정보처리학회논문지D (The KIPS Transactions:PartD)

한국정보처리학회 (Korea Information Processing Society)
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데이터웨어하우스에서 유전자 알고리즘을 이용한 구체화된 뷰 선택 기법

A Genetic Algorithm for Materialized View Selection in Data Warehouses

  • 이민수 (이화여자대학교 컴퓨터학과)
  • 발행 : 2004.04.01
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초록

데이터 웨어하우스는 복잡한 질의 및 분석을 위해서 다양한 종류의 여러 정보 출처들로부터 정보를 모아서 저장한다. 일반적으로 웨어하우스에는 자주 실행되는 질의들을 미리 계산해서 구체화된 뷰의 형태로 저장한다. 웨어하우스를 설계할 때 가장 중요한 일들 중의 하나는 웨어하우스에서 유지될 구체화된 뷰의 선택이다. 이것은 뷰들의 유지를 위해 제한된 시간이 주어졌을 때, 모든 질의들에 대한 총 질의 응답 시간을 최소화하는 방법으로 일련의 뷰들을 선택하는 것이다(유지-비용 뷰 선택 문제). 본 논문에서는 최적에 가까운 일련의 뷰들을 계산하기 위해 유전자 알고리즘을 사용하여 유지-비용 뷰 선택 문제에 대한 효율적인 해결책을 제안한다. 특히 OR 뷰 그래프들의 관점에서의 유지-비용 뷰 선택 문제를 다룬다. 본 논문의 접근방식은 휴리스틱 방법을 사용한 기존의 탐색-기반 접근 방식들에 비해서, 시간 복잡도에서 큰 향상을 보여준다. 본 논문의 알고리즘은 최적의 질의 비용에 비해 10%이내의 추가비용만을 갖는 해결책을 제시하면서도 실행시간 측면에서는 매우 향상된 선형 증가만을 보인다. 본 논문의 알고리즘에 대한 프로토타입을 구현하였으며 이것을 사용하여 논문에서 제안하는 접근방식의 분석을 수행하였다.

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

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참고문헌

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