21st International Conference on Data Engineering (ICDE'05)最新文献_第3页

Odysseus: a high-performance ORDBMS tightly-coupled with IR features 奥德修斯:一个高性能ORDBMS与IR特性紧密耦合

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.95

K. Whang, Min-Jae Lee, Jae-Gil Lee, Min-Soo Kim, Wook-Shin Han

引用次数: 30

Reverse nearest neighbors in large graphs 在大图中反转最近邻

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.124

Man Lung Yiu, D. Papadias, N. Mamoulis, Yufei Tao

引用次数: 43

Data privacy through optimal k-anonymization 通过最优k-匿名化实现数据隐私

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.42

R. Bayardo, R. Agrawal

{"title":"Data privacy through optimal k-anonymization","authors":"R. Bayardo, R. Agrawal","doi":"10.1109/ICDE.2005.42","DOIUrl":"https://doi.org/10.1109/ICDE.2005.42","url":null,"abstract":"Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838802","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: 1327

Filter based directory replication and caching 基于过滤器的目录复制和缓存

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.67

Apurva Kumar

引用次数: 3

On the optimal ordering of maps and selections under factorization 分解下映射的最优排序与选择

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.97

Thomas Neumann, S. Helmer, G. Moerkotte

引用次数: 15

Adaptive overlapped declustering: a highly available data-placement method balancing access load and space utilization 自适应重叠聚类:一种平衡访问负载和空间利用率的高可用数据放置方法

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.16

Akitsugu Watanabe, H. Yokota

引用次数: 18

Adaptive process management with ADEPT2 使用ADEPT2进行自适应流程管理

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.17

M. Reichert, S. Rinderle-Ma, U. Kreher, P. Dadam

引用次数: 187

Corpus-based schema matching 基于语料库的模式匹配

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.39

J. Madhavan, P. Bernstein, A. Doan, A. Halevy

{"title":"Corpus-based schema matching","authors":"J. Madhavan, P. Bernstein, A. Doan, A. Halevy","doi":"10.1109/ICDE.2005.39","DOIUrl":"https://doi.org/10.1109/ICDE.2005.39","url":null,"abstract":"Schema matching is the problem of identifying corresponding elements in different schemas. Discovering these correspondences or matches is inherently difficult to automate. Past solutions have proposed a principled combination of multiple algorithms. However, these solutions sometimes perform rather poorly due to the lack of sufficient evidence in the schemas being matched. In this paper we show how a corpus of schemas and mappings can be used to augment the evidence about the schemas being matched, so they can be matched better. Such a corpus typically contains multiple schemas that model similar concepts and hence enables us to learn variations in the elements and their properties. We exploit such a corpus in two ways. First, we increase the evidence about each element being matched by including evidence from similar elements in the corpus. Second, we learn statistics about elements and their relationships and use them to infer constraints that we use to prune candidate mappings. We also describe how to use known mappings to learn the importance of domain and generic constraints. We present experimental results that demonstrate corpus-based matching outperforms direct matching (without the benefit of a corpus) in multiple domains.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330488","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: 435

An enhanced query model for soccer video retrieval using temporal relationships 基于时间关系的足球视频检索增强查询模型

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.20

Shu‐Ching Chen, M. Shyu, Na Zhao

引用次数: 20

Exploiting correlated attributes in acquisitional query processing 在获取查询处理中利用相关属性

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.63

A. Deshpande, Carlos Guestrin, W. Hong, S. Madden

{"title":"Exploiting correlated attributes in acquisitional query processing","authors":"A. Deshpande, Carlos Guestrin, W. Hong, S. Madden","doi":"10.1109/ICDE.2005.63","DOIUrl":"https://doi.org/10.1109/ICDE.2005.63","url":null,"abstract":"Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358476","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: 138