IDEA: Interactive Data Exploration and Analysis.

Peter G. Selfridge, Divesh Srivastava, Lynn O. Wilson: IDEA: Interactive Data Exploration and Analysis. SIGMOD Conference 1996: 24-34
  author    = {Peter G. Selfridge and
               Divesh Srivastava and
               Lynn O. Wilson},
  editor    = {H. V. Jagadish and
               Inderpal Singh Mumick},
  title     = {IDEA: Interactive Data Exploration and Analysis},
  booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on
               Management of Data, Montreal, Quebec, Canada, June 4-6, 1996},
  publisher = {ACM Press},
  year      = {1996},
  pages     = {24-34},
  ee        = {, db/conf/sigmod/SelfridgeSW96.html},
  crossref  = {DBLP:conf/sigmod/96},
  bibsource = {DBLP,}


The analysis of business data is often an ill-defined task characterized by large amounts of noisy data. Because of this, business data analysis must combine two kinds of interwined tasks: exploration and analysis. Exploration is the process of finding the appropriate subset of data to analyze, and analysis is the process of measuring the data to provide the business answer. While there are many tools available both for exploration and analysis, a single tool or set of tools may not provide full support for these interwined tasks. We report here on a project that set out to understand a specific business data analysis problem and build an environment to support it. The results of this understanding are, first of all, a detailed list of requirements of this task; second, a set of capabilities that meet these requirements; and third, an implemented client-server solution that addresses many of these requirements and identifies others for future work. Our solution incorporates several novel perspectives on data analysis and combines a history mechanisms with a graphical, re-usable representation of the analysis and exploration process. Our approach emphasizes using the database itself to represent as many of these functions as possible.

Copyright © 1996 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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H. V. Jagadish, Inderpal Singh Mumick (Eds.): Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996. ACM Press 1996 BibTeX , SIGMOD Record 25(2), June 1996

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Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6): 914-925(1993) BibTeX
Ronald J. Brachman, Tej Anand: The Process of Knowledge Discovery in Databases: A First Sketch. KDD Workshop 1994: 1-12 BibTeX
Ronald J. Brachman, Peter G. Selfridge, Loren G. Terveen, Boris Altman, Fern Halper, Thomas Kirk, Alan Lazar, Deborah L. McGuinness, Lori Alperin Resnick: Integrated Support for Data Archeology. Int. J. Cooperative Inf. Syst. 2(2): 159-185(1993) BibTeX
Marcel Holsheimer, Martin L. Kersten: Architectural Support for Data Mining. KDD Workshop 1994: 217-228 BibTeX
Gregory Piatetsky-Shapiro, William J. Frawley (Eds.): Knowledge Discovery in Databases. AAAI/MIT Press 1991, ISBN 0-262-62080-4
Contents BibTeX
Evangelos Simoudis, Brian Livezey, Randy Kerber: Integrating Inductive and Deductive Reasoning for Database Mining. KDD Workshop 1994: 37-48 BibTeX

Referenced by

  1. Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
  2. Ronald J. Brachman, Tom Khabaza, Willi Klösgen, Gregory Piatetsky-Shapiro, Evangelos Simoudis: Mining Business Databases. Commun. ACM 39(11): 42-48(1996)
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