International Conference Papers

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Author: search.filters.author.Nouali, OmarEnd date: 2019

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Now showing 1 - 10 of 15
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    Combining Tags and Reviews to Improve Social Book Search Performance
    (Springer, 2018-08-15) Chaa, Messaoud; Nouali, Omar; Bellot, Patrice
    The emergence of Web 2.0 and social networks have provided important amounts of information that led researchers from different fields to exploit it. Social information retrieval is one of the areas that aim to use this social information to improve the information retrieval performance. This information can be textual, like tags or reviews, or non textual like ratings, number of likes, number of shares, etc. In this paper, we focus on the integration of social textual information in the research model. As it seems logical that integrating tags in the retrieval model should not be in the same way taken to integrate reviews, we will analyze the different influences of using tags and reviews on both the settings of retrieval parameters and the retrieval effectiveness. After several experiments, on the CLEF social book search collection, we concluded that combining the results obtained from two separate indexes and two models with specific parameters for tags and reviews gives good results compared to when using a single index and a single model.
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    Using ABE for Medical Data Protection in Fog Computing
    (2019-05-03) Krinah, Abdelghani; Challal, Yacine; Omar, Mawloud; Nouali, Omar
    Fog is an extension of the cloud computing paradigm, developed to fix the clouds latency, especially for applications requiring a very short response time, such as e-health applications. However, these applications also require a high level of data confidentiality, hence the need to apply appropriate encryption techniques, which can ensure security needs, while respecting the characteristics of the infrastructures devices. In this article, we will focus on ABE encryption, through the work done to study its applicability in the cloud and the Internet of things, as well as the improvements that can be made to adapt it to the fog computing environment.
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    Toward a new Backpressure-based framework to Enhance Situational Awareness in Disaster Response
    (IEEE ICT-DM-2017, 2017-12-11) Kabou, Abdelbaset; Nouali-Taboudjemat, Nadia; Nouali, Omar
    Crises generate intense need to communication not just as a panic reaction to crisis, but also due the critical need for communication in order to better coordinate during response activities. In the afterward of a disaster, the lack of resources to handle this increase of data, due to the fragility of network infrastructures, leads to network congestion or overload. The results is that critical data are prevented from reaching decision makers, which has a direct impact on situational-awareness. To overcome this problem, we propose a new cross layer architecture for Wireless Mesh Network with a twofold objective: one, to include a filtering system able to identify the most critical data and two, to propose a routing layer with the capacity to prioritize these data while ensuring the stability and throughput optimality of the whole network. The proposed solution combines both by proposing an adjustable and fully distributed version of the high throughput efficient Backpressure routing protocol, with a geolocation and role-based filtering and prioritizing system. Both components collaborate in a way to identify and send most critical data, using a lower end-to-end delay, without however starving less critical data. Extensive experiments, using NS-3 simulator, are used to validate the proposal and confirm the high impact of the introduced ideas
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    Accelerated Search over Encrypted Cloud Data
    (IEEE, 2017-02-13) Boucenna, Fateh; Nouali, Omar; Dabah, Adel; Kechid, Samir
    Companies and other organizations such as hospitals seek more and more to enjoy the benefits of cloud computingin terms of storage space and computing power. However, outsourced data must be encrypted in order to be protected againstpossible attacks. Therefore, traditional information retrieval systems (IRS) are no longer effective and must be adapted in order towork over encrypted cloud data. In addition, in order to providethe ability to search over an encrypted index, we use the vectormodel to represent documents and queries which is the most usedin the literature. During the search process, the query vectormust be compared with each document vector which is a time consuming process since the data collection is generally huge.Consequently, the search performance is degraded and the searchprocess is too slow. To overcome this drawback, we proposethe use of High Performance Computing (HPC) architecturesto accelerate the search over encrypted cloud data. Indeed,we propose several techniques that take benefit from Graphics Processing Unit (GPU) and computer cluster architectures by distributing the work between different threads. In addition,in order to get the best performance, we design our solutionsso that they can process several queries simultaneously. Theexperimental study using 400.000 documents demonstrates theefficiency of our proposals by reaching a speed-up around 46x.
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    Concept-based Semantic Search over Encrypted Cloud Data
    (2016-04-23) Boucenna, Fateh; Nouali, Omar; Kechid, Samir
    Cloud computing is a technology that allows companies and individuals to outsource their data and their applications. The aim is to take advantage from the power of storage and processing offered by such technology. However, in order to preserve data privacy, it is crucial that all data must be encrypted before being outsourced into the cloud. Moreover, authorized users should be able to recover their outsourced data. This process can be complicated due to the fact that data are encrypted. The traditional information retrieval systems only work over data in the clear. Therefore, dedicated information retrieval systems were developed to deal with the encrypted cloud data. Several kinds of search over cloud data have been proposed in the literature such as Boolean search, multi-keyword ranked search and fuzzy search. However, the semantic search is little addressed in the literature. In this paper, we propose an approach called SSE-S that take into account the semantic search in the cloud by using Wikipedia ontology to understand the meaning of documents and queries with maintaining the security and the privacy issues.
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    Semantic Annotations and Context Reasoning to Enhance Knowledge Reuse in E-learning
    (2013) Boudebza, Souad; Azouaou, Faiçal; Berkani, Lamia; Nouali, Omar
    We address in this paper the need of improving knowledge reusability within online Communities of Practice of E-learning (CoPEs). Our approach is based on contextual semantic annotations. An ontological-based contextual semantic annotation model is presented. The model serves as the basis for implementing a context aware annotation system called “CoPEAnnot”. Ontological and rule-based context reasoning contribute to improving knowledge reuse by adapting CoPEAnnot’s search results, navigation and recommendation. The proposal has been experimented within a community of learners.
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    Using Clustering and Modified Classification algorithm without a learning corpus for automatic text summarization
    (2013-02-05) Aries, Abdelkrime; Oufaida, Houda; Nouali, Omar
    In this paper we describe a modified classification method destined for extractive summarization purpose. The classification in this method doesn’t need a learning corpus; it uses the input text to do that. First, we cluster the document sentences to exploit the diversity of topics, then we use a learning algorithm (here we used Naive Bayes) on each cluster considering it as a class. After obtaining the classification model, we calculate the score of a sentence in each class, using a scoring model derived from classification algorithm. These scores are used, then, to reorder the sentences and extract the first ones as the output summary. We conducted some experiments using a corpus of scientific papers, and comparing our system to another system which is UNIS system. Also, we experiment the impact of clustering threshold tuning, on the resulted summary, as well as the impact of adding more features to the classifier. We found that this method is interesting, and gives good performance, and the addition of new features (which is simple using this method) can improve summary’s accuracy.
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    Deal with multiplicity and diversity of relevance factors in XML retrieval
    (2010-07-05) Bal, Kamal; Nouali, Omar
    In this paper, we are interested in content oriented XML information retrieval whose aim is to retrieve not a list of relevant documents, but only fragments of document (XML element) relevant to the user information need. Retrieved XML elements must not only contain relevant information but also be at good level of granularity. The coexistence of content and structural information in XML documents makes that multiple and diverse relevance sources condition the selection and the rank of relevant XML element. It is admitted that the consideration of several relevance sources will surely improve quality of results. However, actual XML retrieval approaches consider just some relevance sources and neglect others due to relevance sources heterogeneity. In this paper, we try to identify and classify theses relevance sources and to propose a way to exploit multiple and diverse relevance sources in retrieval process.
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    Towards a Dynamic Evacuation System for Disaster Situations
    (IEEE, 2014-03) Benssam, Ali; Bendjoudi, Ahcène; Yahiaoui, Saïd; Nouali-Taboudjemat, Nadia; Nouali, Omar
    Medical evacuation is one of the most important modules in the emergency plans activated during disaster situations. It aims at evacuating victims to the most appropriate health-care facilities. Evacuation plans were for a long time performed approximatively and passively rather than optimally and proactively between the disposal site and the targeted hospital and they often lacked visibility on the evolution of the events that may change data and leading to a revision of the plans. However, thanks to the proliferation of information and communication technologies (ICTs) in all aspects of life, the evacuation operations in disaster situations had known a great enhancement. In fact, critical operations such as real-time monitoring of the state of resources used during the evacuation process, detecting the occurring changes and reflecting them on the global process to provide dynamic and optimal evacuation plans become possible. In this paper, we propose a framework for dynamic evacuation operations in disaster situations. We design a system that takes into consideration the above challenges such as detecting changes and using them in an intelligent way to enable dynamic, optimal and up-to-date evacuation plans. The provided prototype is called DEvacuS (Dynamic Evacuation System).
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    Improvement of a retrieval and f iltering systems by an automatic multi words extraction tool
    (Ghassan Issa, General Chair, 2006-04) Nouali, Omar; Krinah, Abdelghani
    The role of all retrieval systems consists in finding documents (or document excerpts) that best answer an information need. This need, whether precise and instant (a request) or general and long term (a profile), is most often expre ssed by a list of entities called terms. The goal of this article is to present an automatic mu lti words recognition and extraction tool, compiled from text extracts, in order to build a thesaurus, or terminological basis, useful by search engines and filtering systems in order to boost their performances.