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
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
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
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
Most of the deliverables are unfortunately private. When possible, public ones are listed below.
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 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 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 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 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 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.
Pr. Céline Robardet
INSA de LYON Coordinator