From the January 2015 issue
Best Keyword Cover Search
By Ke Deng, Xin Li, Jiaheng Lu, and Xiaofang Zhou
It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.
Editorials and Announcements
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- TKDE celebrates its 25th Anniversary. Editor-in-Chief Jian Pei says, "We are celebrating the 25th Anniversary of TKDE. Since its first issue in March 1989, TKDE has published 2,981 articles, and another 220 articles in the early access portal. With 898 submissions and 79 accepted articles in 2012, TKDE is now the premier journal in the broad and general fields of data management, data mining, and knowledge engineering. We thank all the authors, reviewers, and readers for their continuing support to TKDE. As always, we are eager to hear your ideas and suggestions, and will do our best to meet your expectations. With all your passions, contributions, and supports, TKDE is embracing the new era of big data and big data analytics. Happy birthday to TKDE!"
- eBooks of issues of TKDE can now be downloaded from the Computer Society Digital Library
- State of the Journal Editorial (January 2015)
- Editorial: State of the Transactions (January 2014)
- Editorial (August 2013)
- New EIC Editorial (Feb 2013)
- Outgoing EIC Editorial (Feb 2013)
- State of the Journal (Feb 2012)
- EIC Editorial (January 2011)
- Special Section on the International Conference on Data Engineering (June 2014)
- Special Section on the 27th International Conference on Data Engineering (ICDE 2011)(Oct 2012)
- Special Section on Keyword Search on Structured Data (Dec 2011)
- Cloud Data Management (Sept 2011)
- Special Section on the 26th International Conference on Data Engineering (Aug 2011)
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IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.
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