M'hamed Mataoui

New tasks scheduling strategy for resources allocation in Cloud computing Environment

Scientific applications are very complex and need massive computing power and storage space. Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy for resource allocation for maximizing profit in cloud computing environment. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the resources exploitation. To show the interest of the proposed solution, Experiments results are conducted on a simulation data set.

A fuzzy link-based approach for XML information retrieval

The increasing amount of available XML documents collections has led to the emergence of new challenges in information retrieval field. Therefore, multiple sources of evidence were used to retrieve XML elements at different levels of granularity. XML information retrieval combines textual and structural information to perform different information retrieval tasks. In this paper, we propose a new approach exploiting link evidence to re-rank XML retrieval results. Our approach, based on fuzzy logic concepts, combines both content and link evidence for all retrieved XML elements. The combination process generates a new ranked list from the initial returned list. Experiment based on INEX 2007 Wikipedia collection showed improvement of the interpolated precision values.

Evidence-fuzzy system for human concurrent activities recognition

Activity recognition is an important task which can be applied to many real-life problems in pervasive computing. In this work, we propose concurrent activities recognition system based on two layers of inference. The first layer develops a framework for dealing with fusion system through merging different sources of information using evidence theory. The second layer proposes a decision framework under the fuzzy logic formalism. Our experimental results suggest that the fuzzy logic method for the plausibility combinations at the decision level is the best for activities in progress simultaneously but not necessarily involving the users interaction at the same time steps. It yields high accuracy of 79.7%, regarding experimental results.

Query expansion in XML information retrieval: A new approach for terms selection

Query Expansion is an important component for information retrieval systems. It makes possible the reformulation of the initial user query by adding new terms. In this paper, we propose a new approach for term selection in the relevance feedback process. This approach, based on Rocchio formula, is an adaptation to the XML information retrieval context. It can resolve two major problems specific to the XML information retrieval: the overlapping problem in the list of retrieved elements; and the problem of inclusion of irrelevant elements in the selection of new query terms.

Query optimization by attributes annotation in a database mediation system

The large amount of information available on autonomous, distributed and heterogeneous data sources requires a scheme of retrieval, selection and analysis, increasingly powerful to locate and extract precisely the desired information. The information integration systems offer an access to multiple data sources through queries on a global schema. A query posed on a database mediation system will be divided and sent to all the databases containing the attributes appearing in the query. But obviously, there are some databases which do not contain any answer. In this paper we propose to annotate numerical and enumerated attributes and to save these annotations in the mediator. By using these annotations, a query on the mediator will be sent only to the databases that surely contain answers.

Experiments on PageRank algorithm in the XML information retrieval context

In this paper we present two adaptations of the PageRank algorithm to collections of XML documents and the experimental results obtained for the Wikipedia collection used at INEX−1 2007. These adaptations to which we referred as “DOCRANK and TOPICAL_docrank” allow the re-rank of the results returned by the base run execution to improve retrieval quality. Our experiments are performed on the results returned by the three best ranked systems in the “Focused” task of INEX 2007. Evaluations have shown improvements in the quality of retrieval results (improvement of some topics is very significant, eg: topic 491, topic 521, etc.). The best improvement achieved in the results returned by the DALIAN2 university system (global rate obtained for the 107 topics of INEX 2007) was about 3.78%.