TY - GEN
T1 - A framework and a language for on-line analytical processing on graphs
AU - Beheshti, Seyed Mehdi Reza
AU - Benatallah, Boualem
AU - Motahari-Nezhad, Hamid Reza
AU - Allahbakhsh, Mohammad
PY - 2012/11/26
Y1 - 2012/11/26
N2 - Graphs are essential modeling and analytical objects for representing information networks. Existing approaches, in on-line analytical processing on graphs, took the first step by supporting multi-level and multi-dimensional queries on graphs, but they do not provide a semantic-driven framework and a language to support n-dimensional computations, which are frequent in OLAP environments. The major challenge here is how to extend decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, one of the critical deficiencies of graph query languages, e.g. SPARQL, is the lack of support for n-dimensional computations. In this paper, we propose a graph data model, GOLAP, for online analytical processing on graphs. This data model enables extending decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, we extend SPARQL to support n-dimensional computations. The approaches presented in this paper have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on synthetic and real-world datasets.
AB - Graphs are essential modeling and analytical objects for representing information networks. Existing approaches, in on-line analytical processing on graphs, took the first step by supporting multi-level and multi-dimensional queries on graphs, but they do not provide a semantic-driven framework and a language to support n-dimensional computations, which are frequent in OLAP environments. The major challenge here is how to extend decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, one of the critical deficiencies of graph query languages, e.g. SPARQL, is the lack of support for n-dimensional computations. In this paper, we propose a graph data model, GOLAP, for online analytical processing on graphs. This data model enables extending decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, we extend SPARQL to support n-dimensional computations. The approaches presented in this paper have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on synthetic and real-world datasets.
KW - Graph OLAP
KW - Query Processing
KW - SPARQL
UR - http://www.scopus.com/inward/record.url?scp=84869380239&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35063-4_16
DO - 10.1007/978-3-642-35063-4_16
M3 - Conference proceeding contribution
AN - SCOPUS:84869380239
SN - 9783642350627
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 213
EP - 227
BT - Web Information Systems Engineering
A2 - Wang, X. Sean
A2 - Cruz, Isabel
A2 - Delis, Alex
A2 - Huang, Guanyan
PB - Springer, Springer Nature
T2 - 13th International Conference on Web Information Systems Engineering, WISE 2012
Y2 - 28 November 2012 through 30 November 2012
ER -