Social Network services have quickly become a powerful means by which people share real-time messages. Typically, social networks are modeled as large underlying graphs. Responding to this emerging trend, it becomes critically important to interactively view and analyze this massive amount of data from different perspectives and with multiple granularities. While Online analytical processing (O...

Decisional systems are based on multidimensional databases improving OLAP analyses. This chapter describes a new OLAP operator named “BLEND” that performs multigranular analyses. This operation transforms multidimensional structures when querying in order to analyze measures according to several granularity levels like one parameter. We study valid uses of this operation in the context of stric...

OLAP tools have been widely used for decision support analysis. In turn, ontologies has proven to be an effective and well accepted technology for recovering of semantic information. Thus, the integration of these technologies (OLAP and ontology) allows new types of queries that, individually, these technologies are not capable of performing. In this context, considering that related works stil...

We address the problem of querying data cubes for Online Analytical Processing (OLAP) analysis, directly on the Semantic Web (SW). We rst introduce CQL, a simple algebra for querying data cubes at a conceptual level. Taking advantage of QB4OLAP metadata, we automatically translate CQL queries into SPARQL ones, and propose query optimization strategies that adapt, to the particular OLAP setting,...

This paper reviews the literature concerning the practice of using Online Analytical Processing (OLAP) systems to recall information stored by Online Transactional Processing (OLTP) systems. Such a review provides a basis for discussion on the need for the information that are recalled through OLAP systems to maintain the contexts of transactions with the data captured by the respective OLTP sy...

Now there is tremendous interest in data warehousing and OLAP applications. OLAP applications typically view data as having multiple logical dimensions (e.g., product, location) with natural hierarchies de ned on each dimension, and analyze the behavior of various measure attributes (e.g., sales, volume) in terms of the dimensions. OLAP queries typically involve hierarchical selections on some ...