Materialized view selection as constrained evolutionary optimization

Yu, Jeffrey Xu; Yao, Xin; Choi, Chi-Hon; Gou, Gang

doi:10.1109/tsmcc.2003.818494

Cited by 83 publications

(43 citation statements)

References 12 publications

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“…Randomize algorithms such as simulated annealing algorithms [7,8,14] and genetic algorithms [5,13,18,30,16] have been used and explored for the selection of materialized views in order to improve the quality of the solution. These algorithms use a randomized search strategy for deciding which views to materialize.…”

Section: Randomized Algorithms Based Methodsmentioning

confidence: 99%

“…We have implemented our approach and compared it with a randomized method because in previous studies [30,7,8], it was shown that randomized algorithms provide a significant improvement in the performance compared to deterministic algorithms. The most commonly used randomized algorithms in the context of view selection are simulated annealing algorithms and genetic algorithms.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Although, several heuristic algorithms have been proposed in literature to solve the view selection problem such as deterministic algorithms i.e., greedy algorithms [11,29,12,25,20,21,26,2], randomized algorithms i.e., genetic algorithms [31,18,13,30,16,5] and simulated annealing algorithms [14,7,8] or hybrid algorithms [32] which combine the strategies of pure deterministic algorithms and pure randomized algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Modeling View Selection as a Constraint Satisfaction Problem

Mami

Coletta

Bellahsène

2011

Lecture Notes in Computer Science

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Abstract. Using materialized views can highly speed up the query processing time. This paper deals with the view selection issue, which consists in finding a set of views to materialize that minimizes the expected cost of evaluating the query workload, given a limited amount of resource such as total view maintenance cost and/or storage space. However, the solution space is huge since it entails a large number of possible combinations of views. For this matter, we have designed a solution involving constraint programming, which has proven to be a powerful approach for modeling and solving combinatorial problems. The efficiency of our method is evaluated using workloads consisting of queries over the schema of the TPC-H benchmark. We show experimentally that our approach provides an improvement in the solution quality (i.e., the quality of the obtained set of materialized views) in term of cost saving compared to genetic algorithm in limited time. Furthermore, our approach scales well with the query workload size.

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Section: Randomized Algorithms Based Methodsmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling View Selection as a Constraint Satisfaction Problem

Mami

Coletta

Bellahsène

2011

Lecture Notes in Computer Science

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“…It is well-known that evolutionary algorithms (EAs) are powerful techniques for solving various kinds of optimization problems in real-world applications [1,2]. Traditionally, researchers have been concentrating their attentions on EAs applied to stationary optimization problems, where problems are given in advance and maintain fixed during the evolutionary process.…”

Section: Introductionmentioning

confidence: 99%

Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization

Tang

Chen

et al. 2008

Memetic Comp.

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In recent years, there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs). Among approaches developed for EAs to deal with DOPs, immigrants schemes have been proven to be beneficial. Immigrants schemes for EAs on DOPs aim at maintaining the diversity of the population throughout the run via introducing new individuals into the current population. In this paper, we carefully examine the mechanism of generating immigrants, which is the most important issue among immigrants schemes for EAs in dynamic environments. We divide existing immigrants schemes into two types, namely the direct immigrants scheme and the indirect immigrants scheme, according to the way in which immigrants are generated. Then experiments are conducted to understand the difference in the behaviors of different types of immigrants schemes and to compare their performance in dynamic environments. Furthermore, a new immigrants scheme is proposed to combine the merits of two types of immigrants schemes. The experimental results show that the interactions between the two types of schemes

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“…fully used EAs in solving various kinds of problems in many fields [3], [21], [23], [36], [39]. Many scholars also concerned the related theoretical study of EAs.…”

mentioning

confidence: 99%

A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems

Chen

Sun

et al. 2009

IEEE Trans. Syst., Man, Cybern. B

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Abstract-In the past decades, many theoretical results related to the time complexity of evolutionary algorithms (EAs) on different problems are obtained. However, there is not any general and easy-to-apply approach designed particularly for populationbased EAs on unimodal problems. In this paper, we first generalize the concept of the takeover time to EAs with mutation, then we utilize the generalized takeover time to obtain the mean first hitting time of EAs and, thus, propose a general approach for analyzing EAs on unimodal problems. As examples, we consider the so-called (N + N ) EAs and we show that, on two well-known unimodal problems, LEADINGONES and ONEMAX, the EAs with the bitwise mutation and two commonly used selection schemes both need O(n ln n + n 2 /N ) and O(n ln ln n + n ln n/N ) generations to find the global optimum, respectively. Except for the new results above, our approach can also be applied directly for obtaining results for some population-based EAs on some other unimodal problems. Moreover, we also discuss when the general approach is valid to provide us tight bounds of the mean first hitting times and when our approach should be combined with problem-specific knowledge to get the tight bounds. It is the first time a general idea for analyzing population-based EAs on unimodal problems is discussed theoretically.

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Materialized view selection as constrained evolutionary optimization

Cited by 83 publications

References 12 publications

Modeling View Selection as a Constraint Satisfaction Problem

Modeling View Selection as a Constraint Satisfaction Problem

Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization

A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems

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