Agent Based Dynamic Data Splitting In Relationnals Data Warehouses

In this paper, we propose a new approach to manage data distribution in a relationnal data warehouse environment. This approach deals with the dynamic data splitting on a set of interconnected machines. The data distribution that we consider is different from the “classical” one which depends on the data use. The distribution in our approach consists in splitting data when the machine reaches his storage capacity limit. This distribution assures the scalability and exploits the storage and processing resources available in the organization using the data warehouse. It is worth noting that our approach is based on a multi-agent model mixed with the scalability distribution proposed by the Scalable Distributed Data Structures. In this paper, we focus on the global dynamic for the data splitting operation based on Branch and Bound algorithm.