Dynamic View Management System for Query Prediction to View Materialization

Daneshpour, Negin; Barfourosh, Ahmad Abdollahzadeh

doi:10.4018/jdwm.2011040104

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…An important challenge is to adapt our approach for maintaining materialized views, considering the connection between multi-query optimization and the maintenance view problem. Finally, we are evaluating the impact of the query changes on our proposal (Daneshpour & Barforoush, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Views Selection: There exist a great number of selection algorithms, taking account or not of constraints such as space and maintenance cost. For a complete classification, we refer to the survey paper of Mami et al (Mami & Bellahsene, 2012) which divides algorithms in the following categories: deterministic algorithms (Shukla & Deshpande & Naughton, 1998;Souza & Sampaio;1999;Theodoratos & Sellis 1997), randomized algorithms (Ioannidis & Kang, 1990), evolutionary algorithms (Horng & Chang & Liu & Kao, 1999) (Phan & Li, 2008), or with an unknown workload (Daneshpour & Barforoush, 2011;Lawrence & Rau-chaplin, 2008). 4.…”

Section: Related Workmentioning

confidence: 99%

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Coupling Materialized View Selection to Multi Query Optimization

Boukorca

Bellatreche

Senouci³

et al. 2015

International Journal of Data Warehousing and Mining

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Materialized views are queries whose results are stored and maintained in order to facilitate access to data in their underlying base tables of extremely large databases. Selecting the best materialized views for a given query workload is a hard problem. Studies on view selection have considered sharing common sub expressions and other multi-query optimization techniques. Multi-Query Optimization is a well-studied domain in traditional and advanced databases. It aims at optimizing a workload of queries by finding and reusing common sub-expression between queries. Finding the best shared expression is known as a NP-hard problem. The shared expressions usually identified by graph structure have been used to be candidate for materialized views. This shows the strong interdependency between the problems of materialized view selection (PVS) and multi query optimization (PMQO), since the PVS uses the graph structure of the PMQO. Exploring the existing works on PVS considering the interaction between PVS and PMQO figures two main categories of studies: (i) those considering the PMQO as a black box where the output is the graph and (ii) those preparing the graph to guide the materialized view selection process. In this category, the graph generation is based on individual query plans, an approach that does not scale, especially with the explosion of Big Data applications requiring large number of complex queries with high interaction. To ensure a scalable solution, this work proposes a new technique to generate a global processing plan without using individual plans by borrowing techniques used in the electronic design automation (EDA) domain. This paper first presents a rich state of art regarding the PVS and a classification of the most important existing work. Secondly, an analogy between the MQO problem and the EDA domain, in which large circuits are manipulated, is established. Thirdly, it proposes to model the problem with hypergraphs which are massively used to design and test integrated circuits. Fourthly, it proposes a deterministic algorithm to select materialized views using the global processing plan. Finally, experiments are conducted to show the scalability of our approach.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Coupling Materialized View Selection to Multi Query Optimization

Boukorca

Bellatreche

Senouci³

et al. 2015

International Journal of Data Warehousing and Mining

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show abstract

“…The algorithm involves many factors but it doesnt consider some factors R. SABBAGH AND N. DANESHPOUR 503 like the cost of dropping the materialized view. One of the view selection algorithms predicts the next query and materializes related views according to the prediction [23]. But always the prediction isnt confident.…”

Section: Related Workmentioning

confidence: 99%

Improved View Selection Algorithm Using SOM and 0/1 Knapsack

Gol

Daneshpour

2019

Stat., optim. inf. comput.

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Data warehouse is designed for answering analytical queries. Data warehouse saves historical data. In the data warehouse, the response time to analytical queries is long. So reducing the response time is a critical problem. There are a lot of algorithms to solve the problem. Some of them, materialize frequent views. The previously posed queries have important information that will be used in the future. This paper proposes an algorithm for view materialization. The proposed algorithm finds proper views using previous queries and materializes them. The views are able to answer future queries. The view selection algorithm has four steps. At first, it clusters previous queries by SOM method. Then frequent queries are found by Apriori algorithm. In the third step the problem is converted to 0/1 knapsack equations and finally, optimal queries are joined to create only one view for each cluster. This paper improves the first and third step. This paper uses the SOM algorithm for clustering previous queries in the first step and it solves the 0/1 knapsack equations according to shuffled frog leaping algorithm in the third step. Experimental results show that it improves the previous view selection algorithms according to response time and storage space factor.

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An Adaptive Query Optimization in a Hierarchical Mediator System

Park

Joo

2011

Communication and Networking

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Dynamic View Management System for Query Prediction to View Materialization

Cited by 4 publications

References 53 publications

Coupling Materialized View Selection to Multi Query Optimization

Coupling Materialized View Selection to Multi Query Optimization

Improved View Selection Algorithm Using SOM and 0/1 Knapsack

An Adaptive Query Optimization in a Hierarchical Mediator System

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