. Etcheverry, L. & Vaisman, A. A. Simperl, E., Cimiano, P., Polleres, A., Corcho, O., & Presutti, V., editors. Enhancing OLAP Analysis with Web Cubes, pages 469\textendash483. Springer Berlin Heidelberg, 2012.
Enhancing OLAP Analysis with Web Cubes [link]Paper  abstract   bibtex   
Traditional OLAP tools have proven to be successful in analyzing large sets of enterprise data. For today\textquoterights business dynamics, sometimes these highly curated data is not enough. External data (particularly web data), may be useful to enhance local analysis. In this paper we discuss the extraction of multidimensional data from web sources, and their representation in RDFS. We introduce Open Cubes, an RDFS vocabulary for the specification and publication of multidimensional cubes on the Semantic Web, and show how classical OLAP operations can be implemented over Open Cubes using SPARQL 1.1, without the need of mapping the multidimensional information to the local database (the usual approach to multidimensional analysis of Semantic Web data). We show that our approach is plausible for the data sizes that can usually be retrieved to enhance local data repositories.
@inbook {etcheverry_enhancing_2012,
	title = {Enhancing {OLAP} Analysis with Web Cubes},
	booktitle = {The Semantic Web: Research and Applications},
	series = {Lecture Notes in Computer Science},
	number = {7295},
	year = {2012},
	pages = {469{\textendash}483},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {Traditional {OLAP} tools have proven to be successful in analyzing large sets of enterprise data. For today{\textquoteright}s business dynamics, sometimes these highly curated data is not enough. External data (particularly web data), may be useful to enhance local analysis. In this paper we discuss the extraction of multidimensional data from web sources, and their representation in {RDFS.} We introduce Open Cubes, an {RDFS} vocabulary for the specification and publication of multidimensional cubes on the Semantic Web, and show how classical {OLAP} operations can be implemented over Open Cubes using {SPARQL} 1.1, without the need of mapping the multidimensional information to the local database (the usual approach to multidimensional analysis of Semantic Web data). We show that our approach is plausible for the data sizes that can usually be retrieved to enhance local data repositories.},
	keywords = {Artificial Intelligence (incl. Robotics), Computer Communication Networks, Database Management, Information Systems and Communication Service, Information Systems Applications (incl. Internet), User Interfaces and Human Computer Interaction},
	isbn = {978-3-642-30283-1, 978-3-642-30284-8},
	url = {http://link.springer.com/chapter/10.1007/978-3-642-30284-8_38},
	author = {Lorena Etcheverry and Vaisman, Alejandro A.},
	editor = {Simperl, Elena and Cimiano, Philipp and Polleres, Axel and Corcho, Oscar and Presutti, Valentina}
}
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