Diversification on big data in query processing

Recently, in the area of big data, some popular applications such as web search engines and recommendation systems, face the problem to diversify results during query processing. In this sense, it is both significant and essential to propose methods to deal with big data in order to increase the div...

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Vydáno v:Frontiers of Computer Science Ročník 14; číslo 4; s. 144607
Hlavní autoři: ZHANG, Meifan, WANG, Hongzhi, LI, Jianzhong, GAO, Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing Higher Education Press 01.08.2020
Springer Nature B.V
Témata:
Algorithms
Big Data
Computer Science
Diversification
Hotels & motels
Queries
Query processing
Recommender systems
Research Article
Search engines
Strings
Synthetic data
query processing
big data
diversification
ISSN:2095-2228, 2095-2236
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Popis
Shrnutí:Recently, in the area of big data, some popular applications such as web search engines and recommendation systems, face the problem to diversify results during query processing. In this sense, it is both significant and essential to propose methods to deal with big data in order to increase the diversity of the result set. In this paper, we firstly define the diversity of a set and the ability of an element to improve the overall diversity. Based on these definitions, we propose a diversification framework which has good performance in terms of effectiveness and efficiency. Also, this framework has theoretical guarantee on probability of success. Secondly, we design implementation algorithms based on this framework for both numerical and string data. Thirdly, for numerical and string data respectively, we carry out extensive experiments on real data to verify the performance of our proposed framework, and also perform scalability experiments on synthetic data.
Bibliografie:query processing
Document accepted on :2019-03-05
diversification
big data
Document received on :2018-09-22
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2095-2228
2095-2236
DOI:10.1007/s11704-019-8324-9