| An experimental survey on big data frameworks W Inoubli, S Aridhi, H Mezni, M Maddouri, E Mephu Nguifo Future Generation Computer Systems 86, 546-564, 2018 | 164 | 2018 |
| A comparative study on streaming frameworks for big data W Inoubli, S Aridhi, H Mezni, M Maddouri, EM Nguifo VLDB 2018-44th International Conference on Very Large Data Bases: Workshop …, 2018 | 42 | 2018 |
| A DSS based on GIS and Tabu search for solving the CVRP: The Tunisian case S Faiz, S Krichen, W Inoubli The Egyptian Journal of Remote Sensing and Space Science 17 (1), 105-110, 2014 | 31 | 2014 |
| Big data frameworks: A comparative study W Inoubli, S Aridhi, H Mezni, A Jung CoRR, abs/1610.09962, 2016 | 20 | 2016 |
| Expect: EXplainable Prediction Model for Energy ConsumpTion A Mouakher, W Inoubli, C Ounoughi, A Ko Mathematics 10 (2), 248, 2022 | 14 | 2022 |
| A distributed and incremental algorithm for large-scale graph clustering W Inoubli, S Aridhi, H Mezni, M Maddouri, E Mephu Nguifo Future Generation Computer Systems 134, 334-347, 2022 | 11* | 2022 |
| Distributed scalable association rule mining over covid-19 data M Shahin, W Inoubli, SA Shah, SB Yahia, D Draheim International Conference on Future Data and Security Engineering, 39-52, 2021 | 8 | 2021 |
| Pregnancy Associated Breast Cancer gene expressions: new insights on their regulation based on Rare Correlated Patterns B Souad, I Wissem, BY Sadok, D Gayo IEEE/ACM transactions on computational biology and bioinformatics, 2020 | 6 | 2020 |
| DGL4C: a deep semi-supervised graph representation learning model for resume classification W Inoubli, A Brun Workshop on Recommender Systems for Human Resources@ RecSys, 2022 | 1 | 2022 |
| A distributed and incremental algorithm for large-scale graph clustering W Inoubli, S Aridhi, H Mezni, M Maddouri, EM Nguifo | 1 | 2020 |
| Un algorithme distribué pour le clustering de grands graphes W Inoubli, S Aridhi, H Mezni, M Maddouri, EM Nguifo 20ème édition de la conférence francophone" Extraction et gestion des …, 2020 | 1 | 2020 |
| A distributed framework for large-scale time-dependent graph analysis W Inoubli, L Almada, TLC da Silva, G Coutinho, L Peres, RP Magalhaes, ... ECML PKDD 2017-TD-LSG 2017: workshop Advances in Mining Large-Scale Time …, 2017 | 1 | 2017 |
| Large-scale knowledge graph representation learning M Badrouni, C Katar, W Inoubli Knowledge and Information Systems, 1-21, 2024 | | 2024 |
| Un algorithme d'apprentissage profond et semi-supervisé basé sur la représentation de graphes pour la classification des CV W Inoubli, A Brun 24ème conférence francophone sur l'Extraction et la Gestion des …, 2024 | | 2024 |
| Systematic literature review on Heterogeneous Information Networks K Ammar, W Inoubli, S Zghal, EM Nguifo | | 2023 |
| Graph Representation Learning for Recommendation Systems: A Short Review K Ammar, W Inoubli, S Zghal, E Mephu Nguifo International Conference on Information and Knowledge Systems, 33-48, 2023 | | 2023 |
| DGCN: LEARNING GRAPH REPRESENTATIONS VIA DENSE CONNECTIONS A PREPRINT K Abidi, W Inoubli, EM Nguifo | | 2023 |
| Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs. K Ammar, W Inoubli, S Zghal, A Borji, EM Nguifo Procedia Computer Science 225, 1104-1113, 2023 | | 2023 |
| DGCN: Learning Graph Representations Via Dense Connections K Abidi, W Inoubli, EM Nguifo Procedia Computer Science 225, 951-960, 2023 | | 2023 |
| Analysis and Mining of Large Dynamic Graphs: case of graph clustering W Inoubli Université de Tunis El Manar (Tunisie), 2021 | | 2021 |
Engelbert Mephu Nguifoprofesseur d'informatique, University Clermont Auvergne在 isima.fr 的电子邮件经过验证
