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Topic

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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Journal ArticleDOI
TL;DR: An implemented technique for producing optimizing compilers forDSELs, based on Kamin's idea of DSELs for program generation, using a data type of syntax for basic types, a set of smart constructors that perform rewriting over those types, some code motion transformations, and a back-end code generator.
Abstract: Functional languages are particularly well-suited to the interpretive implementations of Domain-Specific Embedded Languages (DSELs). We describe an implemented technique for producing optimizing compilers for DSELs, based on Kamin's idea of DSELs for program generation. The technique uses a data type of syntax for basic types, a set of smart constructors that perform rewriting over those types, some code motion transformations, and a back-end code generator. Domain-specific optimization results from chains of domain-independent rewrites on basic types. New DSELs are defined directly in terms of the basic syntactic types, plus host language functions and tuples. This definition style makes compilers easy to write and, in fact, almost identical to the simplest embedded interpreters. We illustrate this technique with a language Pan for the computationally intensive domain of image synthesis and manipulation.

155 citations

Journal ArticleDOI
01 Aug 2008
TL;DR: This paper designs a unified framework for processing sliding-window top-k queries on uncertain streams, and shows that all the existing top-K definitions in the literature can be plugged into this framework, resulting in several succinct synopses that use space much smaller than the window size.
Abstract: Query processing on uncertain data streams has attracted a lot of attentions lately, due to the imprecise nature in the data generated from a variety of streaming applications, such as readings from a sensor network. However, all of the existing works on uncertain data streams study unbounded streams. This paper takes the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on arguably one of the most important types of queries---top-k queries.The challenge of answering sliding-window top-k queries on uncertain data streams stems from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, combined with the difficulty of coping with the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top-k queries on uncertain streams. We show that all the existing top-k definitions in the literature can be plugged into our framework, resulting in several succinct synopses that use space much smaller than the window size, while are also highly efficient in terms of processing time. In addition to the theoretical space and time bounds that we prove for these synopses, we also present a thorough experimental report to verify their practical efficiency on both synthetic and real data.

155 citations

Proceedings Article
27 Jul 2011
TL;DR: A series of generative probabilistic models are proposed, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them.
Abstract: We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic bornIn relation between a person and location entity. Concretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. The output of each model is a clustering of observed relation tuples and their associated textual expressions to underlying semantic relation types. Our proposed models exploit entity type constraints within a relation as well as features on the dependency path between entity mentions. We examine effectiveness of our approach via multiple evaluations and demonstrate 12% error reduction in precision over a state-of-the-art weakly supervised baseline.

155 citations

Journal ArticleDOI
01 Dec 2007
TL;DR: The main finding of the paper will be that metrics may behave differently through different algorithms and may not show correlations with some applications’ accuracy on output data.
Abstract: k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper evaluates a family of clustering-based algorithms that are more flexible and even attempts to improve precision by ignoring the restrictions of user-defined Domain Generalization Hierarchies. The evaluation of the new approaches with respect to cost metrics shows that metrics may behave differently with different algorithms and may not correlate with some applications' accuracy on output data.

154 citations

Book ChapterDOI
14 Mar 1988
TL;DR: A logic-based language for manipulating complex objects constructed using set and tuple constructors is introduced and applications of the language to procedural data, semantic database models, heterogeneous databases integration, and datalog query evaluation are presented.
Abstract: A logic-based language for manipulating complex objects constructed using set and tuple constructors is introduced. A key feature of the language is the use of base and derived data functions. Under some stratification restrictions, the semantics of programs is given by a canonical minimal and causal model that can be computed using a finite sequence of fixpoints. Applications of the language to procedural data, semantic database models, heterogeneous databases integration, and datalog query evaluation are presented.

154 citations


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