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 offered right here in a longer and sophisticated layout got via well known specialists and canopy subject matters together with schema evolution for databases and knowledge warehouses, publishing OLAP cubes at the Semantic internet, layout concerns in social enterprise intelligence initiatives, context-aware company intelligence, and key functionality symptoms in information 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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Additional info for Business Intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015, Tutorial Lectures
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. , Stirna, J.
The ﬁrst two lines of work refer to the evolution of multidimensional database schemata and the adaptation of its contents, and the ﬁnal eﬀort refers to a comparison of the logical design between star and snowﬂake alternatives. ” Then, we can ask queries that span several versions Table 4. Summary table for Sect. 2 Works Problem  Input Multidimensional MD schema; database Changes of adaptation schemata Output Method New schema and instances Automatic adaptation of multidimensional schema and instances through simple and complex operators of an evolution algebra [46, 47] Multidimensional MD schema; database Changes of adaptation schemata Normalized or Use of primitive de-normalized dimension update new (RDBMS) operators and schema complex operators that map the multidimensional schemata to RDBMS schemata  Comparison of Perform the changes logical designs to both star and to changes snowﬂake designs Evolution of Changes of alternative schemata relational logical designs 24 P.
The query evaluation mechanism of SPARQL is based on subgraph matching: RDF triples are interpreted as nodes and edges of directed graphs, and the query graph is matched to the data graph, instantiating the variables in the query graph deﬁnition. The selection criteria is expressed as a graph pattern in the WHERE clause of a SPARQL query. 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.