GRAISearch

Use of Graphics Rendering and Artificial Intelligence for Improved Mobile Search Capabilities

GRAISearch is an Industry-Academia Partnerships and Pathways project (IAPP) aiming at transferring knowledge from Academia to Industries. It is also a support for training and career development of researchers (Marie Curie).

A Web startup, Tapastreet Ltd, as well as two academic institutions, INSA de Lyon (LIRIS UMR CNRS 5205) and Trinity College Dublin (TCD) for their knowledge resp. in Data science & Visualization, are involved.

The goal is to transfer recent technologies from research on video summarization, 3D scene reconstruction, data mining and event detection from social media, into the products of Tapastreet LTD.

This page is dedicated to INSA de Lyon workpackages:

Go to the GRAISearch Website


EU Seventh framework program      Marie Curie Actions


INSA     Tapastreet Ltd     Trinity College Dublin

Project description

WP3: Trajectory Mining

Mathematical methods and prototypes for mining and predicting local communities workflows through contextualized trajectory pattern mining applied to social network data from Tapastreet. For that, the following sub-tasks are considered: (i) community detection, (ii) places of interest (POI) characterization, and finally (iii) constrained-based mining of contextualized trajectories. The goal is to provide a valuable input for WP5 which entails making recommendations to social media end users w.r.t. their social trajectories and context, the recommendation could be a breaking news events (whose detection is handled in WP4) and is triggered by a fix on the persons location.

More Info soon

WP4: Event Detection

Develop a computing solution for (i) event detection and (ii) sources of trust identification in geolocalized social media data streams. It is a centre piece between WP3 and WP5: Geo-localized breaking events are detected and recommended to a user entering (or predicted to enter in) the corresponding location. For task (i), we design/use data-mining techniques to detect/predict unexpected events/patterns in data streams in presence of concept drift. We propose to model task (ii) as an original problem of temporal dependency discovery between some topics from different social media and bring an algorithmic solution through a graph-mining algorithm.

More Info soon

WP5: Recommendations

Identification of a recommendation strategy and the design of a recommender prototype for geo-located social media users in a geo-local context. Thanks to the results of WP3 and WP4, we make use of user trajectories stratified by demography, characterized points of interest, and trusted breaking news. Here we close the loop on WP3&4 who's learnings are applied and built into this recommender system prototype. The strategy will be based on real data supplied by Tapastreet, expertise from Tapastreet's machine learning department and knowledge and expertise from INSA de Lyon.

More Info soon

Results

Public deliverables

Most of the deliverables are unfortunately private. When possible, public ones are listed below.

Publications

  • A. Zimmermann, M. Kaytoue, M. Plantevit, C. Robardet, J.-F. Boulicaut. Profiling Users of the Velo'v Bike Sharing System. Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015 (MUD@ICML 2015), pages 63-64.
  • P. Houdyer, A. Zimmerman, M. Kaytoue, M. Plantevit, C. Robardet, J. Mitchell. Gazouille: Detecting and Illustrating Local Events from Geo-localized Social Media Streams in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Part III, LNAI 9286
  • M. Kaytoue, Y. Pitarch, M. Plantevit, C. Robardet. Triggering patterns of topology changes in dynamic graphs, in Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on ,pp.158,165, 17-20 Aug. 2014
    doi: 10.1109/ASONAM.2014.6921577 file

Education

  • Quentin Veyret: Student at INSA, Quentin performed his final 6 months internship at Tapastreet.
  • Rémi Domingues: Student at INSA in 4th year, Rémi performed a 3 months internship at Tapastreet.
  • Nicolas Buisson: Student at INSA in 4th year, Rémi performed a 3 months internship at Tapastreet.

Céline Robardet Professor

Céline is Professor in Computer Science at INSA de Lyon. Coordinates the GRAISearch project at INSA de Lyon. She is also the head of the team Data Mining and Machine Learning of the CNRS UMR 5205 LIRIS.

Mehdi Kaytoue Assistant Professor

Mehdi is Assistant Professor in Computer Science at INSA de Lyon. He will be seconded at Tapasteet for 8 months. Teaches AI, Semantic Web and data mining. Research in the DM2L group (CNRS 5205 LIRIS).

Marc Plantevit Assistant Professor

Marc is Assistant Professor in Computer Science at Université Lyon 1. He will be seconded at Tapasteet for 4 months. Teaches data mining and database theory. Research in the DM2L group (CNRS 5205 LIRIS).

Marian Scuturici Assistant Professor

Marian is Assistant Professor in Computer Science at INSA de Lyon. He will be seconded at Tapasteet for 2 months. Teaches algorithm theory and programming. Research in the Database group (CNRS 5205 LIRIS).

Pierre Houdyer Engineer, secondment

Pierre got his degree in computer science at IN'TECH INFO (ESIA). Lead developer at Tapastreet Ltd., he will join INSA de Lyon for secondment in October 2015.

Albrecht Zimmerann Recruit, PhD

Albrecht got his PhD in Computer Science in 2009. He joined the group from Feb. 2014 to August 2015 as the GRAISearch new recruit at INSA.

Contact

Pr. Céline Robardet
INSA de LYON Coordinator
http://liris.cnrs.fr/celine.robardet

GRAISearch - FP7-PEOPLE-2013-IAPP - Grant Agreement Number 612334 - Webmaster: Mehdi Kaytoue