A contextual personalized recommander system for mobile OLAP

The main drawbacks and handled devices (small storage space, small size of the display screen, discontinuance of the connection to the WLAN etc) are often incompatible with the need of querying and browsing information extracted from enormous amounts of data which are accessible through the network. A key characteristic of emerging OLAP database systems will be to use them with mobile device. To overcome this we investigate in this paper to propose a contextual recommender system (CARS) for mobile OLAP based on multi-agent architecture called (MASCARS). This system extracts first the user profile from the log data file of OLAP server. For this we use a variant of prediction and explanation algorithms. These profiles then form a knowledge base. This knowledge base will be used to generate automatically a rule base (ACE), for assigning weights to the attributes of data cubes by type of query and user preferences. The best sequence of requests will be deduced for using in CARS.