Titlebook: Database Systems for Advanced Applications; 22nd International C Sel?uk Candan,Lei Chen,Wen Hua Conference proceedings 2017 Springer Intern

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書目名稱Database Systems for Advanced Applications影響因子(影響力)




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




書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度




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




書目名稱Database Systems for Advanced Applications被引頻次




書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名




書目名稱Database Systems for Advanced Applications年度引用




書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名




書目名稱Database Systems for Advanced Applications讀者反饋




書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名

隼鷹

Learning the Structures of Online Asynchronous Conversations online via instant messaging applications on personal computers or mobile devices. Since these conversations are sequentially organized, which fails to show the logical relations between messages, they are called asynchronous conversations in previous studies. Unfortunately, the sequential layouts

女上癮

A Question Routing Technique Using Deep Neural Network for Communities of Question Answering asked by their users. Although CQA frameworks receive new questions in a linear rate, the rate of the unanswered questions increases in an exponential way. This variation eventually compromise effectiveness of the CQA frameworks as knowledge sharing platforms. The main cause for this challenge is t

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糾纏

An Adaptive Data Partitioning Scheme for Accelerating Exploratory Spark SQL Queries next queries. Thus, data used in the previous queries are often reused, at least in part, in the next queries. This fact may be used to accelerate queries with data partitioning, a widely used technique that enables skipping the irrelevant data for better I/O performance. For getting effective part

tic-douloureux

Semi-Supervised Network Embeddingachine learning tasks such as node classification, link prediction, and social recommendation. Current popular network embedding methods normally first transform the network into a set of node sequences, and then input them into an unsupervised feature learning model to generate a distributed repres

斜

CirE: Circular Embeddings of Knowledge Graphscontinuous vector spaces and then constructs . triples. However, KG embedding models are sensitive to infrequent and uncertain objects. Furthermore, there is a contradiction between learning ability and learning cost. To this end, we propose circular embeddings (CirE) to learn representations of ent