By Esteban Zimányi, Alberto Abelló
This ebook constitutes the academic lectures of the fifth ecu company Intelligence summer season college, eBISS 2015, held in Barcelona, Spain, in July 2015.
The tutorials provided right here in a longer and sophisticated layout got by means of well known specialists and canopy subject matters together with schema evolution for databases and knowledge warehouses, publishing OLAP cubes at the Semantic net, layout concerns in social company intelligence tasks, context-aware company intelligence, and key functionality signs in info warehouses.
Read or Download Business Intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015, Tutorial Lectures PDF
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Extra resources for Business Intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015, Tutorial Lectures
Relevant to OLAP queries, SPARQL supports aggregate functions and the GROUP BY clause, as in classic SQL. Due to space limitations, in the remainder we assume the reader is familiar with the basic notions of RDF and SPARQL. 4 QB: The RDF Data Cube Vocabulary We now study in detail the QB vocabulary, and discuss its possibilities and limitations for representing and analyzing MD data. 1 A. Vaisman Vocabulary Description As mentioned above, QB is the W3C recommendation to publish statistical data and metadata in RDF, following the Linked Data principles.
These two eﬀorts are based on traditional MD data management systems, and require the existence of a local DW to store SW data. Also, they do not consider the possibility of directly querying ` a la OLAP MD data over the SW. Thus, a second line of research tries to overcome these drawbacks, exploring data models and tools that allow publishing and performing OLAP-like analysis directly over SW MD. The work we discuss in the remainder, follows this approach. Terms like self-service BI , and Situational BI , refer to the capability of incorporating situational data into the decision process with little or no intervention of programmers or designers.
346–355. IEEE Computer Society (1999) 47. : Updating OLAP dimensions. J. ) DOLAP, pp. 60–66. ACM (1999) 48. : Schema evolution for stars and snowﬂakes. In: ICEIS (1), pp. 425–433(2004) 49. : Metadata management in a multiversion data warehouse. J. Data Semant. 8, 118–157 (2007) 50. : Automatic detection of structural changes in data warehouses. , W¨ oß, W. ) DaWaK 2003. LNCS, vol. 2737, pp. 119–128. Springer, Heidelberg (2003) 51. : Analysing slices of data warehouses to detect structural modiﬁcations.