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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
06 Aug 1991
TL;DR: In this article, an expanded virtual register (EVR) data structure is provided comprising an infinite, linearly ordered set of virtual register elements with a remap() function defined upon the EVR.
Abstract: A process for optimizing compiler intermediate representation (IR) code, and data structures for implementing the process; the process is preferably embodied in a compiler computer program operating on an electronic computer or data processor with access to a memory storage means such as a random access memory and access to a program mass storage means such as an electronic magnetic disk storage device. The compiler program reads an input source program stored in the program mass storage means and creates a dynamic single assignment intermediate representation of the source program in the memory using pseudo-machine instructions. To create the dynamic single assignment intermediate representation, during compilation, the compiler creates a plurality of virtual registers in the memory for storage of variables defined in the source program. Means are provided to ensure that the same virtual register is never assigned to more than once on any dynamic execution path. An expanded virtual register (EVR) data structure is provided comprising an infinite, linearly ordered set of virtual register elements with a remap() function defined upon the EVR. Calling the remap() function with an EVR parameter causes an EVR element which was accessible as [n] prior to the remap operation to be accessible as [n+1] after the remap operation. A subscripted reference map comprising a dynamic plurality of map tuples is used. Each map tuple associates the real memory location accessible under a textual name with an EVR element. A compiler can use the map tuple to substitute EVR elements for textual names, eliminating unnecessary load operations from the output intermediate representation.

79 citations

Posted Content
TL;DR: In this paper, the authors define the semantics of clean query answering in terms of certain/possible answers as the greatest lower bound/least upper bound of all possible answers obtained from the clean instances.
Abstract: Matching dependencies were recently introduced as declarative rules for data cleaning and entity resolution. Enforcing a matching dependency on a database instance identifies the values of some attributes for two tuples, provided that the values of some other attributes are sufficiently similar. Assuming the existence of matching functions for making two attributes values equal, we formally introduce the process of cleaning an instance using matching dependencies, as a chase-like procedure. We show that matching functions naturally introduce a lattice structure on attribute domains, and a partial order of semantic domination between instances. Using the latter, we define the semantics of clean query answering in terms of certain/possible answers as the greatest lower bound/least upper bound of all possible answers obtained from the clean instances. We show that clean query answering is intractable in some cases. Then we study queries that behave monotonically wrt semantic domination order, and show that we can provide an under/over approximation for clean answers to monotone queries. Moreover, non-monotone positive queries can be relaxed into monotone queries.

79 citations

Proceedings ArticleDOI
01 Jun 1987
TL;DR: This paper shows that the problem of determining whether a query is safe when terms involving function symbols are represented as distinct occurrences of uninterpreted infinite predicates over which certain finiteness dependencies hold.
Abstract: A database query is said to be safe if its result consists of a finite set of tuples If a query is expressed using a set of pure Horn Clauses, the problem of determining whether it is safe is in general undecidable In this paper, we show that the problem is decidable when terms involving function symbols (including arithmetic) are represented as distinct occurrences of uninterpreted infinite predicates over which certain finiteness dependencies hold. We present a sufficient condition for safety when some monotonicity constraints also hold.

79 citations

Journal ArticleDOI
01 Oct 2009
TL;DR: It is shown that minimization is intractable in general and study the more restricted problem of maintaining minimality incrementally when performing operations, and several results on the problem of approximating uncertain data in an insufficiently expressive model are presented.
Abstract: In general terms, an uncertain relation encodes a set of possible certain relations. There are many ways to represent uncertainty, ranging from alternative values for attributes to rich constraint languages. Among the possible models for uncertain data, there is a tension between simple and intuitive models, which tend to be incomplete, and complete models, which tend to be nonintuitive and more complex than necessary for many applications. We present a space of models for representing uncertain data based on a variety of uncertainty constructs and tuple-existence constraints. We explore a number of properties and results for these models. We study completeness of the models, as well as closure under relational operations, and we give results relating closure and completeness. We then examine whether different models guarantee unique representations of uncertain data, and for those models that do not, we provide complexity results and algorithms for testing equivalence of representations. The next problem we consider is that of minimizing the size of representation of models, showing that minimizing the number of tuples also minimizes the size of constraints. We show that minimization is intractable in general and study the more restricted problem of maintaining minimality incrementally when performing operations. Finally, we present several results on the problem of approximating uncertain data in an insufficiently expressive model.

79 citations

Proceedings Article
30 Aug 2005
TL;DR: This work proposes a new query processing technique called content-based routing (CBR) that eliminates the single-plan restriction in current systems, and presents low-overhead adaptive algorithms that partition input data based on statistical properties relevant to query execution strategies, and efficiently route individual tuples through customized plans based on their partition.
Abstract: Query optimizers in current database systems are designed to pick a single efficient plan for a given query based on current statistical properties of the data. However, different subsets of the data can sometimes have very different statistical properties. In such scenarios it can be more efficient to process different subsets of the data for a query using different plans. We propose a new query processing technique called content-based routing (CBR) that eliminates the single-plan restriction in current systems. We present low-overhead adaptive algorithms that partition input data based on statistical properties relevant to query execution strategies, and efficiently route individual tuples through customized plans based on their partition. We have implemented CBR as an extension to the Eddies query processor in the TelegraphCQ system, and we present an extensive experimental evaluation showing the significant performance benefits of CBR.

79 citations


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