Publications

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Books

Papers in journals with reviewing committee

[J8] S. Aridhi, A. Montresor, Y. Velegrakis. BLADYG: A Graph Processing Framework for Large Dynamic Graphs. Big Data Research (BDR), Elsevier, 9(C), pp. 9-17, 2017. [Publisher’s version] [Supplementary material]

[J7] W. Dhifli, S. Aridhi, E. M. Nguifo. MR-SimLab: Scalable Subgraph Selection with Label Similarity for Big Data. Information Systems, Elsevier, 2017, 69, pp. 155-163, 2017. [Publisher’s version] [IF = 2.77]

[J6] N. Karabadji, H. Seridi., F. Bousetouane, W. Dhifli., S. Aridhi. An Evolutionary Scheme for Decision Tree Construction. Knowledge-Based Systems (KBS), Elsevier, 116, pp. 166-177, 2017. [Publisher’s version] [IF = 4.52]

[J5] S. Aridhi and E. Mephu Nguifo. Big Graph Mining: Frameworks and Techniques. Big Data Research (BDR), Elsevier, 6, pp. 1-10, 2016. [Publisher’s version]

[J4] S. Aridhi, H. Sghaier, M. Zoghlami, M. Maddouri and E. Mephu Nguifo. Prediction of Ionizing Radiation Resistance in Bacteria using a multiple instance learning model. Journal of Computational Biology (JCB), 23(1): pp. 10-20, 2016. [Publisher’s version] [Supplementary material] [IF = 1.03]

[J3] S. Aridhi, P. Lacomme, L. Ren, B. Vincent. A MapReduce-based approach for shortest path problem in large-scale networks. Engineering Applications of Artificial Intelligence, Elsevier, 41, pp. 151-165, 2015, ISSN 0952-1976. [Publisher’s version] [Supplementary material] [IF = 2.89]

[J2] S. Aridhi, L. d’Orazio, M. Maddouri and E. Mephu Nguifo. Density-based data partitioning strategy to approximate large-scale subgraph mining. Information Systems, Elsevier, 48, pp. 213-223, 2015, ISSN 0306-4379. [Publisher’s version] [Supplementary material] [IF = 2.77]

[J1] S. Aridhi, L. d’Orazio, M. Maddouri and E. Mephu Nguifo. Un partitionnement basé sur la densité de graphe pour approcher la fouille distribuée de sous-graphes fréquents. Technique et Science Informatiques, 33(9-10), pp. 711-737, 2014. [Publisher’s version]

International conferences/workshops with program committee

[IC14] W. Inoubli, S. Aridhi, H. Mezni, M. Maddouri and E. Mephu Nguifo. An Experimental Survey on Big Data Frameworks . Extremely Large Databases Conference (XLDB) 2017. (Lightning talk, poster)

[IC13] W. Inoubli, L. Almada, T.L. Coelho da Silva, G. Coutinho, L. Peres, R.P. Magalhaes, J.F. de Macedo,  S. Aridhi, E. Mephu Nguifo. A Distributed Framework for Large-Scale Time-Dependent Graph Analysis. Joint Workshop on Large-Scale Evolving Networks and Graphs in conjunction with ECML-PKDD 2017.

[IC12]  S. Aridhi, S.Z. Alborzi, M.S. Tabbone, M.D. Devignes and D.W. Ritchie. Neighborhood-Based Label Propagation in Large Protein Graphs. Function SIG@ISMB-ECCB 2017.

[IC11] S.Z. Alborzi, S. Aridhi, M.D. Devignes, R. Saidi, A. Renaux, M.J. Martin and D.W. Ritchie. Automatic Generation of Functional Annotation Rules Using Inferred GO-Domain Associations. Function SIG@ISMB-ECCB 2017.

[IC10] S. Aridhi, H. Sghaier, M. Zoghlami, M. Maddouri and E. Mephu Nguifo. Prediction of ionizing radiation resistance in bacteria using a multiple instance learning model. In Proceedings of the 2nd International Workshop on Advances in Bioinformatics and Artificial Intelligence: Bridging the Gap (BAI ’16) @ IJCAI’16, New York, USA. (Highlight paper) [Supplementary material]

[IC9] S. Aridhi, M. Brugnara,A. Montresor, Y. Velegrakis. Distributed k-core Decomposition and Maintenance in Large Dynamic Graphs. In Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS’16), pp. 161-168, Irvine, 2016. [Publisher’s version] [Supplementary material]

[IC8] S. Aridhi, A. Montresor, Y. Velegrakis. BLADYG: A novel block-centric framework for the analysis of large dynamic graphs. In Proceedings of the ACM Workshop on High Performance Graph Processing (HPGP’16) @ HPDC’16, pp. 39-42, Kyoto, Japan, 2016. [Publisher’s version] [Supplementary material]

[IC7] C. Sakouhi, S. Aridhi, A. Guerrieri, S. Sassi and A. Montresor. DynamicDFEP: A distributed edge partitioning approach for large dynamic graphs. In Proceedings of the 20th International Database Engineering & Applications Symposium (IDEAS’16), pp. 61-168, Montreal, Canada, 2016. [Publisher’s version]

[IC6] N. Karabadji, S. Aridhi, H. Seridi. A Frequent Closed Connected Subgraph Mining Algorithm in Unique Edge Label Graphs. In Proceedings of the 12th International Conference on Machine Learning and Data Mining (MLDM’16), pp. 43-57, New York, USA, 2016. [Publisher’s version]

[IC5] S. Aridhi, L. d’Orazio, M. Maddouri and E. Mephu Nguifo. Cost Models for Distributed Pattern Mining in the Cloud. In Proceedings of IEEE International Conference on Big Data Science and Engineering, IEEE, pp. 112-119, Helsinki, Finland, 2015. [Publisher’s version]

[IC4] S. Aridhi, B. Vincent, P. Lacomme and L. Ren. Shortest Path Resolution Using Hadoop. 10th International Conference on Modeling, Optimization and Simulation (MOSIM ’14), Nancy, France, 2014.

[IC3] S. Aridhi, H. Sghaier, M. Maddouri and E. Mephu Nguifo. Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model. In Proceedings of the 12th International Workshop on Data Mining in Bioinformatics (BioKDD ’13). ACM, New York, NY, USA, 18-24. [Publisher’s version] [Supplementary material]

[IC2] R. Saidi, S. Aridhi, M. Maddouri, E. Mephu Nguifo. Feature extraction in protein sequence classification : a new stability measure. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB ’12). ACM, New York, NY, USA, 683-689. [Publisher’s version]

[IC1] R. Saidi, S. Aridhi, M. Maddouri et E. Mephu Nguifo. Etude de stabilité de méthodes de sélection de motifs à partir des séquences protéiques. In Proceedings of « Conférence internationale sur l’extraction et la gestion des connaissances » (EGC ’10), 703-704, 2010. [Publisher’s version]

International symposiums with program committee

[S3] S. Aridhi, H. Sghaier, M. Maddouri et E. Mephu Nguifo. Domain knowledge-based model for phenotype prediction of ionizing-radiation-resistance in bacteria. ISCB Student Council Symposium 2014 meeting, Strasbourg 2014. [Publisher’s version]

[S2] S. Aridhi, L. d’Orazio, M. Maddouri and E. Mephu Nguifo. A novel MapReduce-based approach for distributed frequent subgraph mining. Machine Learning and Data Analytics Symposium (MLDAS), Doha 2014.

[S1] S. Aridhi, H. Sghaier, M. Maddouri et E. Mephu Nguifo. in silico phenotype prediction of ionizing-radiation-resistant bacteria by extraction of discriminative motifs. ISCB Student Council Symposium 2011 meeting, Vienna 2011. [Publisher’s version]

National conferences with program committee

[NC7] S. Aridhi, B. Vincent, P. Lacomme and L. Ren. Taking advantages of the MapReduce paradigm in one hadoop cluster for conception of efficient optimisation method. Workshop on Big Spatial Data, Orléans, France, 2014.

[NC6] S. Aridhi, L. d’Orazio, M. Maddouri et E. Mephu Nguifo. A Novel MapReduce-based approach for distributed frequent subgraph mining. 19ème congrès national sur la Reconnaissance de Formes et l’Intelligence Artificielle (RFIA’14), Rouen, France, 2014.

[NC5] S. Aridhi, L. d’Orazio, M. Maddouri et E. Mephu Nguifo. Un partitionnement basé sur la densité de graphe pour approcher la fouille distribuée de sous-graphes fréquents. Big Data Mining and Visualization, Paris, France, 2013. [Access online]

[NC4] S. Aridhi, L. d’Orazio, M. Maddouri et E. Mephu Nguifo. Fouille de sous-graphes fréquents dans les nuages. Journée sur le Décisionnel dans le Nuage (Cloud BI), Lyon, France, 2013.

[NC3] R. Saidi, W. Dhifli, S. Aridhi, M. Agier, G. Bronnier, D. Debroas, L. d’Orazio, F. Enault, S. Guillaume, E. Mephu Nguifo. Protein classification in the case of large and many-class datasets : A comparison with BLAST and BLAT. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Paris, France, 2011.

[NC2] S. Aridhi, R. Saidi, M. Maddouri et E. Mephu Nguifo. Etude paramétrique de la stabilité de méthodes de sélection de motifs à partir des séquences protéiques. 17ème Rencontres de la Société Francophone de Classification (SFC), Saint-Denis de la Réunion, France, 2010.

[NC1] R. Saidi, S. Aridhi, M. Agier, G. Bronnier, D. Debroas, L. d’Orazio, F. Enault, S. Guillaume, E. Mephu Nguifo. Functional prediction in the scope of large-scale multi-class learning. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Montpellier, France, 2010. [Publisher’s version]

Technical reports

[TR1] C. Arouri, E. Mephu Nguifo, S. Aridhi, C. Roucelle, G. Bonnet-Loosli, N. Tsopzé. Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction. Technical report, arXiv:1412.5513, 2014.