Article

DADA: a data cube for dominant relationship analysis

Authors:

Anthony K. H. Tung,

Shan WangAuthors Info & Claims

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

Pages 659 - 670

https://doi.org/10.1145/1142473.1142547

Published: 27 June 2006 Publication History

Abstract

The concept of dominance has recently attracted much interest in the context of skyline computation. Given an N-dimensional data set S, a point p is said to dominate q if p is better than q in at least one dimension and equal to or better than it in the remaining dimensions. In this paper, we propose extending the concept of dominance for business analysis from a microeconomic perspective. More specifically, we propose a new form of analysis, called Dominant Relationship Analysis (DRA), which aims to provide insight into the dominant relationships between products and potential buyers. By analyzing such relationships, companies can position their products more effectively while remaining profitable.To support DRA, we propose a novel data cube called DADA (Data Cube for Dominant Relationship Analysis), which captures the dominant relationships between products and customers. Three types of queries called Dominant Relationship Queries (DRQs) are consequently proposed for analysis purposes: 1)Linear Optimization Queries (LOQ), 2)Subspace Analysis Queries (SAQ), and 3)Comparative Dominant Queries (CDQ). Algorithms are designed for efficient computation of DADA and answering the DRQs using DADA. Results of our comprehensive experiments show the effectiveness and efficiency of DADA and its associated query processing strategies.

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Cited By

Tai LChen A(2023)Strategies to Selecting Most Profitable Products by Price SettingsInternational Journal of Information Technology & Decision Making10.1142/S021962202350043823:02(885-915)Online publication date: 17-May-2023
https://doi.org/10.1142/S0219622023500438
Mouratidis KLi KTang B(2023)Quantifying the competitiveness of a dataset in relation to general preferencesThe VLDB Journal10.1007/s00778-023-00804-133:1(231-250)Online publication date: 8-Aug-2023
https://doi.org/10.1007/s00778-023-00804-1
Pawar MSaini S(2022)Mining Top-K Competitors by Eliminating the K-Least Items from Unstructured DatasetInnovations in Electronics and Communication Engineering10.1007/978-981-16-8512-5_54(505-514)Online publication date: 13-Mar-2022
https://doi.org/10.1007/978-981-16-8512-5_54
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Index Terms

DADA: a data cube for dominant relationship analysis
1. Information systems
  1. Data management systems

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cover image ACM Conferences

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

June 2006

830 pages

ISBN:1595934340

DOI:10.1145/1142473

General Chairs:
Clement Yu
University of Illinois at Chicago
,
Peter Scheuermann
Northwestern University
,
Program Chair:
Surajit Chaudhuri
Microsoft Research

Copyright © 2006 ACM.

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Publication History

Published: 27 June 2006

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SIGMOD/PODS06: International Conference on Management of Data and Symposium on Principles Database and Systems

June 27 - 29, 2006

IL, Chicago, USA

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Cited By

Tai LChen A(2023)Strategies to Selecting Most Profitable Products by Price SettingsInternational Journal of Information Technology & Decision Making10.1142/S021962202350043823:02(885-915)Online publication date: 17-May-2023
https://doi.org/10.1142/S0219622023500438
Mouratidis KLi KTang B(2023)Quantifying the competitiveness of a dataset in relation to general preferencesThe VLDB Journal10.1007/s00778-023-00804-133:1(231-250)Online publication date: 8-Aug-2023
https://doi.org/10.1007/s00778-023-00804-1
Pawar MSaini S(2022)Mining Top-K Competitors by Eliminating the K-Least Items from Unstructured DatasetInnovations in Electronics and Communication Engineering10.1007/978-981-16-8512-5_54(505-514)Online publication date: 13-Mar-2022
https://doi.org/10.1007/978-981-16-8512-5_54
Li QYan ZLi JYang ZLin ZLeong HChen LYu N(2022)Event Cube for Suicidal Event Analysis: A Case StudyWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_39(512-526)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-90888-1_39
Tang BMouratidis KHan MLi GLi ZIdreos SSrivastava D(2021)On m-Impact Regions and Standing Top-k Influence ProblemsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452832(1784-1796)Online publication date: 9-Jun-2021
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Yang ZLi XZhang XLuo WLi K(2021)K-truss community most favorites query based on top-tWorld Wide Web10.1007/s11280-021-00947-7Online publication date: 22-Oct-2021
https://doi.org/10.1007/s11280-021-00947-7
Tai LWang EChen A(2021)Finding the most profitable candidate product by dynamic skyline and parallel processingDistributed and Parallel Databases10.1007/s10619-021-07323-4Online publication date: 18-Mar-2021
https://doi.org/10.1007/s10619-021-07323-4
Jiang TZhang BLin DGao YLI Q(2020)Efficient column-oriented processing for mutual subspace skyline queriesSoft Computing10.1007/s00500-020-04875-yOnline publication date: 21-Mar-2020
https://doi.org/10.1007/s00500-020-04875-y
Tang BMouratidis KYiu MChen Z(2019)Creating top ranking options in the continuous option and preference spaceProceedings of the VLDB Endowment10.14778/3339490.333950012:10(1181-1194)Online publication date: 1-Jun-2019
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Morimoto Y(2019)Privacy-Aware Set-based Information Analysis2019 International Conference on Sustainable Information Engineering and Technology (SIET)10.1109/SIET48054.2019.8986065(1-4)Online publication date: Sep-2019
https://doi.org/10.1109/SIET48054.2019.8986065
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