[Defended PhD Thesis] Jacques Fize: Matching between massive and heterogeneous data : application to biodiversity data

PhD Thesis Defence: 12 novembre 2019, 13h30


  • Start Date:  October 2016
  • Defence : November 2019
  • University:  University of Montpellier
  • PhD School: I2S – Information, Structures, Systèmes
  • Field(s):  Computer Science, Data Sciences
  • Doctoral Thesis Advisors: Mathieu Roche, Cirad Tetis et Maguelonne Teisseire, Irstea Tetis
  • Funding: Cirad – Irstea
  • #DigitAg: Axes 4 & 5 – Challenges : cross-cutting subject, 8

Keywords: Data sciences, Big Data, biodiversity data


In scientific literature, few approaches exists for matching heterogeneous data in a generic way. As part of this thesis, propositions will be established in multidisciplinary ways of matching under 3 axes : thematic matching, spatial matching and temporal matching. The identification of pertinent descriptors will be established under these 3 axes using symbolic, statistic and semantic methods and the use of NLP methods for exploring textual data.


Contact:  jacques.fize [AT] cirad.fr​

Social Networks:  site – GitHubResearchGateLinkedIn

Communications / Publications

Fize J., Roche M., Teisseire M. (2018) Gemedoc: A Text Similarity Annotation Platform. In: Silberztein M., Atigui F., Kornyshova E., Métais E., Meziane F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science, vol 10859. Springer, Cham – https://doi.org/10.1007/978-3-319-91947-8_35

Jacques Fize, Mathieu Roche, Maguelonne Teisseire (2018). Matching heterogeneous textual data using spatial features 13th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-18) (to appear)