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Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.


Papers
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Proceedings ArticleDOI
01 Jan 2006
TL;DR: An efficient algorithm for important class association rule mining using genetic network programming (GNP) and measuring the significance of the association via the chi-squared test to present a classifier using these extracted rules.
Abstract: An efficient algorithm for important class association rule mining using genetic network programming (GNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. Instead of generating a large number of candidate rules, the method can obtain a sufficient number of important association rules for classification. The proposed method measures the significance of the association via the chi-squared test. Therefore, all the extracted important rules can be used for classification directly. In addition, the method suits class association rule mining from dense databases, where many frequently occurring items are found in each tuple. Users can define conditions of extracting important class association rules. In this paper, we describe an algorithm for class association rule mining with chi-squared test using GNP and present a classifier using these extracted rules.

70 citations

Journal ArticleDOI
TL;DR: It is shown that augmenting Horn-clause logic with hypothetical addition increases its data-complexity from PTIME to PSPACE, and any typed generic query that is computable in PSPACE can be expressed as a stratified rulebase of hypothetical additions and deletions.

70 citations

Patent
15 Mar 2000
TL;DR: In this paper, traces are used to analyze tuple space access in distributed systems, resulting in optimizations based upon certain conditions which, if met, enable a tuple to remain visible in tuple space without blocking, so that other processes can continue to read the tuple while a first process is updating the tuple.
Abstract: A data processing system stores information in tuple space as tuples that are accessible by multiple entities. Methods, apparatus, computer-readable media, and data structures provide efficient extensions to tuple space coordination languages for example Linda, that increase concurrency by lessening tuple removal, without requiring compile time analysis, altering existing primitives, or adding new primitives. Traces are used to analyze tuple space access in distributed systems, resulting in optimizations based upon certain conditions which, if met, enable a tuple to remain visible in tuple space without blocking, so that other processes can continue to read the tuple while a first process is updating the tuple. A run-time optimization modifies the conditions if the execution is in a closed system that is known not to intentionally contain deadlock, further improving performance.

70 citations

Proceedings Article
23 Sep 2007
TL;DR: This paper proposes an alternative approach to materializing probabilistic views, by giving conditions under which a view can be represented by a block-independent disjoint (BID) table, and proposes a novel partial representation that can represent all views but may not define a unique probability distribution.
Abstract: Views over probabilistic data contain correlations between tuples, and the current approach is to capture these correlations using explicit lineage. In this paper we propose an alternative approach to materializing probabilistic views, by giving conditions under which a view can be represented by a block-independent disjoint (BID) table. Not all views can be represented as BID tables and so we propose a novel partial representation that can represent all views but may not define a unique probability distribution. We then give conditions on when a query's value on a partial representation will be uniquely defined. We apply our theory to two applications: query processing using views and information exchange using views. In query processing on probabilistic data, we can ignore the lineage and use materialized views to more efficiently answer queries. By contrast, if the view has explicit lineage, the query evaluation must reprocess the lineage to compute the query resulting in dramatically slower execution. The second application is information exchange when we do not wish to disclose the entire lineage, which otherwise may result in shipping the entire database. The paper contains several theoretical results that completely solve the problem of deciding whether a conjunctive view can be represented as a BID and whether a query on a partial representation is uniquely determined. We validate our approach experimentally showing that representable views exist in real and synthetic workloads and show over three magnitudes of improvement in query processing versus a lineage based approach.

70 citations

Proceedings ArticleDOI
01 May 1998
TL;DR: Overall, this paper believes it is the first to demonstrate by implementation experience that it is practical to build a compiler for HPF using a general and powerful integer-set framework.
Abstract: In this paper, we describe our experience with using an abstract integer-set framework to develop the Rice dHPF compiler, a compiler for High Performance Fortran. We present simple, yet general formulations of the major computation partitioning and communication analysis tasks as well as a number of important optimizations in terms of abstract operations on sets of integer tuples. This approach has made it possible to implement a comprehensive collection of advanced optimizations in dHPF, and to do so in the context of a more general computation partitioning model than previous compilers. One potential limitation of the approach is that the underlying class of integer set problems is fundamentally unable to represent HPF data distributions on a symbolic number of processors. We describe how we extend the approach to compile codes for a symbolic number of processors, without requiring any changes to the set formulations for the above optimizations. We show experimentally that the set representation is not a dominant factor in compile times on both small and large codes. Finally, we present preliminary performance measurements to show that the generated code achieves good speedups for a few benchmarks. Overall, we believe we are the first to demonstrate by implementation experience that it is practical to build a compiler for HPF using a general and powerful integer-set framework.

70 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023203
2022459
2021210
2020285
2019306
2018266