Sabeur Aridhi

Sabeur ARIDHI

I am a Postdoctoral researcher at the Machine Learning for Big Data Group at the Department of Computer Science at Aalto University in Finland. Before joining Aalto University, I was postdoctoral researcher and member of the Database and Information Management Group (dbTrento) at the University of Trento (Italy).

My research area includes Big Data Management & Analytics, Cloud Computing, Data Mining and Bioinformatics.

I hold a PhD in computer science from the Laboratory of Informatics, Modeling and Optimization of Systems (LIMOS) at the Blaise Pascal University (France)


E-mail : sabeur.aridhi@gmail.com
Address : Aalto University P.O. Box 15400, 00076, Helsinki, Finland
Phone : +358 50 4699 028 (Office)
Office : A326 (Konemiehentie 2)

Research

Research Topics

Big Data Analytics
Data mining / machine learning
Cloud computing
Distributed Systems
Bioinformatics

PhD Thesis

Topic: Distributed Subgraph Mining in the Cloud. [Access online]
Supervisors:
Engelbert Mephu Nguifo - Blaise Pascal University, France
Laurent d'Orazio - Blaise Pascal University, France
Mondher Maddouri - University of Manouba, Tunisia

Publications

Books

International journals with reviewing committee

  • 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 = 1.737]
  • 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 = 2.207]
  • 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 = 1.456]
  • 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

  • S. Aridhi, M. Brugnara,A. Montresor, Y. Velegrakis. Distributed k-core Decomposition and Maintenance in Large Dynamic Graphs. ACM International Conference on Distributed and Event-Based Systems (DEBS), Irvine, 2016. (Accepted)
  • S. Aridhi, A. Montresor, Y. Velegrakis. BLADYG: A novel block-centric framework for the analysis of large dynamic graphs. High Performance Graph Processing workshop @ HPDC 2016, Kyoto, Japan, 2016. (Accepted)
  • N. Karabadji, S. Aridhi, H. Seridi. A Frequent Closed Connected Subgraph Mining Algorithm in Unique Edge Label Graphs. International Conference on Machine Learning and Data Mining (MLDM), New York, USA, 2016. (Accepted)
  • 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]
  • 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. [PDF]
  • 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]
  • 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]
  • 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

  • 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]
  • 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.
  • 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

  • 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.
  • 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.
  • 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]
  • 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.
  • 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. [PDF]
  • 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. [PDF]
  • 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

  • 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.

Reviewer

Reviewing committees

  • Journals: Pattern Recognition Letters
  • Conferences and Workshops: EDBT 2015, KDIR 2015, BDMA 2014, ICPR 2014, EDA 2013, EDA 2014

Program committees

  • International ESWC 2015 Workshop: Surfacing the Deep and the Social Web (SDSW) 2015
  • International Conference on Computer Science and its Applications (CIIA) 2015 - Computational intelligence track

Seminars

Cost models for distributed pattern mining in the cloud [Access online]

  • Date: 2014-02-11, Time: 14:30-16:30
  • Location: Povo1, Ofek room, DISI department, University of Trento, Italy
  • Event: DBTrento group meeting

Distributed Graph Mining in the Cloud

  • Date: 2014-10-08, Time: 11:00-13:00
  • Location: Povo2, Levico room, DISI department, University of Trento, Italy
  • Event: DBTrento group meeting

Mining Large Datasets: Case of Mining Graph Data in the Cloud [Access online]

  • Date: 2014-05-16, Time: 10:00-12:00
  • Location: LIRIS, Claude Bernard Lyon 1 University, France
  • Event: Big Data Forum 2014

Students

Master

  • Chayma Sakouhi, 2014/2015, co-advising with Alberto Montresor and Salma Sassi: Edge-based graph partitioning of large dynamic graphs.
  • Cyrine Arouri, 2013/2014, co-advising with Engelbert Mephu Nguifo, Cécile Roucelle and Gaelle Bonnet: Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction.

Curriculum Vitae in English or in French

Software

MRGRAM

MRGRAM is a MapReduce-based framework for distributed frequent subgraph mining. MRGRAM is built on top of Hadoop and it provides a data partitioning technique that considers data characteristics. It uses the densities of graphs in order to partition the input data.
Please refer to the paper to find all the information about algorithms.

ORHadoop

ORHadoop is a distributed framework for shortest path resolution in large-scale networks.
Please refer to the paper to find all the information about algorithms.

MIL-ALIGN

MIL-ALIGN is used to predict bacterial ionizing radiation resistance using a multiple-instance learning model. It runs on a Windows or a UNIX platform that contains a Java Runtime Environment (JRE).

PREFON_META

PREFON_META is used to predict functional domains of metagenomics data.

Teaching

    My Massive Open Online Course (MOOC) on Big Data for Smart Cities is available here!

  • Provider: edX
  • University / entity: IEEEx
  • Instructors: Dr. Sabeur Aridhi and Pr. Yannis Velegrakis
  • Course length: 4 Weeks

    2015-2016 - Department of Computer Science - Aalto University

  • Convex Optimization for Big Data (Lecture, Master 1)
  • 2013-2014 - ISIMA - Blaise Pascal University

  • Databases ~ Fondements des bases de données (Lecture, ISIMA 2)
  • Advanced object programming ~ Programmation objets avancée (Lab works, ISIMA 2)
  • C programming language ~ Langage C (Lab works, ISIMA 1)
  • 2013-2014 - UFR ST - Blaise Pascal University

  • Data mining ~ Fouille de données et Extraction de connaissances (Lab works, Master degree: 2nd year)
  • Service Oriented Architecture and emerging technologies ~ Architecture orientée services et technologies émergentes (Lab works, Master degree: 2nd year)
  • 2012-2013 - UFR ST - Blaise Pascal University

  • Data mining ~ Fouille de données et Extraction de connaissances (Lab works, Master degree: 2nd year)
  • Service Oriented Architecture and emerging technologies ~ Architecture orientée services et technologies émergentes (Lab works, Master degree: 2nd year)
  • Functional programming ~ Programmation fonctionnelle (Lab works, Bachelor's degree: 1st year)
  • 2011-2012 - UFR ST - Blaise Pascal University

  • Data Structures and Algorithms ~ Algorithme et structures de données (Lab works, Bachelor's degree: 1st year)
  • 2011-2012 - FSJ - University of Jendouba

  • Algorithmic complexity ~ Complexité Algorithmique (Lecture-Tutorials, Bachelor's degree: 3rd year)
  • Compilation technique ~ Techniques de compilation (Tutorials, Bachelor's degree: 3rd year)
  • Data Structures and Algorithms ~ Algorithme et structures de données (Tutorials, Bachelor's degree: 1st year)
  • 2010-2011 - FSJ - University of Jendouba

  • Algorithmic complexity ~ Complexité Algorithmique (Tutorials, Bachelor's degree: 3rd year)
  • Compilation technique ~ Techniques de compilation (Tutorials, Bachelor's degree: 3rd year)
  • Operating Systems and System Programming ~ Système d'exploitation (Tutorials, Bachelor's degree: 1st and 2nd year)

Other

Affiliations

  • Department of Information Engineering and Computer Science (DISI), University of Trento (UniTN), Italy.
  • Laboratory of Informatics, Modeling and Optimization of Systems (LIMOS), CNRS UMR 6158 Blaise Pascal University, France.
  • ACM student Member.

Events

  • Participation to the Franco-German Summer School on Cloud Computing, July 17-22, Munich, Germany 2011.
  • Participation to the CNRS Thematic School: GRISBI: Calcul Scientifique sur Grille pour la Bioinformatique, Sept. 27 - Oct. 01, 2010, Roscoff, France 2010.
  • Training: Initiation on using Grid Computing environments AUVERGRID, June 22-26, Clermont Ferrand, France 2009.

Honors and Awards

  • 2015: Award from the IEEE Smart Cities Initiative in the city of Trento, Italy.
  • 2009: First prize awarded by Jendouba Governorate (Tunisia) on the occasion of the national Science Day.
  • 2008: First prize awarded by Bizerta Governorate (Tunisia) on the occasion of the national Science Day.
  • 2006: First prize awarded by Bizerta Governorate (Tunisia) on the occasion of the national Science Day.