2015 International Conference on Big Data and Smart Computing (BigComp) (2015)
Jeju, South Korea
Feb. 9, 2015 to Feb. 11, 2015
Liang He , University of Science and Technology of China, Hefei, China
Bin Shao , Microsoft Research, Beijing, China
Yatao Li , Microsoft Research, Beijing, China
Enhong Chen , University of Science and Technology of China, Hefei, China
The acquisition of knowledge becomes scalable. Due to the great connectedness, knowledge data by its very nature are complex entity graphs with rich schemata. The machine-processable knowledge keeps its pace with the phenomenal “Big Data” era. On the one hand, we have a revolutionary way of piling knowledge up; on the other hand, the technology of making the knowledge graph accessible, i.e. how to serve the knowledge to support real-life applications, evolves slowly. This paper presents our efforts of serving real-world knowledge graphs at scale for real-time query processing.
Resource description framework, Computer architecture, Microprocessors, Query processing, Data models, Real-time systems, Indexes
L. He, B. Shao, Y. Li and E. Chen, "Distributed real-time knowledge graph serving," 2015 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Jeju, South Korea, 2015, pp. 262-265.