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Top κ Favorite Probabilistic Products Queries.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Oct2016, Vol. 28 Issue 10, p2808-2821. 14p. - Publication Year :
- 2016
-
Abstract
- With the development of the economy, products are significantly enriched, and uncertainty has been their inherent quality. The probabilistic dynamic skyline (PDS) query is a powerful tool for customers to use in selecting products according to their preferences. However, this query suffers several limitations: it requires the specification of a probabilistic threshold, which reports undesirable results and disregards important results; it only focuses on the objects that have large dynamic skyline probabilities; and, additionally, the results are not stable. To address this concern, in this paper, we formulate an uncertain dynamic skyline (UDS) query over a probabilistic product set. Furthermore, we propose effective pruning strategies for the UDS query, and integrate them into effective algorithms. In addition, a novel query type, namely the top κ favorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to select κ products which can meet the needs of a customer set at the maximum level. To tackle the TFPP query, we propose a TFPP algorithm and its efficient parallelization. Extensive experiments with a variety of experimental settings illustrate the efficiency and effectiveness of our proposed algorithms. [ABSTRACT FROM PUBLISHER]
- Subjects :
- *PARALLEL algorithms
*SUPPLY & demand
*CUSTOMER satisfaction
*ONLINE shopping
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 28
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 118051766
- Full Text :
- https://doi.org/10.1109/TKDE.2016.2584606