Publications

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Books and Book Chapters

[B2] M. Zoghlami, S. Aridhi, M. Maddouri and E. Mephu Nguifo. An Overview of in Silico Methods for the Prediction of Ionizing Radiation Resistance in Bacteria. In: Ionizing Radiation: Advances in Research and Applications, Physics Research and Technology Series,  ISBN: 978-1-53613-539-8, 2018.

[B1] S. Aridhi, P. Lacomme, R. Phan. Bases de données NoSQL et Big Data : Concevoir des bases de données pour le Big Data. Editeur : Ellipses, ISBN : 2340002613, Décembre 2014.

 

Proceedings
[P1] S. Aridhi, J.A. Fernandes de Macêdo, E. Mephu Nguifo and K. Zeitouni. Proceedings of the Workshop on Large-Scale Time Dependent Graphs (TD-LSG 2017) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, September 18, 2017.

Papers in journals with reviewing committee

[J10] W. Inoubli, S. Aridhi, H. Mezni, M. Maddouri, E. M. Nguifo. An Experimental Survey on Big Data Frameworks. Future Generation Computer Systems, Elsevier, 2018. [Accepted] [Supplementary material] [IF = 3.99]

[J9] N. Karabadji, S. Beldjoudi, H. Seridi., S. Aridhi, W. Dhifli. Improving Memory Based User Collaborative Filtering with Evolutionary Multi-Objective Optimization. Expert Systems With Applications (ESWA), Elsevier, 98, pp.153-165, 2018. [IF = 3.92]

[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

[S4] 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. European Week of Astronomy and Space Science (EWASS 2017), Prague, Czech republic, 2017. [arXiv:1412.5513]

[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]

Thesis

[T2] S. Aridhi. Distributed frequent subgraph mining in the cloud. Ph.D. thesis , Blaise Pascal University, France, November 2013. [PDF]

[T1] S. Aridhi. Feature extraction methods in grid computing environments. Master’s thesis , University of Jendouba, Tunisia, March 2010.