Anne LAURENT

Directrice du Centre de compétences HPC@LR

Bâtiment : Bâtiment 5, Étage 3

Bureau : 03/162

Téléphone : +33 4 67 41 85 85

Fax : +33 4 67 41 85 00

Email : Anne.Laurent@lirmm.fr

Statut : Permanent

Anne Laurent is Full Professor at the LIRMM lab. As a member of the Open Data Research group, she works on data mining, sequential pattern mining, tree mining, both for trends and exceptions detections and is particularly interested in the study of the use of fuzzy logic to provide more valuable results, while remaining scalable.
Anne Laurent heads the Computer Science Department at Polytech’Montpellier Engineering School at the University of Montpellier 2, which prepares a 5-year Masters in computer science and management.

   

Publications

Teaching

Polytech'Montpellier - Computer Science and Management

  • IG3: C Programming
  • IG4: Computer Networks, Advanced Databases, Relational-Object Databases
  • IG5: Data Warehouses, Data Mining

Master in Computer Science

Master Research:

  • Advanced Data Mining
  • Handling Imperfect Data
  • Decisional Information Systems

Master IC:

  • Data Mining
  • OLAP

Scientific Collaborations

EXPEDO STIC-Asia Project

This project aims at analysing huge volumes of historical data. Existing methods are not appropriate as they are mostly based on reporting (no data mining to automatically mine relevant patterns), and no user-friendly query-based navigation exists. The targets of EXPEDO are:

  •  to consider data mining techniques,
  •  to build a prototype,
  •  to consider real data sets (e.g. agricultural data).

 Partners: Cergy-Pontoise (ETIS), Tours (LI), Paris-Sud (LRI), Malaysia HELP University College (HELP UC), Indonesia Institut Teknologi Bandung (ITB), Stikom Bali, Pakistan Pakistan Science Foundation (PSF)

MIDAS - ANR Masses de données

The MIDAS project is a ‘Recherche Fondamentale’ developing and demonstrating new methods the following scientific challenges related to the construction of summaries:
 • Summaries are built from infinite streams size;
 • The construction of summaries must be incremental (done ‘on the fly’);
 • The amount of CPU used to process compatible with the arrival rate of the elements;
 • The summaries must cover the whole past part of the history of a stream.
 
 Partners: EDF R&D, FT R&D, ENST, INRIA-Axis, LGI2P, CEREGMIA

LIP6-UPMC

B. Bouchon-Meunier, MJ Lesot, M. Rifqi (Département  DAPA - Equipe MALIRE)

LIG-UJF

A. Termier

LIRMM

Industrial Collaborations

IBM

EDF

SQLI

Satin-IP

Namae Concept- Accompagnement & expertise linguistiques en dépôt de noms

New-ID / Phone-Advance

Mots-clés

Databases, [Fuzzy-]Data Mining, Data Warehouses, Fuzzy Logic, Sequential Patterns, Fuzzy Summaries, OLAP, [Fuzzy-]OLAP Mining, [Fuzzy-]Tree Mining, Outliers.

Dernière mise à jour le 05/04/2019