• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.325-338
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• 2004

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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.573-580
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• 2018

Various studies have been conducted to analyze the location appropriateness and capacity of shelters. However, research on how to select new shelters is relatively insufficient. Since the shelter is designated in case of emergency, it is also necessary to efficiently select the location of the shelter. Therefore, this study presented a method for selecting the location of the shelter using network analysis that has been used to analyze the location appropriateness of shelters and genetic algorithm which is a representative heuristic algorithm. First, the network analysis using the existing civil defense evacuation facility data was performed and the result showed that the vulnerability of evacuation has a high deviation by region in the study area. In order to minimize the evacuation vulnerable area, the genetic algorithm was designed then the location of new shelters was determined. The initial solution consisting of candidate locations of new shelters was randomly generated and the optimal solution was found through the process of selection, crossover, and mutation. As a result of the experiment, the area with a high percentage of the evacuation vulnerable areas was prioritized and the effectiveness of the proposed method could be confirmed. The results of this study is expected to contribute to the positioning of new shelters and the establishment of an efficient evacuation plan in the future.