The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Efficient Storage Structures for a Stock Investment Recommendation System

주식 투자 추천 시스템을 위한 효율적인 저장 구조

  • 하유민 (연세대학교 컴퓨터과학) ;
  • 김상욱 (한양대학교 정보통신학부) ;
  • 박상현 (연세대학교 컴퓨터과학과) ;
  • 임승환 (한양대학교 전자통신컴퓨터공학과)
  • Published : 2009.04.30
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Abstract

Rule discovery is an operation that discovers patterns frequently occurring in a given database. Rule discovery makes it possible to find useful rules from a stock database, thereby recommending buying or selling times to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investments. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure improves the query performance of the previous one up to about 170 times.

규칙 탐사는 주어진 데이터베이스로부터 빈번하게 발생하는 패턴들을 발견하는 연산이다. 규칙 탐사 연산을 이용하여 주식 데이터베이스로부터 유용한 규칙들을 발견하고 이를 토대로 주식 투자자들에게 주식의 매매를 적절한 시점에 추천할 수 있다. 본 논문에서는 이러한 주식 투자 시스템에서 질의를 효율적으로 처리하기 위한 저장 구조에 관하여 논의한다. 먼저, 주식 투자 추천을 지원하기 위한 다섯 가지 저장 구조들을 제안하고, 각 구조들의 특징과 장단점을 비교한다. 또한, 실제 주가 데이터를 이용한 실험을 통하여 제안된 저장 구조들의 성능을 검증한다. 실험 결과에 의하면, 히스토그램을 이용한 저장 구조의 경우, 기존의 기법에 비하여 질의 처리 성능이 약 170배 개선되는 것으로 나타났다.

Keywords

References

  1. R. Agrawal, C. Faloutsos, and A. Swami, 'Efficient Similarity Search in Sequence Databases,' In Proc. Int'l. Conf. on Foundations of Data Organization and Algorithms, FODO, pp. 69-84, Oct., 1993 https://doi.org/10.1007/3-540-57301-1_5
  2. S. W. Kim, S. H. Park, and W. W. Chu, 'An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases,' In Proc. Int'l. Conf. on Data Engineering, IEEE, pp.607-614, 2001 https://doi.org/10.1109/ICDE.2001.914875
  3. W. K. Loh, S. W. Kim, and K. Y. Whang, 'A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases,' Data Mining and Knowledge Discovery Journal, Vol.9, No.1, pp.5-28, July, 2004 https://doi.org/10.1023/B:DAMI.0000026902.89522.a3
  4. S. H. Park et al., 'Efficient Searches for Similar Subsequences of Difference Lengths in Sequence Databases,' In Proc. Int'l. Conf. on Data Engineering, IEEE ICDE, pp.23-32, 2000 https://doi.org/10.1109/ICDE.2000.839384
  5. P. Bloomfield, Fourier Analysis of Time Series, Wiley, 2000
  6. You-min Ha, Sanhyun Park, Sang-Wook Kim, Jung-Im Won, and Jee-Hee Yoon, 'Rule Discovery and Matching in Stock Databases,' 32nd Annual IEEE International Computer Software and Applications Conference(COMPSAC 2008), pp.192-198, 2008 https://doi.org/10.1109/COMPSAC.2008.20
  7. R. Agrawal and R. Srikant, 'Fast Algorithms for Mining Association Rules,' In Proc. Int'l. Conf. on Very Large Data Bases, VLDB, pp.487-499, 1994
  8. R. Agrawal and R. Srikant, 'Mining Sequential Patterns,' In Proc. Int'l. Conf. on Data Engineering, IEEE ICDE, pp.3-14, 1995 https://doi.org/10.1109/ICDE.1995.380415
  9. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, 'Fast Subsequence Matching in Time-series Databases,' In Proc. Int'l. Conf. on Management of Data, ACM SIGMOD, pp.419-429, May, 1994 https://doi.org/10.1145/191843.191925
  10. T. Anderson, 'The Statistical Analysis of Time Series,' Wiley, 1971
  11. G. Das, K.-I. Lin, H. Mannila, Gopal Renganathan, and Padhraic Smyth, 'Rule Discovery from Time Series,' In Proc. Int'l. Conf. on Knowledge Discovery and Datamining, pp.16-22, 1998
  12. S. Park and W. W. Chu, 'Discovering and Matching Elastic Rules From Sequence Databases,' in Fundamenta Informaticae, Vol.47, No.1-2, pp.75-90, Aug-Sept, 2001
  13. Koscom Data Mall, http://datamall.koscom.co.kr, 2005