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Conference

Symposium on Principles of Database Systems 

About: Symposium on Principles of Database Systems is an academic conference. The conference publishes majorly in the area(s): Conjunctive query & Query language. Over the lifetime, 1393 publications have been published by the conference receiving 102305 citations.


Papers
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Proceedings ArticleDOI
03 Jun 2002
TL;DR: The need for and research issues arising from a new model of data processing, where data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams are motivated.
Abstract: In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

2,933 citations

Proceedings ArticleDOI
03 Jun 2002
TL;DR: The tutorial is focused on some of the theoretical issues that are relevant for data integration: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.
Abstract: Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

2,716 citations

Proceedings ArticleDOI
21 Mar 1983
TL;DR: It is shown that every protocol for this problem has the possibility of nontermination, even with only one faulty process, in the asynchronous consensus problem.
Abstract: The consensus problem involves an asynchronous system of processes, some of which may be unreliable. The problem is for the reliable processes to agree on a binary value. We show that every protocol for this problem has the possibility of nontermination, even with only one faulty process. By way of contrast, solutions are known for the synchronous case, the "Byzantine Generals" problem.

2,017 citations

Proceedings ArticleDOI
01 May 1998
TL;DR: It is proved that under certain conditions LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance.
Abstract: Latent semantic indexing LSI is an information retrieval technique based on the spectral analysis of the term document matrix whose empirical success had heretofore been without rigorous prediction and explanation We prove that under certain conditions LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance We also propose the technique of random projection as a way of speeding up LSI We complement our theorems with encouraging experimental results We also argue that our results may be viewed in a more general framework as a theoretical basis for the use of spectral methods in a wider class of applications such as collaborative ltering

1,235 citations

Proceedings ArticleDOI
Dakshi Agrawal1, Charu C. Aggarwal1
01 May 2001
TL;DR: It is proved that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data, and proposed metrics for quantification and measurement of privacy-preserving data mining algorithms are proposed.
Abstract: The increasing ability to track and collect large amounts of data with the use of current hardware technology has lead to an interest in the development of data mining algorithms which preserve user privacy. A recently proposed technique addresses the issue of privacy preservation by perturbing the data and reconstructing distributions at an aggregate level in order to perform the mining. This method is able to retain privacy while accessing the information implicit in the original attributes. The distribution reconstruction process naturally leads to some loss of information which is acceptable in many practical situations. This paper discusses an Expectation Maximization (EM) algorithm for distribution reconstruction which is more effective than the currently available method in terms of the level of information loss. Specifically, we prove that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data. We show that when a large amount of data is available, the EM algorithm provides robust estimates of the original distribution. We propose metrics for quantification and measurement of privacy-preserving data mining algorithms. Thus, this paper provides the foundations for measurement of the effectiveness of privacy preserving data mining algorithms. Our privacy metrics illustrate some interesting results on the relative effectiveness of different perturbing distributions.

1,091 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202131
202031
201932
201834
201734
201637