An Algorithm for Handling Many Relational Calculus Queries Efficiently

Willard, Dan E.

doi:10.1006/jcss.2002.1848

Cited by 18 publications

(14 citation statements)

References 61 publications

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“…Suffice it to say that the idea of representing relations (tables) using lazy unions and products during query evaluation does not appear to have been investigated systematically as it clashes with the classical System R-based database system architecture where all tables, also intermediate ones, are represented as record streams. A thorough comparison with theoretical results by Goyal and Paige (1997); Willard (1996Willard ( , 2002 for query sublanguages guaranteeing worst-case linear or quasi-linear running time (data complexity) remains to be done. Off-hand it appears that their techniques are complementary to ours and thus potentially combinable with the symbolic unions and products used here.…”

Section: Query Evaluation and Optimizationmentioning

confidence: 98%

Generic multiset programming for language-integrated querying

Henglein

Larsen

2010

Proceedings of the 6th ACM SIGPLAN Workshop on Generic Programming

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This paper demonstrates how relational algebraic programming based on efficient symbolic representations of multisets and operations on them can be applied to the query sublanguage of SQL in a type-safe fashion. In essence, it provides a library for naïve programming with multisets in a generalized SQL-style fashion, but avoids many cases of asymptotically inefficient nested iteration through cross-products.

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Section: Query Evaluation and Optimizationmentioning

confidence: 98%

Generic multiset programming for language-integrated querying

Henglein

Larsen

2010

Proceedings of the 6th ACM SIGPLAN Workshop on Generic Programming

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“…The practical design and engineering space for discrimination-based techniques appears to be substantial, but has yet to be explored. The powerful, but orthogonal looking transformational methods underlying Willard's RCS Willard (1990Willard ( , 2002; Goyal and Paige (1997) look like a promising extension.…”

Section: Discussionmentioning

confidence: 99%

“…An extension of our binary joins to joins over multiple tables with join conditions such as acct.num = cust.num = dep.num is straightforward. For socalled tabular queries involving multiple tables more powerful theoretical techniques (Willard 1990;Goyal and Paige 1997;Willard 2002) for join query evaluation are required, however.…”

Section: Selectionmentioning

confidence: 99%

Optimizing relational algebra operations using generic equivalence discriminators and lazy products

Henglein

2010

Proceedings of the 2010 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation

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We show how to efficiently evaluate generic map-filter-product queries, generalizations of select-project-join (SPJ) queries in relational algebra, based on a combination of two novel techniques: generic discrimination-based joins and lazy (formal) products.Discrimination-based joins are based on the notion of (equivalence) discriminator. A discriminator partitions a list of values according to a user-specified equivalence relation on keys the values are associated with. Equivalence relations can be specified in an expressive embedded language for denoting equivalence relations. We show that discriminators can be constructed generically (by structural recursion on equivalence expressions), purely functionally, and efficiently (worst-case linear time). The array-based basic multiset discrimination algorithm of Cai and Paige (1995) provides a base discriminator that is both asymptotically and practically efficient. In contrast to hashing, discrimination is fully abstract (only depends on which equivalences hold on its inputs), and in contrast to comparison-based sorting, it does not require an ordering relation on its inputs. In particular, it is applicable to references (pointers). Furthermore, it has better asymptotic computational complexity than both sorting and hashing.We represent cross-products and unions lazily (symbolically) as formal products of the argument sets (relations). This allows the selection operation to recognize on the fly whenever it is applied to a cross-product and invoke an efficient equijoin implementation. In particular, queries can still be formulated naively, using filter, map and product without an explicit join operation, yet garner the advantages of efficient join-algorithms during evaluation.The techniques subsume many of the optimization techniques based on relational algebra equalities, without need for a query preprocessing phase. They require no indexes and behave purely functionally. They can be considered a form of symbolic execution of set expressions that automate and encapsulate dynamic program transformation of such expressions and lead to asymptotic performance improvements over naive execution in many cases.

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“…Efficient implementation techniques for join queries and extensions have been studied in a large literature, e.g., [Ioa96]. Some methods also provide precise complexity guarantees, e.g., [Wil02,LBSL16].…”

Section: Implementation Of Join Queriesmentioning

confidence: 99%

Logic programming applications: what are the abstractions and implementations?

Liu¹

2018

Declarative Logic Programming: Theory, Systems, and Applications

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This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations.the introduction of non-declarative features in logic languages and resulted in the writing of obscure logic programs.Despite the challenges, the most exciting aspect of logic programming is its vast areas of applications. They range from database queries to program analysis, from text processing to decision making, from security to knowledge engineering, and more. These vast, complex, and interrelated areas make it challenging but necessary to provide a deeper understanding of the various kinds of applications in order to help advance the state of the art of logic programming and realize its benefits.This article presents an overview of applications of logic programming based on a study of the abstractions and implementations of logic languages. The rationale is that abstractions and implementations are the enabling technologies of the applications. The abstractions are essential for determining what kinds of application problems can be expressed and how they can be expressed, for ease of understanding, reuse, and maintenance. The underlying implementations are essential for high-level declarative languages to be sufficiently efficient for substantial applications.We discuss the following essential abstractions, where data abstractions are for expressing the data, and control abstractions are for expressing computations over the data:1. data abstractions: objects and relationships; 2. control abstractions: (1) join, (2) recursion, and (3) constraint, which capture bounded, cyclic, and general computations, respectively. Logic language abstractionsLogic languages provide very high-level data and control abstractions, using mostly very simple language constructs. We describe these abstractions and their meanings intuitively. Data abstractionsAll data in logic languages are abstracted, essentially, as objects and relationships.

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An Algorithm for Handling Many Relational Calculus Queries Efficiently

Cited by 18 publications

References 61 publications

Generic multiset programming for language-integrated querying

Generic multiset programming for language-integrated querying

Optimizing relational algebra operations using generic equivalence discriminators and lazy products

Logic programming applications: what are the abstractions and implementations?

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