• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.1-14
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• 2012

As the size of the data warehouse is large, the selection of indices on the data warehouse affects the efficiency of the query processing of the data warehouse. Indices induce the lower query processing cost, but they occupy the large storage areas and induce the index maintenance cost which are accompanied by database updates. The bitmap join indices are well applied when we optimize the star join queries which join a fact table and many dimension tables and the selection on dimension tables in data warehouses. Though the bitmap join indices with the binary representations induce the lower storage cost, the task to select the indexing attributes among the huge candidate attributes which are generated is difficult. The processes of index selection are to reduce the number of candidate attributes to be indexed and then select the indexing attributes. In this paper on bitmap join index selection problem we reduce the number of candidate attributes by the data mining techniques. Compared to the existing techniques which reduce the number of candidate attributes by the frequencies of attributes we consider the frequencies of attributes and the size of dimension tables and the size of the tuples of the dimension tables and the page size of disk. We use the mining of the frequent itemsets as mining techniques and reduce the great number of candidate attributes. We make the bitmap join indices which have the least costs and the least storage area adapted to storage constraints by using the cost functions applied to the bitmap join indices of the candidate attributes. We compare the existing techniques and ours and analyze them in order to evaluate the efficiencies of ours.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.22-24
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• 2002

XML이 웹상에서 데이타 교환의 표준으로 채택되면서 XML 데이타를 관계형 데이타베이스를 이용하여 저장하고 처리하는 것이 많이 연구되고 있다. 본 연구에서는 관계형 데이타베이스에 저장되어 있는 XML 데이타의 효과적인 질의 처리에 사용할 수 있는 인덱스 구조로서 디렉토리 인덱스를 제안한다. 디렉토리 인덱스는 정규 경로식 처리에 있어서 비트맵을 이용하여 조인 연산을 크게 줄여 처리 시간이 빠르며 인덱스의 갱신에도 효과적으로 대처할 수 있다.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.146-164
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• 2010

Due to an increasing volume of XML data, it is considered prudent to store XML data on an industry-strength database system instead of relying on a domain specific application or a file system. For shredded XML data stored in relational tables, however, it may not be straightforward to apply existing algorithms for twig query processing, since most of the algorithms require XML data to be accessed in a form of streams of elements grouped by their tags and sorted in a particular order. In order to support XML query processing within the common framework of relational database systems, we first propose several bitmap indexes and their strategies for supporting holistic twig joining on XML data stored in relational tables. Since bitmap indexes are well supported in most of the commercial and open-source database systems, the proposed bitmapped indexes and twig query processing strategies can be incorporated into relational query processing framework with more ease. The proposed query processing strategies are efficient in terms of both time and space, because the compressed bitmap indexes stay compressed during data access. In addition, we propose a hybrid index which computes twig query solutions with only bit-vectors, without accessing labeled XML elements stored in the relational tables.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.789-791
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• 2016

수동 금속 연마과정의 효율성을 향상시키기 위한 자동 시편 연마 시스템들이 개발되었으나 기존 시스템들은 시스템 내부 데이터 처리를 비-트리 인덱스를 이용하기 때문에 데이터 엑세스 효율성이 떨어지며 다수의 기기가 접속할 경우 저장 공간의 낭비가 발생한다는 문제점이 있다. 따라서 본 논문에서는 조인 결과에 대한 인덱스를 생성하고 컬럼 값 분포도가 낮은 데이터에 대한 액세스 효율성이 높은 비트캡 인덱스를 비트리 구조에 삽입하여 시편 연마기 데이터를 제공하는 시스템을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.121-123
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• 2001

대용량의 데이터가 저장되는 데이터 웨어하우징 환경에서는 조인이나 집계 함수와 같은 고비용의 연산의 효율적인 처리는 매우 중요하다. 본 논문에서는 집계 함수(aggregate function)와 조인이 모두 포함된 질의를 처리하는 새로운 기법을 제안한다. 제안하는 기법은 먼저 차원 테이블(dimension table)을 미리 그룹핑한 후, 비트맵 조인 인덱스(bitmap join index)를 이용하여 조인을 처리하는 방식을 사용한다. 이 결과, 사실 테이블만을 접근하여 집계 함수를 처리함으로써 기존 기법이 가지는 성능 저하의 문제점을 해결할 수 있다. 기존 기법과 제안하는 기법에 대한 비용 모델(cost model)을 정립하고, 이를 기반으로 시뮬레이션을 수행함으로써 제안된 기법의 우수성을 규명한다.

• Journal of KIISE
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• v.43 no.6
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• pp.700-709
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• 2016

Recently, with the rapid growth of LOD (Linked Open Data) existing methods based on a single machine have limitation in performance. Existing solutions use distributed framework such as Mapreduce in order to improve the performance. However, the MapReduce framework for processing SPARQL queries involves multiple MapReduce jobs and additional costs incurred. In addition, the problem of unnecessary data processing arises. In this study, we proposed a method to reduce the number of MapReduce jobs during SPARQL query processing and join indexes based on Bitmap for minimizing the costs of processing unnecessary data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.5
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• pp.1-10
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• 2002

Efficient processing of expensive queries that include joins and/or aggregate functions is crucial in data warehousing environment since there reside enormous volume of data. In this paper, we propose a new method for processing of queries that have both of joins and aggregate functions. The proposed method first performs grouping of the dimension table and then processes join by using the bitmap join index. This makes only the fact table accessed for processing aggregate functions, and thus resolves the serious performance degradation of the existing method. For showing the superiority of the proposed method, we suggest the cost models for the proposed and existing ones, and perform extensive simulations based on the TPC-H benchmark.