Models & Optimisation and Mathematical Analysis Journal


This journal concerns both the national and international scientific community and will be primarily focusing on Models and Optimisation of Systems. Systems will be utilized in different applications for example, Web technologies, Information Systems, Decision Systems, Embedded Systems, Control-command Systems and Real-time Systems. Space of journal is also dedicated to mathematical analysis like functional spaces, polynomial computing etc. MOMAjournal reserves a space for research work in the field of the history of mathematics and the didactics of mathematics.







Automatic Control of Multiple Pseudo- Relevance Feedback Pass

Mokhtari Chakir,  Debakla Mohammed,  Meftah Boudjelal, 

Résumé: Automatic query expansion (AQE) based on Pseudo Relevance Feedback (PRF) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose the study of the behavior of a set of features based on the query and the set of feedback documents in the aim to control automatically the convergence of the query expansion process to the pertinent set of documents. It is a new look for which is commonly known in IR community as prediction of query performance or query ambiguity. The results obtained show that is very interesting to use these features as good predictors for any PRF methods. Index Terms— Information retrieval, Automatic query expansion, pseudo-relevance feedback, query performance.

Mots clés: Information retrieval ; Automatic query expansion ; pseudo-relevance feedback ; query performance

Measurement and Social Network Analysis With Parallel Frequent Pattern Mining

محمد الأمين يرمس,  محمد رباح, 

Résumé: Social network analysis (‘SNA’) measures are a vital tool for understanding the behavior of networks and graphs. However, the huge amount of data generated by networks require new techniques to optimize the calculation. Many efficient algorithms based on graph calculation have been used in the last decades; most of them do not scale the large amount of data. In this paper, we propose a new approach based on parallel frequent pattern mining as an essential data-mining task, with a goal of calculating degree centralities. To take advantage of the computing power provided by HPCs, the use of a hybrid distribution of data (horizontal and vertical) seems necessary to reduce the computing time. The implementation show how is benefic to combine parallel and distributed programming techniques such as MPI with data mining tools.

Mots clés: Parallel Frequent Pattern Mining ; Social Network Analysis ; Degree Centralities ; HPC