Multidimensional query recommendation

In this master thesis we will first summarize the recent efforts to support analytical tasks over relational sources. These efforts have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decision oriented processes (such as query recommendation or similar). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. This thesis discusses how a SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e. collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections. Afterwards, we talk about how this characterization can be exploited in a wide range of decisional tasks such as query recommendation and others. Finally, we present an implementation example of this approach with limiting it with normalization phase because it surprisingly turned out that it is hard enough to achieve with regards to time available. This implementation may later be upgraded to demonstrate the full potential of this novel approach. 1.3 Scop