provides an additional degree of flexibility not seen in previous (single-dimensional) approaches. Often this flexibility leads to higher-quality anonymizations, as measured both by general-purpose metrics and more specific notions of query answerability. Optimal multidimensional anonymization is NP-hard (like previous optimal ��-anonymity problems). However, we introduce a simple greedy approximation algorithm, and experimental results show that this greedy algorithm frequently leads to more desirable anonymizations than exhaustive optimal algorithms for two single-dimensional models.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"1 1","pages":"25-25"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82839072","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: 1209
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
{"title":"R-trees with Update Memos","authors":"Xiaopeng Xiong, Walid G. Aref","doi":"10.1109/ICDE.2006.125","DOIUrl":"https://doi.org/10.1109/ICDE.2006.125","url":null,"abstract":"The problem of frequently updating multi-dimensional indexes arises in many location-dependent applications. While the R-tree and its variants are one of the dominant choices for indexing multi-dimensional objects, the R-tree exhibits inferior performance in the presence of frequent updates. In this paper, we present an R-tree variant, termed the RUM-tree (stands for R-tree with Update Memo) that minimizes the cost of object updates. The RUM-tree processes updates in a memo-based approach that avoids disk accesses for purging old entries during an update process. Therefore, the cost of an update operation in the RUM-tree reduces to the cost of only an insert operation. The removal of old object entries is carried out by a garbage cleaner inside the RUM-tree. In this paper, we present the details of the RUM-tree and study its properties. Theoretical analysis and experimental evaluation demonstrate that the RUMtree outperforms other R-tree variants by up to a factor of eight in scenarios with frequent updates.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"65 1","pages":"22-22"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85127798","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: 79
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
{"title":"Composition and Disclosure of Unlinkable Distributed Databases","authors":"B. Malin, L. Sweeney","doi":"10.1109/ICDE.2006.41","DOIUrl":"https://doi.org/10.1109/ICDE.2006.41","url":null,"abstract":"An individual’s location-visit pattern, or trail, can be leveraged to link sensitive data back to identity. We propose a secure multiparty computation protocol that enables locations to provably prevent such linkages. The protocol incorporates a controllable parameter specifying the minimum number of identities a sensitive piece of data must be linkable to via its trail.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"25 1","pages":"118-118"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84029051","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: 5
Approximation Techniques for Indexing the Earth Mover’s Distance in Multimedia Databases 多媒体数据库中推土机距离索引的近似技术
22nd International Conference on Data Engineering (ICDE'06) Pub Date : 2006-04-03 DOI: 10.1109/ICDE.2006.25
I. Assent, Andrea Wenning, T. Seidl
{"title":"Approximation Techniques for Indexing the Earth Mover’s Distance in Multimedia Databases","authors":"I. Assent, Andrea Wenning, T. Seidl","doi":"10.1109/ICDE.2006.25","DOIUrl":"https://doi.org/10.1109/ICDE.2006.25","url":null,"abstract":"Todays abundance of storage coupled with digital technologies in virtually any scientific or commercial application such as medical and biological imaging or music archives deal with tremendous quantities of images, videos or audio files stored in large multimedia databases. For content-based data mining and retrieval purposes suitable similarity models are crucial. The Earth Mover’s Distance was introduced in Computer Vision to better approach human perceptual similarities. Its computation, however, is too complex for usage in interactive multimedia database scenarios. In order to enable efficient query processing in large databases, we propose an index-supported multistep algorithm. We therefore develop new lower bounding approximation techniques for the Earth Mover’s Distance which satisfy high quality criteria including completeness (no false drops), index-suitability and fast computation. We demonstrate the efficiency of our approach in extensive experiments on large image databases","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"91 1","pages":"11-11"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83790023","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: 82
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
{"title":"C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking","authors":"Dong Xin, Zheng Shao, Jiawei Han, Hongyan Liu","doi":"10.1109/ICDE.2006.31","DOIUrl":"https://doi.org/10.1109/ICDE.2006.31","url":null,"abstract":"It is well recognized that data cubing often produces huge outputs. Two popular efforts devoted to this problem are (1) iceberg cube, where only significant cells are kept, and (2) closed cube, where a group of cells which preserve roll-up/drill-down semantics are losslessly compressed to one cell. Due to its usability and importance, efficient computation of closed cubes still warrants a thorough study. In this paper, we propose a new measure, called closedness, for efficient closed data cubing. We show that closedness is an algebraic measure and can be computed efficiently and incrementally. Based on closedness measure, we develop an an aggregation-based approach, called C-Cubing (i.e., Closed-Cubing), and integrate it into two successful iceberg cubing algorithms: MM-Cubing and Star-Cubing. Our performance study shows that C-Cubing runs almost one order of magnitude faster than the previous approaches. We further study how the performance of the alternative algorithms of C-Cubing varies w.r.t the properties of the data sets.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"93 1","pages":"4-4"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83886403","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: 54
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