TY - GEN
T1 - Large-Scale Semantic Data Management For Urban Computing Applications
AU - Song, Shengli
AU - Zhang, Xiang
AU - Guo, Bin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Due to the current lack of effectiveness on perception, management, and coordination for urban computing applications, a great number of semantic data has not yet been fully exploited and utilized, decreasing the effectiveness of urban services. To address the problem, we propose a semantic data management framework, RDFStore, for large-scale urban data management and query. RDFStore uses hashcode as the basic encoding pattern for semantic data storage. Based on the characteristics of strong connectedness of the data clique with different semantics, we construct indexes through the maximum clique on the whole semantic data. The large-scale semantic data of urban computing is organized and managed. On the basis of clique index, we adopt CLARANS clustering to enhance the accessibility of vertexes, and the data management is fulfilled. The experiment compares RDFStore to the mainstream platforms, and the results show that the proposed framework does enhance the effectiveness of semantic data management for urban computing applications.
AB - Due to the current lack of effectiveness on perception, management, and coordination for urban computing applications, a great number of semantic data has not yet been fully exploited and utilized, decreasing the effectiveness of urban services. To address the problem, we propose a semantic data management framework, RDFStore, for large-scale urban data management and query. RDFStore uses hashcode as the basic encoding pattern for semantic data storage. Based on the characteristics of strong connectedness of the data clique with different semantics, we construct indexes through the maximum clique on the whole semantic data. The large-scale semantic data of urban computing is organized and managed. On the basis of clique index, we adopt CLARANS clustering to enhance the accessibility of vertexes, and the data management is fulfilled. The experiment compares RDFStore to the mainstream platforms, and the results show that the proposed framework does enhance the effectiveness of semantic data management for urban computing applications.
KW - Data clustering
KW - Encoding pattern
KW - Semantic data management
KW - Urban computing
UR - http://www.scopus.com/inward/record.url?scp=85064054112&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15093-8_8
DO - 10.1007/978-3-030-15093-8_8
M3 - 会议稿件
AN - SCOPUS:85064054112
SN - 9783030150921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 107
EP - 123
BT - Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers
A2 - Li, Shijian
PB - Springer Verlag
T2 - 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018
Y2 - 11 May 2018 through 13 May 2018
ER -