article

Free access

i3: intelligent, interactive investigation of OLAP data cubes

Authors:

Sunita Sarawagi,

Gayatri SatheAuthors Info & Claims

ACM SIGMOD Record, Volume 29, Issue 2

Page 589

https://doi.org/10.1145/335191.336564

Published: 16 May 2000 Publication History

PDF eReader

Abstract

The goal of the i³(eye cube) project is to enhance multidimensional database products with a suite of advanced operators to automate data analysis tasks that are currently handled through manual exploration. Most OLAP products are rather simplistic and rely heavily on the user's intuition to manually drive the discovery process. Such ad hoc user-driven exploration gets tedious and error-prone as data dimensionality and size increases. We first investigated how and why analysts currently explore the data cube and then automated them using advanced operators that can be invoked interactively like existing simple operators.

Our proposed suite of extensions appear in the form of a toolkit attached with a OLAP product. At this demo we will present three such operators: DIFF, RELAX and INFORM with illustrations from real-life datasets.

References

[1]

S. Saxawagi. Explaining diffcrence# in multidimensional aggregates, in Proc. of the 25th Int'l Conference ou Very Large Databases (VLDB), 1999.

Digital Library

Google Scholar

[2]

S. Saxawagi. User adaptive exploration of olap data cubes. Submitted for publication: http://#, it, iitb. ernet, in/'suni#a, 2000.

Google Scholar

Cited By

View all

Currim SSnodgrass RSuh Y(2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3636425
Lockhart BPeng JWu WWang JWu E(2021)Explaining inference queries with bayesian optimizationProceedings of the VLDB Endowment10.14778/3476249.347630414:11(2576-2585)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476304
Ma PDing RHan SZhang DLi GLi ZIdreos SSrivastava D(2021)MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data AnalysisProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457267(1262-1274)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457267
Show More Cited By

Index Terms

i3: intelligent, interactive investigation of OLAP data cubes
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Physical data models

Recommendations

i3: intelligent, interactive investigation of OLAP data cubes
SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

The goal of the i³(eye cube) project is to enhance multidimensional database products with a suite of advanced operators to automate data analysis tasks that are currently handled through manual exploration. Most OLAP products are rather simplistic and ...
The induced generalized OWA operator

We present the induced generalized ordered weighted averaging (IGOWA) operator. It is a new aggregation operator that generalizes the OWA operator, including the main characteristics of both the generalized OWA and the induced OWA operator. This ...
Constructing Choquet integral-based operators that generalize weighted means and OWA operators

A new class of aggregation operators is proposed to generalize weighted means and OWA operators.SUOWA operators are defined by using Choquet integral.These operators are continuous, monotonic, idempotent, compensative and homogeneous of degree 1 ...

Comments

Information & Contributors

Information

Published In

ACM SIGMOD Record Volume 29, Issue 2

June 2000

609 pages

ISSN:0163-5808

DOI:10.1145/335191

Editors:
Weidong Chen
Southern Methodist Univ., Dallas, TX
,
Jeffrey Naughton
Univ. of Wisconsin-Madison, Madison
,
Philip A. Bernstein
Microsoft

Issue’s Table of Contents

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data
May 2000
604 pages
ISBN:1581132174
DOI:10.1145/342009
Chairmen:
Maggie Dunham
Southern Methodist Univ.
,
Jeffrey F. Naughton
Univ. of Wisconsin-Madison
,
Weidong Chen
Southern Methodist Univ.
,
Nick Koudas
AT &T Labs

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2000

Published in SIGMOD Volume 29, Issue 2

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

35
Total Citations
View Citations
180
Total Downloads

Downloads (Last 12 months)67
Downloads (Last 6 weeks)21

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Currim SSnodgrass RSuh Y(2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3636425
Lockhart BPeng JWu WWang JWu E(2021)Explaining inference queries with bayesian optimizationProceedings of the VLDB Endowment10.14778/3476249.347630414:11(2576-2585)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476304
Ma PDing RHan SZhang DLi GLi ZIdreos SSrivastava D(2021)MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data AnalysisProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457267(1262-1274)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457267
Omidvar-Tehrani BAmer-Yahia SBorromeo R(2019)User group analyticsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0527-428:2(243-266)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s00778-018-0527-4
Tang BHan SYiu MDing RZhang DChirkova RYang JSuciu D(2017)Extracting Top-K Insights from Multi-dimensional DataProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3035922(1509-1524)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3035922
Kahng MFang DChau DBinnig CFekete ANandi A(2016)Visual exploration of machine learning results using data cube analysisProceedings of the Workshop on Human-In-the-Loop Data Analytics10.1145/2939502.2939503(1-6)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1145/2939502.2939503
Nandi AYu CBohannon PRamakrishnan R(2012)Data Cube Materialization and Mining over MapReduceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2011.25724:10(1747-1759)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1109/TKDE.2011.257
Alves RRibeiro JBelo O(2009)Mining significant change patterns in multidimensional spacesInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2009.0290734:3/4(219-241)Online publication date: 1-Nov-2009
https://dl.acm.org/doi/10.1504/IJBIDM.2009.029073
Siddiqui TChaudhuri SNarasayya V(2021)COMPAREProceedings of the VLDB Endowment10.14778/3476249.347629114:11(2419-2431)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476291
Battle LScheidegger C(2021)A Structured Review of Data Management Technology for Interactive Visualization and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302889127:2(1128-1138)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028891
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations