IDiff: Informative summarization of differences in multidimensional aggregates

Data Mining and Knowledge Discovery

Volumn 5, Issue 4, 2001, Pages 255-276

  Sarawagi, Sunita ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Advanced aggregates; Data summarization; Difference mining; Multidimensional databases; OLAP; OLAP mining integration

Indexed keywords

EID: 23044531476     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1011494927464     Document Type: Article

References (20)

1. Arbor Software Corporation, Sunnyvale, CA. Multidimensional Analysis: Converting Corporate Data into Strategic Information, http://www.arborsoft.com.
2. Chaudhuri, S. and Dayal, U. 1997. An overview of data warehouse and OLAP technology. ACM SIGMOD Record.
3. Codd, E.F. 1993. Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate. Technical Report, E. F. Codd and Associates.
4. Cognos Software Corporation. 1997. Power play 5, special edition, http://www.cognos.com/powercubes/index.html.
5. International Data Corporation, http://www.idc.com, 1997.
6. The OLAP Council. The OLAP benchmark, http://www.olapcouncil.org.
7. Cover, T.M. and Thomas, J.A. 1991. Elements of Information Theory. New York: John Wiley and Sons.
8. DBMS. 1998. Open olap in intelligent enterprise. DBMS Magazine, April 1998. http://www.dbmsmag.com/9804d14.html.
9. Information Discovery, http://www.datamine.inter.net/.
10. Flajolet, P. and Martin, G.N. 1985. Probabilistic counting algorithms for database applications. Journal of Computer and System Sciences, 31:182-209.
11. Gerber, C. 1996. Excavate your data. Datamation, May 1 1996. http://www.datamation.com/PlugIn/issues/1996/may1/05asoft3.html.
12. Han, J. 1998. Towards on-line analytical mining: An integration of data warehousing and data mining. DB Summit: http://www.dbsummit.com/articles/Han/han.html.
13. Han, J. and Fu, Y. 1995. Discovery of multiple-level association rules from large databases. In Proc. of the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland.
14. Intelligent Data Analysis Group IDAG. 1999. Olap vendors increasingly see data mining integration as potent differentiator. http://www.idagroup.com/v2n0701.htm.
15. Information Discovery Inc. 1996. Olap and datamining: Bridging the gap. http://www.datamining.com/datamine/bridge.htm.
16. Microsoft Corporation. 1998. http://www.microsoft.com/data/oledb/olap/spec/. OLE DB for OLAP version 1.0 Specification.
17. Sarawagi, S. 1999. Explaining differences in multidimensional aggregates. In Proc. of the 25th Int'l Conference on Very Large Databases (VLDB).
18. Shukla, A., Deshpande, P.M., Naughton, J.F., and Ramasamy, K. 1996. Storage estimation for multidimensional aggregates in the presence of hierarchies. In Proc. of the 22nd Int'l Conference on Very Large Databases, Mumbai (Bombay), India, pp. 522-531.
19. 3: Intelligent, interactive investigaton of OLAP data cubes. In Proc. ACM SIGMOD International Conf. on Management of Data (Demonstration section), Dallas, USA, May 2000. http://www.it.iitb.ernet.in/sunita/icube.
20. Thomsen, E. 1998. Olap and datamining: Creating a total dss solution. DB Summit, http://www.dbsummit.com/articles/Thornsen/thornsen.html.