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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.


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Patent
17 Oct 2011
TL;DR: In this article, the authors present a system for automatically converting a manual test case representation (in a natural language) into a machine-readable test-case representation using a methodical process of trial-and-error to resolve ambiguities.
Abstract: A computer system, method and computer program product for automatically converting, through automating-test-automation software, a manual test case representation (in a natural language), for testing a target software, into a machine-readable test case representation. In preferred embodiments, the machine-readable test case is in the form of a keyword-based test case that is made from action-target-data tuples. The automation-test-software uses a methodical process of trial-and-error to resolve ambiguities that are generally present (and generally resolvable by humans) in the manual test case representation.

53 citations

Proceedings ArticleDOI
29 Mar 2009
TL;DR: This paper proposes techniques which leverage the COUNT information to ef¿ciently acquire unbiased samples of the hidden database and discusses variants for interfaces which do not provide COUNTinformation.
Abstract: A large number of online databases are hidden behind form-like interfaces which allow users to execute search queries by specifying selection conditions in the interface. Most of these interfaces return restricted answers (e.g., only top-k of the selected tuples), while many of them also accompany each answer with the COUNT of the selected tuples. In this paper, we propose techniques which leverage the COUNT information to ef?ciently acquire unbiased samples of the hidden database. We also discuss variants for interfaces which do not provide COUNTinformation. We conduct extensive experiments to illustrate the ef?ciency and accuracy of our techniques.

53 citations

Proceedings ArticleDOI
09 Jun 2008
TL;DR: A lightweight data structure for indexing the tuples in the streaming buffer is developed that can gracefully adapt to tuples with many attributes and partially ordered domains of any size and complexity.
Abstract: The problem of skyline computation has attracted considerable research attention. In the categorical domain the problem becomes more complicated, primarily due to the partially-ordered nature of the attributes of tuples.In this paper, we initiate a study of streaming categorical skylines. We identify the limitations of existing work for offline categorical skyline computation and realize novel techniques for the problem of maintaining the skyline of categorical data in a streaming environment. In particular, we develop a lightweight data structure for indexing the tuples in the streaming buffer, that can gracefully adapt to tuples with many attributes and partially ordered domains of any size and complexity. Additionally, our study of the dominance relation in the dual space allows us to utilize geometric arrangements in order to index the categorical skyline and efficiently evaluate dominance queries. Lastly, a thorough experimental study evaluates the efficiency of the proposed techniques.

53 citations

Proceedings ArticleDOI
16 Jan 2017
TL;DR: This work shows completeness of the Sparse Orthogonal Vectors problem for the class of first-order properties under fine-grained reductions, the first such completeness result for a standard complexity class.
Abstract: Properties definable in first-order logic are algorithmically interesting for both theoretical and pragmatic reasons. Many of the most studied algorithmic problems, such as Hitting Set and Orthogonal Vectors, are first-order, and the first-order properties naturally arise as relational database queries. A relatively straightforward algorithm for evaluating a property with k+1 quantifiers takes time O(mk) and, assuming the Strong Exponential Time Hypothesis (SETH), some such properties require O(mk−e) time for any e > 0. (Here, >m represents the size of the input structure, i.e., the number of tuples in all relations.)We give algorithms for every first-order property that improves this upper bound to mk/2Θ (S log n), i.e., an improvement by a factor more than any poly-log, but less than the polynomial required to refute SETH. Moreover, we show that further improvement is equivalent to improving algorithms for sparse instances of the well-studied Orthogonal Vectors problem. Surprisingly, both results are obtained by showing completeness of the Sparse Orthogonal Vectors problem for the class of first-order properties under fine-grained reductions. To obtain improved algorithms, we apply the fast Orthogonal Vectors algorithm of References [3, 16].While fine-grained reductions (reductions that closely preserve the conjectured complexities of problems) have been used to relate the hardness of disparate specific problems both within P and beyond, this is the first such completeness result for a standard complexity class.

53 citations

Journal ArticleDOI
01 Nov 2017
TL;DR: A new algorithm Hydra is proposed, which overcomes the quadratic runtime complexity in the number of tuples of state-of-the-art DC discovery methods and results in a speedup by orders of magnitude, especially for datasets with a large number of Tuples.
Abstract: Denial constraints (DCs) are a generalization of many other integrity constraints (ICs) widely used in databases, such as key constraints, functional dependencies, or order dependencies. Therefore, they can serve as a unified reasoning framework for all of these ICs and express business rules that cannot be expressed by the more restrictive IC types. The process of formulating DCs by hand is difficult, because it requires not only domain expertise but also database knowledge, and due to DCs' inherent complexity, this process is tedious and error-prone. Hence, an automatic DC discovery is highly desirable: we search for all valid denial constraints in a given database instance. However, due to the large search space, the problem of DC discovery is computationally expensive.We propose a new algorithm Hydra, which overcomes the quadratic runtime complexity in the number of tuples of state-of-the-art DC discovery methods. The new algorithm's experimentally determined runtime grows only linearly in the number of tuples. This results in a speedup by orders of magnitude, especially for datasets with a large number of tuples. Hydra can deliver results in a matter of seconds that to date took hours to compute.

53 citations


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