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테이블 유사도 분석을 통한 테이블 통합 기법

A Table Integration Technique Using Query Similarity Analysis

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주제분류컴퓨터학

초록

In this paper, we propose a technique to analyze similarity between SQL queries and to assist integrating similar tables. First, the table information was extracted from the SQL queries through the query structure analyzer, and the similarity between the tables was measured using the Jacquard index technique. Then, similar table clusters are generated through hierarchical cluster analysis method and the co-occurence probability of the table used in the query is calculated. The possibility of integrating similar tables is classified by using the possibility of co-occurence of similarity table and table, and classifying them into an integrable cluster, a cluster requiring expert review, and a cluster with low integration possibility. This technique analyzes the SQL query in practice and analyse the possibility of table integration independent of the existing business, so that the existing schema can be effectively reconstructed without interruption of work or additional cost.

Abstract

In this paper, we propose a technique to analyze similarity between SQL queries and to assist integrating similar tables. First, the table information was extracted from the SQL queries through the query structure analyzer, and the similarity between the tables was measured using the Jacquard index technique. Then, similar table clusters are generated through hierarchical cluster analysis method and the co-occurence probability of the table used in the query is calculated. The possibility of integrating similar tables is classified by using the possibility of co-occurence of similarity table and table, and classifying them into an integrable cluster, a cluster requiring expert review, and a cluster with low integration possibility. This technique analyzes the SQL query in practice and analyse the possibility of table integration independent of the existing business, so that the existing schema can be effectively reconstructed without interruption of work or additional cost.

참고문헌 (14)

  1. Jong Suk Lee, "Modeling on Data Performance for Very Large Database", Proceedings of the Korea Safety Management & Science, 1, pp.383~391 (2012)
  2. Kang Soo Seo, "The Guide for Data Architecture Professional", Korea Data Agency, 214~553 (2013)
  3. R. Y. Wang, "A Framework for Analysis of Data Quality Research", Transactions on Knowledge and Data Engineering, 7(4), pp.623~640 (1995)
  4. 이혜경, "데이터베이스 성능향상용 역정규화의 무용성", 전자공학회논문지 - CI, 42(3), pp.145~152 (2005), 대한전자공학회
  5. 서혜영, "글로벌 제조기업의 Master 데이터 통합 관리 시스템 구축 사례 연구", Entrue Journal of Information Technology, 7(2), pp.91~102 (2008), 엘지씨엔에스
  6. S. Castano, "Conceptual Schema Analysis : Techniques and Applications", ACM Transactions on Database Systems, 23(3), pp.286~333 (1998)
  7. H. Köpcke, "Frameworks for entity matching : A comparison", Data & Knowledge Engineering, 69(2), pp.197~210 (2010)
  8. 오세웅, "XML을 이용한 데이터베이스 통합방안에 관한 연구", 한국항해항만학회지, 29(9), pp.883~890 (2005), 한국항해항만학회
  9. Sanjay Madria, "An XML Schema Integration and Query Mechanism System", Data & Knowledge Engineering, 65(2), pp.265~303 (2008)
  10. D. P. Groth, "Visual Representation of Database Queries using Structual similarity", Information Visualization, pp.102~107 (2003)
  11. Yun Hee Han, "Design and Implementation of Database Cache engine based on Similarity Query Matching", Master’s, pp.2008
  12. P. J. Rousseeuw, "Silhouette : a graphical aid to the interpretation and validation of cluster analysis", Journal of Computational and Applied Mathematics, 20, pp.53~65 (1987)
  13. 이수정, "협력필터링의 데이터 희소성 해결을 위한 자카드 지수 반영의 유사도 성능 분석", 컴퓨터교육학회 논문지, 19(4), pp.59~66 (2016), 한국컴퓨터교육학회[DOI]
  14. L. Kaufman, "Finding Groups in Data: An Introduction to Cluster Analysis" (1990), Wiley

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