https://dspace.univ-guelma.dz/jspui/handle/123456789/2 Mon, 06 Apr 2026 23:53:49 GMT 2026-04-06T23:53:49Z https://dspace.univ-guelma.dz/jspui/handle/123456789/18980 An Introduction to Databases KHEBIZI, Ali Sun, 14 Dec 2025 00:00:00 GMT https://dspace.univ-guelma.dz/jspui/handle/123456789/18980 2025-12-14T00:00:00Z https://dspace.univ-guelma.dz/jspui/handle/123456789/18956 Indexation des mégadonnées basée IoT dans la chaîne d'approvisionnement alimentaire / Indexing Big Data Based on IoT in Food Supply Chain ZIAYA, Ilyas During the industrial revolution, ensuring food safety has become a critical concern, where the primary objective is to improve the quality of life and safety of citizens. This objective is challenged by the complexity of supply chain processes, which involve the collection of big data from multiple stages and actors, as well as inefficiencies in monitoring and traceability mechanisms that rely on technologies such as IoT and ICT. Alongside these technological factors, Blockchain technology has emerged as a promising solution, offering secure, tamper-proof, and transparent data management across supply chain stages. However, scalability issues hinder traceability query efficiency, as searches must be performed sequentially across blocks. This Ph.D thesis addresses this limitation by developing indexing techniques for big data in the food supply chain to optimize search processes and enhance traceability performance. The first contribution integrates the B-tree indexing technique with the blockchain by introducing a modified transaction structure that records fabrication time and computes per-block ranges. The B-tree structure manages these fabrication time ranges together with their corresponding block numbers, which are continuously updated with each new block until they cover the entire blockchain network. This approach enables the system to efficiently locate relevant blocks containing traceability data, allowing queries to be executed only within the identified blocks, thereby optimizing inter-block searches. The second contribution presents a novel blockchain-based traceability system that combines Natural Language Processing (NLP) with B+ tree indexing, where NLP interprets consumer text queries to identify target supply chain stages and the B+ tree index narrows searches to relevant blocks, ensuring accurate and efficient responses. The third contribution proposes the MerkleB+ tree (MB+ tree), a hybrid structure designed to optimize big data transaction management within each block, reducing linear scans while preserving the security guarantees of the Merkle tree. The simulation of these three systems was conducted using the Hyperledger Fabric framework, which supports food supply chain scenarios and enables the creation of a decentralized network based on our specifications. Performance testing under various network configurations showed that the three systems demonstrated significant improvements in query efficiency, providing faster and more reliable access to traceability information, as well as superior performance compared to existing methods Wed, 18 Feb 2026 00:00:00 GMT https://dspace.univ-guelma.dz/jspui/handle/123456789/18956 2026-02-18T00:00:00Z https://dspace.univ-guelma.dz/jspui/handle/123456789/18954 A Hybrid Approach for Improving Recommendation Systems CHEMLAL, Maroua Recommender systems have become ubiquitous on the internet. On most e-commerce and digital service platforms, users frequently encounter suggestions such as “customers who liked this product also liked that one.” The primary goal of these systems is to personalize the browsing experience, optimize the conversion rate (visitor → customer), and facilitate the retrieval of relevant information from massive volumes of data. However, despite their effectiveness, recommender systems face several challenges: (1) the lack of data truly adapted to users’ specific needs, (2) user disorientation during the search process, leading to inefficiency, and (3) difficulties in leveraging the richness of multimodal data collected from diverse sources such as social networks, reviews, interaction histories, and contextual information. Recent research has attempted to address these issues by integrating machine learning and deep learning techniques, as well as multi-criteria decision-making methods, to better capture user preferences, context, and external factors. To overcome these limitations, we propose in this work two complementary recommender systems. The first, the Smart Food and Restaurant Advisor (SFRA), employs Multi-Criteria Decision-Making (MCDM) methods to support healthier and more personalized food choices. Unlike traditional approaches that neglect nutritional and lifestyle dimensions, SFRA integrates user profiles enriched with dietary needs, preferences, and geographic data to deliver context-aware recommendations. Our system evaluates and ranks food and restaurant options by considering nutrition, ingredients, and contextual relevance, enabling users to make informed choices that balance taste with health goals. The second system is an innovative multimodal recommendation approach that exploits heterogeneous data sources (user preferences, spatio-temporal context and emotional) and leverages Graph Neural Networks (GNNs) to model complex relationships between users, items, and contextual factors. Furthermore, we introduce a multi-list recommendation mechanism, designed to provide multiple personalized suggestion lists based on different criteria (relevance, diversity, context), thereby enhancing personalization while reducing user disorientation. Mon, 16 Feb 2026 00:00:00 GMT https://dspace.univ-guelma.dz/jspui/handle/123456789/18954 2026-02-16T00:00:00Z https://dspace.univ-guelma.dz/jspui/handle/123456789/18949 Gestion des Projets & Entrepreneuriat Abdelmoumène, Hiba Sun, 14 Dec 2025 00:00:00 GMT https://dspace.univ-guelma.dz/jspui/handle/123456789/18949 2025-12-14T00:00:00Z