多样性感知的时空文本信息的KNN查询处理方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201701007

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Abstract It is very important to find textual contents satisfying user's demand among a mount of textual contents with location and time tags generated on web. Firstly, location variables and time variables of data objects are normalized, and a three-dimensional Rtree index combining location variables and time variables is designed. Then, a DST-KNN query algorithm and an improved diversity-aware KNN query algorithm called IDST-KNN query algorithm are proposed.Finally, experiments on massive datasets illustrate that the query processing approaches are efficient and accurate.

Key words： Temporal Spatial Textual Content k Nearest Neighbor Query Diversity

Received: 10 September 2016

ZTFLH:

TP 311

About author:: LI Chen(Corresponding author), born in 1991, master student. Her research interests include query processing. SHEN Derong, born in 1964, Ph.D., professor. Her research interests include Web data processing and distributed database. KOU Yue, born in 1980, Ph.D., associate professor. Her research interests include entity resolution and Web data management. NIE Tiezheng, born in 1980. Ph.D., associate professor. His research interests include data quality and data integration YU Ge, born in 1962, Ph.D., professor. His research interests include data stream, data mining and distributed database.

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	LI Chen
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Cite this article:

LI Chen,SHEN Derong,KOU Yue等. Diversity-Aware KNN Query Processing Approaches for Temporal Spatial Textual Content[J]. , 2017, 30(1): 64-72.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201701007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I1/64

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