QB2OLAP: Enabling OLAP on Statistical Linked Open Data

Jovan Varga; Lorena Etcheverry; Alejandro Vaisman; Oscar Romero; Torben Bach Pedersen; Christian Thomsen

doi:10.1109/ICDE.2016.7498341

QB2OLAP: Enabling OLAP on Statistical Linked Open Data

Jovan Varga, Lorena Etcheverry, Alejandro Vaisman, Oscar Romero, Torben Bach Pedersen, Christian Thomsen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

16 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of 2016 IEEE 32nd International Conference on Data Engineering (ICDE)
Number of pages	4
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	2016
Pages	1346-1349
ISBN (Electronic)	978-1-5090-2020-1
DOIs	https://doi.org/10.1109/ICDE.2016.7498341
Publication status	Published - 2016
Event	2016 IEEE 32nd International Conference on Data Engineering (ICDE) - Aalto University, Helsinki, Finland Duration: 16 May 2016 → 20 May 2016 Conference number: 32 http://icde2016.fi/

Conference

Conference	2016 IEEE 32nd International Conference on Data Engineering (ICDE)
Number	32
Location	Aalto University
Country/Territory	Finland
City	Helsinki
Period	16/05/2016 → 20/05/2016
Internet address	http://icde2016.fi/

Access to Document

10.1109/ICDE.2016.7498341

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inproceedings{4b4e41e0bda645b1965c676d4f036fc6,

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.",

author = "Jovan Varga and Lorena Etcheverry and Alejandro Vaisman and Oscar Romero and Pedersen, {Torben Bach} and Christian Thomsen",

year = "2016",

doi = "10.1109/ICDE.2016.7498341",

language = "English",

pages = "1346--1349",

booktitle = "Proceedings of 2016 IEEE 32nd International Conference on Data Engineering (ICDE)",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

address = "United States",

note = "2016 IEEE 32nd International Conference on Data Engineering (ICDE), ICDE ; Conference date: 16-05-2016 Through 20-05-2016",

url = "http://icde2016.fi/",

}

Varga, J, Etcheverry, L, Vaisman, A, Romero, O, Pedersen, TB & Thomsen, C 2016, QB2OLAP: Enabling OLAP on Statistical Linked Open Data. in Proceedings of 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE (Institute of Electrical and Electronics Engineers), pp. 1346-1349, 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 16/05/2016. https://doi.org/10.1109/ICDE.2016.7498341

QB2OLAP: Enabling OLAP on Statistical Linked Open Data. / Varga, Jovan; Etcheverry, Lorena; Vaisman, Alejandro et al.
Proceedings of 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE (Institute of Electrical and Electronics Engineers), 2016. p. 1346-1349.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - QB2OLAP

T2 - 2016 IEEE 32nd International Conference on Data Engineering (ICDE)

AU - Varga, Jovan

AU - Etcheverry, Lorena

AU - Vaisman, Alejandro

AU - Romero, Oscar

AU - Pedersen, Torben Bach

AU - Thomsen, Christian

N1 - Conference code: 32

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1109/ICDE.2016.7498341

DO - 10.1109/ICDE.2016.7498341

M3 - Article in proceeding

SP - 1346

EP - 1349

BT - Proceedings of 2016 IEEE 32nd International Conference on Data Engineering (ICDE)

PB - IEEE (Institute of Electrical and Electronics Engineers)

Y2 - 16 May 2016 through 20 May 2016

ER -

QB2OLAP: Enabling OLAP on Statistical Linked Open Data

Abstract

Conference

Access to Document

AUB Link

Fingerprint

Cite this