Paper Info
Title
xStore: Federated temporal query processing for large scale RDF triples on a cloud environment.
Abstract
Temporal information retrieval tasks have a long history in information retrieval field and also have attracted neuroscientists working on memory system. It becomes more important in Semantic Web where structured data in RDF triples, often with temporal information, are rapidly accumulated over time. Existing triple stores already support loading RDF triples and answering a given SPARQL query with time interval constraints. However, few triple stores has been optimized for processing time interval queries which are important for temporal information retrieval tasks. In this paper, we propose xStore, a federated SPARQL engine running on a cloud environment, which supports a fast processing of temporal queries. xStore is built on top of heterogeneous storages such as key-value stores and conventional triple stores. Experiments over real-world temporal datasets showed that our approach is faster than a conventional SPARQL engine for processing temporal queries.
Year
DOI
Venue
2017
10.1016/j.neucom.2016.03.116
Neurocomputing
Keywords
Field
DocType
SPARQL,Temporal query processing,RDF
RDF query language,Information retrieval,Computer science,Semantic Web,SPARQL,RDF Schema,Data model,Database,RDF,Cloud computing
Journal
Volume
ISSN
Citations 
256
0925-2312
2
PageRank 
References 
Authors
0.36
21
6
Name
Order
Citations
PageRank
Jinhyun Ahn1255.65
Jae-Hong Eom2868.91
Sejin Nam32823.20
Nansu Zong4455.68
Dong-Hyuk Im5356.06
Hong-Gee Kim622522.83