Titlebook: Database and Expert Systems Applications; 27th International C Sven Hartmann,Hui Ma Conference proceedings 2016 Springer International Publ

Motion

書目名稱Database and Expert Systems Applications影響因子(影響力)




書目名稱Database and Expert Systems Applications影響因子(影響力)學(xué)科排名




書目名稱Database and Expert Systems Applications網(wǎng)絡(luò)公開度




書目名稱Database and Expert Systems Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Database and Expert Systems Applications被引頻次




書目名稱Database and Expert Systems Applications被引頻次學(xué)科排名




書目名稱Database and Expert Systems Applications年度引用




書目名稱Database and Expert Systems Applications年度引用學(xué)科排名




書目名稱Database and Expert Systems Applications讀者反饋




書目名稱Database and Expert Systems Applications讀者反饋學(xué)科排名

intention

General Purpose Index-Based Method for Efficient MaxRS Queryod solves it in time . for a particular given rectangle size. This is inefficient in cases where the queries are frequently called with different parameters. Thus, in this paper, we propose an index-based method that solves the maxRS query in time . for any given query. Besides, our method can be us

Rodent

An Efficient Method for Identifying MaxRS Location in Mobile Ad Hoc Networksg of nodes which hold data items with scores and location information. A query originating node issues a MaxRS query with a size of a rectangle. Then, it retrieves the location of the rectangle which maximizes the sum of the scores of all data items covered by the rectangle (MaxRS location). We can

傻

昏暗

Invigorate

Invigorate

Clumsy

inflate

Modeling Information Diffusion via Reputation Estimationffusion process in the near future. Although this problem has been extensively studied, the challenge of how to effectively combine user activities, network structures and diffused information in social data remains largely open. In addition, no prior work judged the effect of user reputation on the

Decongestant

Mining Arbitrary Shaped Clusters and Outputting a High Quality Dendrograme existing HC algorithms focus on either the quality of the dendrogram or the ability of mining arbitrary shaped clusters. To address the above two aspects simultaneously, we present HICMEN by adopting (1) the classic agglomerative clustering framework that can generate a complete dendrogram, and (2