Titlebook: Big Data; 6th CCF Conference, Zongben Xu,Xinbo Gao,Jiajun Bu Conference proceedings 2018 Springer Nature Singapore Pte Ltd. 2018 artificia

灰塵

書(shū)目名稱(chēng)Big Data影響因子(影響力)




書(shū)目名稱(chēng)Big Data影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Big Data網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Big Data被引頻次




書(shū)目名稱(chēng)Big Data被引頻次學(xué)科排名




書(shū)目名稱(chēng)Big Data年度引用




書(shū)目名稱(chēng)Big Data年度引用學(xué)科排名




書(shū)目名稱(chēng)Big Data讀者反饋




書(shū)目名稱(chēng)Big Data讀者反饋學(xué)科排名

詞匯表

寬宏大量

inchoate

世俗

https://doi.org/10.1007/978-981-15-2878-1hat extent do device models influence the behaviors of their users? The answer to this question is critical to almost every stakeholder in the smartphone app ecosystem, including app store operators, developers, end-users, and network providers. To approach this question, we collect a longitudinal d

PACT

https://doi.org/10.1007/978-981-15-2878-1e-base, an artificial bee colony algorithm combined with Gaussian disturbance optimization was introduced, and a novel Belief rule-base parameter training method was proposed. By the light of the algorithm principle of the artificial bee colony, the honey bee colony search formula and the cross-bord

淺灘

https://doi.org/10.1057/978-1-137-56036-0h. However, the most existing community search methods do not consider the influence of nodes and can not perfectly support the search in large graphs, making them have limitations in practical applications. In this paper, we introduce a community model called . community based on .-core decompositi

sterilization

https://doi.org/10.1057/978-1-137-56036-0entific impact prediction, which is mainly based on longtime accumulated citation networks, metadata and the whole text of papers, is relatively hysteretic and can hardly fit the rapid development of technology. Moreover, Twitter has become one of the most import channels to spread latest technique

HAWK

沉思的魚(yú)

https://doi.org/10.1057/978-1-137-56036-0eries on time-dependent network to find the optimal path, for example: shortest route, highest scoring route, etc. However, in practical application, users will want to be satisfied with the constraint and evaluate the good routes to make a choice, for example, users want to look for the well-evalua