Nadjia. Benblidia

Work place: LRDSI Laboratory, Saad Dahlab University, Blida 1 /Computer Science Department, Blida, 09100, Algeria



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Vision, Pattern Recognition, Image Processing, Data Mining, Information Retrieval


Nadjia. Benblidia is Full Professor and Research Director at the Saad Dahlab University-Blida1 (USDB). She was awarded the Engineer degree in Computer science from USTHB-Algiers (Algeria), the magister (Postgraduate studies) and the PhD in electrical engineering & image processing from USDB, and the PhD in space sciences & environment at the University of Val de Marne (Paris XII). Actually, she is Doctoral School Head 'Data Science and Computing' at USDB. She is also the Director of the Research Laboratory for the Development of Computerized Systems at the University of Blida.

She used to be coordinator of several research projects like Tassili, FP7, AUF, Cnepru, PNR. She has organized several international conferences around Business Intelligence, Data Science & Computing. She is also a member of several scientific committees at the national and international level.

Her current research interests include image processing, computer vision, information retrieval, data mining, big data, machine learning and pattern recognition in various fields especially medical, satellite, security.

Author Articles
Study of Context Modelling Criteria in Information Retrieval

By Melyara. Mezzi Nadjia. Benblidia

DOI:, Pub. Date: 8 Mar. 2017

Whereas the majority of works and research about context-awareness in ubiquitous computing provide context models that make use of context features in a particular application, one of the main challenges these last years has been to come out with prospective standardization of context models. As for Information Retrieval, the lack of consensual Context Models represents the biggest issue. In this paper, we investigate the importance of good context modelling to overcome some of the issues surrounding a search task. Thus, after identifying those issues and listing and categorizing the modelling requirements, the objective of our research is to find correlations between the appreciations of context quality criteria taking into account the user dimension. Likewise, the results of a previous survey about search habits have been used such that many socio-demographic categories were considered and the Kendall's W evaluation performed together with the Friedman test provided very interesting results that encourage the feasibility of building large scale context models.

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