Algorithms and applications for universal quantification in relational databases

Information Systems

Volumn 28, Issue 1-2, 2003, Pages 3-32

  Rantzau, Ralf ^a   Shapiro, Leonard D ^b   Mitschang, Bernhard ^a   Wang, Quan ^c  

^a UNIVERSITY OF STUTTGART   (Germany)

^b Portland State University   (United States)

^c ORACLE CORPORATION   (United States)

Author keywords

Frequent itemset discovery; Grouping; Query operators; Relational division; Set containment join

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; ELECTRONIC COMMERCE; FORMAL LOGIC; OPTIMIZATION; QUERY LANGUAGES;

FREQUENT ITEMSET DISCOVERY; GROUPING; ONLINE ANALYTIC PROCESSING; QUERY OPERATORS; RELATIONAL DIVISION; SET CONTAINMENT JOIN; UNIVERSAL QUANTIFICATION;

RELATIONAL DATABASE SYSTEMS;

EID: 12244283073     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4379(02)00047-9     Document Type: Article

References (28)

1. G. Graefe, R. Cole, Fast algorithms for universal quantification in large databases, TODS 20 (2) (1995) 187-236.
2. F. Bry, Towards an efficient evaluation of general queries: quantifier and disjunction processing revisited, in: Proceedings SIGMOD, Portland, OR, USA, May-June 1989, pp. 193-204.
3. J. Claußen, A. Kemper, G. Moerkotte, K. Peithner, Optimizing queries with universal quantification in object-oriented and object-relational databases, in: Proceedings VLDB, Athens, Greece, August 1997, pp. 286-295.
4. G. Graefe, Query evaluation techniques for large data-bases, ACM Comput. Surveys 25 (2) (1993) 73-170.
5. P. Gulutzan, T. Pelzer, SQL-99 Complete, Really: An Example-Based Reference Manual of the New Standard, R&D Books, Lawrence, Kansas, USA, 1999.
6. ANSI/ISO/IEC 9075-2, Information Technology, Database Language, SQL - Part 2: Foundation (SQL/Foundation), 1999.
7. W3C, XQuery 1.0: An XML Query Language, Working Draft 7, W3C, 2001.
8. J. Smith, P. Chang, Optimizing the performance of a relational algebra data base interface, CACM 18 (10) (1975) 568-679.
9. C. Nippl, R. Rantzau, B. Mitschang, StreamJoin: a generic database approach to support the class of stream-oriented applications, in: Proceedings IDEAS, Yokohama, Japan, September 2000, pp. 83-91.
10. G. Jaeschke, H. -J. Schek, Remarks on the algebra of non first normal form relations, in: Proceedings PODS, Los Angeles, California, USA, March 1982, pp. 124-138.
11. A. Makinouchi, A consideration on normal form of not-necessarily-normalized relation in the relational data model, in: Proceedings VLDB, Tokyo, Japan, October 1977, pp. 447-453.
12. K. Ramasamy, Efficient storage and query processing of set-valued attributes, Ph.D. Thesis, University of Wisconsin, Madison, WI, USA, 2002, 144pp.
13. S. Helmer, G. Moerkotte, Evaluation of main memory join algorithms for joins with set comparison join predicates, in: Proceedings VLDB, Athens, Greece, August 1997, pp. 386-395.
14. S. Melnik, H. Garcia-Molina, Adaptive algorithms for set containment joins, Department of Computer Science, Stanford University, CA, USA, Technical Report, November 2001.
15. S. Melnik, H. Garcia-Molina, Divide-and-conquer algorithm for computing set containment joins, in: Proceedings EDBT, Prague, Czech Republic, March 2002, pp. 427-444.
16. S. Melnik, H. Garcia-Molina, Divide-and-conquer algorithm for computing set containment joins, Stanford University, Extended Technical Report, CA, USA, 2002.
17. K. Ramasamy, J. Patel, J. Naughton, R. Kaushik, Set containment joins: the good, the bad and the ugly, in: Proceedings VLDB, Cairo, Egypt, September 2000, pp. 351-362.
18. S. Helmer, G. Moerkotte, Compiling away set containment and intersection joins, in: Technical Report 04/02, University of Mannheim, Germany, April 2002.
19. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings VLDB, Santiago, Chile, September 1994, pp. 487-499.
20. M. Houtsma, A. Swami, Set-oriented data mining in relational databases, DKE 17 (3) (1995) 245-262.
21. S. Sarawagi, S. Thomas, R. Agrawal, Integrating association rule mining with relational database systems: alternatives and implications, in: Proceedings SIGMOD, Seattle, WA, USA, June 1998, pp. 343-354.
22. S. Thomas, S. Chakravarthy, Performance evaluation and optimization of join queries for association rule mining, in: Proceedings DaWaK, Florence, Italy, August-September 1999, pp. 241-250.
23. R. Rantzau, Frequent itemset discovery with SQL using universal quantification, in: Proceedings Workshop on Database Technology for Data Mining (DTDM), Prague, Czech Republic, March 2002, pp. 51-66.
24. U. Dayal, Queries with quantities: a horticultural approach, in: Proceedings PODS, Atlanta, Georgia, USA, March 1983, pp. 125-136.
25. U. Dayal, Of nests and trees: a unified approach to processing queries that contain nested subqueries, aggregates, and quantifiers, in: Proceedings VLDB, Brighton, England, September 1987, pp. 197-208.
26. P. Hsu, D. Parker, Improving SQL with generalized quantifiers, in: Proceedings ICDE, Taipei, Taiwan, March 1995, pp. 298-305.
27. S. Rao, A. Badia, D. V. Gucht, Providing better support for a class of decision support queries, in: Proceedings SIGMOD, Montreal, Canada, June 1996, pp. 217-227.
28. M. Jarke, J. Koch, Range nesting: a fast method to evaluate quantified queries, in: Proceedings SIGMOD, San Jose, CA, USA, May 1983, pp. 196-206.