In this paper, we propose a new constraint to select materialized webviews. The webview materialization is a term used to represent the transformation of dynamic web data into equivalent static web data. That is the creation of a static instance of a dynamic web page, at a certain point in time. Our new selection constraint consists of limiting the number of sources of materialized webviews. Our first aim is to reduce the update frequency of the materialized webview. This will decrease the access cost to the data sources which may be distant. Secondly, we aim to guarantee the stability of the profit and the materialization load of the materialization plan (the set of materialized webviews) during the selection period. That is we will reduce the probability of early violation of the selection constraints or early materialization loss of the selected plan. Our experiment results show that for small values of the source constraint, our approach can improve the average profit of materialization by more than 15%. Also, when the number of candidate webviews is high, the query response time is improved by applying our solution.

In this paper, we propose an approach, which is based on web usage mining techniques, to recommend webviews to be materialized. The webview materialization is a term used to represent the transformation of dynamic web data into equivalent static web data. That is the creation of a static instance of a dynamic web page, at a certain point in time. In this work we will extend our previous approach [6] which concerns the use of sequential patterns to recommend the materialized webviews. Firstly we analyze the DIWS historic to extract the frequent sequential patterns and the frequent association rules. Then we use these repetitive behaviors to calculate a materialization weight for each webview. The webviews with high materialization weights are the most recommended for the materialization. Our experiment results show that our approach reduces the materialization risk more than those using only the recent time period to select the materialized webviews.

The data warehouse (DWH) is usually presented as a centralized database. In this paper, we propose a new approach to manage data storage and distribution in a data warehouse environment. This approach deals with the dynamic data distribution of the DWH on a set of servers. 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 distributing data when the server reaches its 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. The proposed multi-agent model is composed of stationary agent classes: Client, Dispatcher, Domain and Server, and a mobile agent class called Messenger. These agents collaborate and interact to achieve automatically the storage, the splitting (distribution), the redirection and the access operations on the distributed data warehouse. In this paper, we demonstrate the improvements obtained when we have used the multi-agent system and the Messenger agents in the data storage operation.

With the increase in availability of databases, data needed by a user are frequently in separate autonomous databases. The logical properties of such data differ from the classical ones within a single database. In particular, they call for new functions for data manipulation. MDSL is a new data manipulation language providing such functions. Most of the MDSL functions are not available in other languages.