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Integrating Mining with Relational Database Systems: Alternatives and Implications.

Sunita Sarawagi, Shiby Thomas, Rakesh Agrawal: Integrating Mining with Relational Database Systems: Alternatives and Implications. SIGMOD Conference 1998: 343-354
@inproceedings{DBLP:conf/sigmod/SarawagiTA98,
  author    = {Sunita Sarawagi and
               Shiby Thomas and
               Rakesh Agrawal},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Integrating Mining with Relational Database Systems: Alternatives
               and Implications},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {343-354},
  ee        = {http://doi.acm.org/10.1145/276304.276335, db/conf/sigmod/SarawagiTA98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Data mining on large data warehouses is becoming increasingly important. In support of this trend, we consider a spectrum of architectural alternatives for coupling mining with database systems. These alternatives include: loose-coupling through a SQL cursor interface; encapsulation of a mining algorithm in a stored procedure; caching the data to a file system on-the-fly and mining; tight-coupling using primarily user-defined functions; and SQL implementations for processing in the DBMS. We comprehensively study the option of expressing the mining algorithm in the form of SQL queries using Association rule mining as a case in point. We consider four options in SQL-92 and six options in SQL enhanced with object-relational extensions (SQL-OR). Our evaluation of the different architectural alternatives shows that from a performance perspective, the Cache-Mine option is superior, although the performance of the SQL-OR option is within a factor of two. Both the Cache-Mine and the SQL-OR approaches incur a higher storage penalty than the loose-coupling approach which performance-wise is a factor of 3 to 4 worse than Cache-Mine. The SQL-92 implementations were too slow to qualify as a competitive option. We also compare these alternatives on the basis of qualitative factors like automatic parallelization, development ease, portability and inter-operability.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
Contents

Online Edition: ACM SIGMOD

[Abstract]

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Referenced by

  1. Surajit Chaudhuri: Review - Integrating Mining with Relational Database Systems: Alternatives and Implications. ACM SIGMOD Digital Review 2: (2000)
  2. Haixun Wang, Carlo Zaniolo: Using SQL to Build New Aggregates and Extenders for Object- Relational Systems. VLDB 2000: 166-175
  3. Theodore Johnson, Laks V. S. Lakshmanan, Raymond T. Ng: The 3W Model and Algebra for Unified Data Mining. VLDB 2000: 21-32
  4. Jiawei Han, Jian Pei, Yiwen Yin: Mining Frequent Patterns without Candidate Generation. SIGMOD Conference 2000: 1-12
  5. Damianos Chatziantoniou: Evaluation of Ad Hoc OLAP: In-Place Computation. SSDBM 1999: 34-43
  6. Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, Teresa Mah: Exploratory Mining via Constrained Frequent Set Queries. SIGMOD Conference 1999: 556-558
  7. Laks V. S. Lakshmanan, Raymond T. Ng, Jiawei Han, Alex Pang: Optimization of Constrained Frequent Set Queries with 2-variable Constraints. SIGMOD Conference 1999: 157-168
  8. Marek Wojciechowski: Mining Various Patterns in Sequential Data in an SQL-like Manner. ADBIS (Short Papers) 1999: 131-138
  9. Surajit Chaudhuri: Data Mining and Database Systems: Where is the Intersection? IEEE Data Eng. Bull. 21(1): 4-8(1998)
  10. Tomasz Imielinski, Aashu Virmani: Association Rules... and What's Next? Towards Second Generation Data Mining Systems. ADBIS 1998: 6-25
BibTeX
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