A powerful and easy-to-use querying environment is certainly one of the most important components in a multidimensional database, and its effectiveness is influenced by many other aspects, both logical (data model, integration, policy of view materialization, etc.) and physical (multidimensional or relational storage, indexes, etc.). As is evident, multidimensional querying is often based on the metaphor of the data cube and on the concepts of facts, measures, and dimensions. In contrast to conventional transactional environments, multidimensional querying is often an exploratory process, performed by navigating along the dimensions and measures, increasing/decreasing the level of detail and focusing on specific subparts of the cube that appear to be “promising” for the required information. In this chapter we focus on the main languages proposed in the literature to express multidimensional queries, particularly those based on: (i) an algebraic approach, (ii) a declarative paradigm (calculus), and (iii) visual constructs and syntax. We analyze the problem of evaluation, i.e., the issues related to the efficient data retrieval and calculation, possibly (often necessarily) using some pre-computed data, a problem known in the literature as the problem of rewriting a query using views. We also illustrate the use of particular index structures to speed up the query evaluation process.
MINING BIG DATA FOR EFFICIENT DATA RETRIEVAL: A SURVEY
