Abdelaziz Abdellatif

Web usage mining for the recommendation of materialized webviews

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.

Multi-constraint selection of materialized webviews

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.

MOWS: macro and micro online webview selection

In this paper we present an approach, called MOWS, to select materialized webview in data--intensive websites (DIWS). A webview is a static instance of a dynamic web page. The materialization of webviews consists of storing the results of some requests on the server in order to avoid repetitive data generation from the sources. The aim is to improve the query response time. Our contribution in this work is to apply two steps for the selection of webviews. The first one is executed periodically and it consists of filtering the candidate webviews. It is called macro-selection. The second is executed online and it consists of selecting the materialized webviews. It is called micro-selection. Our experiment results show that our solution is very efficient to improve query response time especially for the websites with high size. For this type of websites, our approach overcomes the existing solutions and it improves their query response times by more than 70%.

Roles of agents in data-intensive web sites

The Data-Intensive Web Sites provide access to a large number of Web pages whose content is dynamically extracted from structured databases. They may be used for shopping or paying in e-commerce, for reading news in a newspaper site or to consult digital library. In this context, users often need rich content and fresh data. Several techniques have been developed to meet the demand for faster and more efficient access to the DIWS. Among them a main role is acquired by the replication, the caching, the materialization, and the refreshing of data. Software agents have proved to be a good tool that may give a high performance results on the Web. In this paper, we address their cases of use in the DIWS. We discuss cases in which agents can be used to improve the data management performance. The aim is to specify tasks that may profit from the increase development in agent technologies.