Most frequent Spatio-Temporal pattern mining and visualization in a large graph

Our research project

  • 2021 | | | | | | | | 2025


    Recent technological advances are leading to a massive production of spatio-temporal data that can be modelled by a graph. This is crucial to analyse, however, these data are very often characterised by a volume and a complexity never previously seen. Despite the dynamism of data mining research, few unsupervised methods are usable for extracting both original and relevant information due to their runtime. It is therefore essential to design and develop innovative data mining approaches that are capable of processing these large volumes of data while taking into account the spatial and temporal aspects. The objective of the MoS-T project is to develop innovative methods to extract the most frequent approximate patterns in a spatiotemporal graph, by exploiting deep neural networks, in order to provide a synthetic visualisation of the spatiotemporal phenomenon being studied.


    Spatio-temporal data, spatio-temporal graph, deep learning, frequent pattern mining, visualization, application to the medical and environmental fields

Organized events

  • Meetings

    • doneKick-off meeting : eventMarch 21, 2022 room in Illkirch-Graffenstaden
  • Workshops in relation with MoS-T

    • doneGAST – Spatial and Temporal Data Management and Analysis : eventJanuary ..., 2023 room in ...



Revues internationales avec comité de lecture et actes

Conférences internationales avec comité de lecture

Conférences nationales avec comité de lecture et actes

Open-source codes

À venir

Open-source datasets

À venir

CNRS ICube Unistra Institut Pascal