Paper Info
Title
Qb2olap: Enabling Olap On Statistical Linked Open Data
Abstract
Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.
Year
Venue
Field
2016
2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE)
Data warehouse,Data mining,Query language,Programming language,Computer science,Linked data,SPARQL,RDF,RDF query language,Information retrieval,Online analytical processing,RDF Schema,Database
DocType
ISSN
Citations 
Conference
1084-4627
3
PageRank 
References 
Authors
0.39
5
6
Name
Order
Citations
PageRank
Jovan Varga1244.62
Lorena Etcheverry21077.97
Alejandro A. Vaisman365755.94
Oscar Romero446735.46
Torben Bach Pedersen52102181.24
Christian Thomsen69512.10