Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2024
Abstract
Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used in tandem with information on user preferences (which can be readily derived by existing methods). Interestingly, our study also finds side-applications with strong practical relevance in the area of multi-objective querying. We propose a suite of algorithms to efficiently produce the heat-map, and conduct case studies and an empirical evaluation to demonstrate the practicality of our work.
Keywords
heat-map, preference queries, multi-dimensional data, market analysis
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
VLDB Journal
Volume
33
Issue
1
First Page
231
Last Page
250
ISSN
1066-8888
Identifier
10.1007/s00778-023-00804-1
Publisher
Springer
Citation
MOURATIDIS, Kyriakos; LI, Keming; and TANG, Bo.
Quantifying the competitiveness of a dataset in relation to general preferences. (2024). VLDB Journal. 33, (1), 231-250.
Available at: https://ink.library.smu.edu.sg/sis_research/8264
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00778-023-00804-1