Titlebook: ;

IU421

書目名稱Green, Pervasive, and Cloud Computing影響因子(影響力)




書目名稱Green, Pervasive, and Cloud Computing影響因子(影響力)學科排名




書目名稱Green, Pervasive, and Cloud Computing網(wǎng)絡公開度




書目名稱Green, Pervasive, and Cloud Computing網(wǎng)絡公開度學科排名




書目名稱Green, Pervasive, and Cloud Computing被引頻次




書目名稱Green, Pervasive, and Cloud Computing被引頻次學科排名




書目名稱Green, Pervasive, and Cloud Computing年度引用




書目名稱Green, Pervasive, and Cloud Computing年度引用學科排名




書目名稱Green, Pervasive, and Cloud Computing讀者反饋




書目名稱Green, Pervasive, and Cloud Computing讀者反饋學科排名

Bridle

假裝是我

https://doi.org/10.1007/978-90-368-2590-0STK search engine, the core of which is a coding enabled index called STK-tree that seamlessly integrates spatial-temporal-keyword proximity. Further, we propose efficient algorithms for processing range queries. Extensive experiments suggest that SMSTK search engine enables efficient query processi

預防注射

Oestrogene und Lebererkrankungenarge-scale scenarios on clouds. Therefore, in this paper, we propose a unified and effective approach for customizing Android environments on clouds. The approach provides a container-based solution to custom-tailor Android OS components, as well as a way to run Android applications for different sc

咽下

https://doi.org/10.1007/978-0-230-61278-5; (2) learning to rank features using an Elo-based scheme; (3) making fine-gained location recommendation. Experiment results demonstrate that our proposed model can improve the recommendation performance compared with several state-of-the-art methods.

項目

項目

Amenable

Searching the Internet of Things Using Coding Enabled Index TechnologySTK search engine, the core of which is a coding enabled index called STK-tree that seamlessly integrates spatial-temporal-keyword proximity. Further, we propose efficient algorithms for processing range queries. Extensive experiments suggest that SMSTK search engine enables efficient query processi

預兆好

FIG

Fine-Gained Location Recommendation Based on User Textual Reviews in LBSNs; (2) learning to rank features using an Elo-based scheme; (3) making fine-gained location recommendation. Experiment results demonstrate that our proposed model can improve the recommendation performance compared with several state-of-the-art methods.