Authors:
Hyun Namgoong
1
;
Harshit Kumar
2
and
Hong-Gee Kim
1
Affiliations:
1
Seoul National University, Korea, Republic of
;
2
Seoul National University and University of Suwon, Korea, Republic of
Keyword(s):
RDF data store, Data warehouse, Cloud computing service, Sesame.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
e-Business
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Process Knowledge and Semantic Services
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
An expanding need for interoperability and structuralization of web data has made use of RDF (Resource Description Framework) plentiful. To guarantee a common usage of the data within various applications, several RDF stores providing data management services have been developed. Here, we represent a systematic approach to solve a late latency problem of data loading of the stores. It enables a fast loading performance for very large size of RDF data, and it is proven with an existing RDF store. This approach employs a cloud computing service and delegates preparation works to the machines which are temporarily borrowed at little payment. Our implementation for a native version of the Sesame RDF Repository was tested on LUBM 1000 University data (138 million triples), and it showed a local store loading time of 16.2 minutes with additional preparation time on a cloud service taking approximately an hour, which can be reduced by adding supplemental machines to the cluster.