Welcome to the homepage of the DIG seminar, which is the regular seminar of the DIG team of LTCI at Télécom ParisTech. The seminar features talks by members of the team and guests from other research groups, as well as discussions on topics of relevance to the team. Attendance is generally open to the public, feel free to contact us if you are interested in coming. Talks are held at Télécom ParisTech, 46 rue Barrault, Paris, France, métro Corvisart. You can contact Antoine Amarilli for any inquiry about the seminar.

The seminar has been formerly called "DBWeb seminar" and "IC2 seminar". You may also be interested in the LTCI Data Science Seminar, which is co-organized by DIG and S2A.

22 November 2017, 12:00, C47

Vwani Roychowdhury, UCLA
The Unreasonable Effectiveness of Data: A Scalable framework for "Understanding" Social Forums and Online Discussions

Abstract: As humans we interpret and react to the world around us in terms of narratives. At a basic level, a narrative is comprised of principal actors and entities, their interactions, and finally the decisions they make to reinforce and protect their interests. The primary question we address in this talk is whether a computer can automatically distill and create such narrative maps from millions of posts and discussions that happen in the online world. How much and which parts of the underlying narratives can be extracted via unsupervised statistical methods, and how much "humanness" needs to becoded into a computer? We provide a framework that uses statistical techniques to generate automated summaries, and show that when augmented with a small-size dictionary that encodes "humanness," the framework can generate effective narratives from a number of domains. We will present several sets of empirical results where millions of posts are processed to generate story graphs and plots of the underlying discussions.

Biography: Vwani Roychowdhury is a Professor of Electrical and Computer Engineering at University of California, Los Angeles (UCLA). He specializes in interdisciplinary work that deal with the modeling and design of information and computing systems, ranging from the physical, biological and engineered systems. He has done pioneering work in Quantum Computing, Nanoelectronics, Peer-to-Peer (P2P), social and complex networks, machine learning, text mining, artificial neural networks, computer vision, and Internet-Scale data processing. He has published more than 200 peer reviewed journal and conference papers, and co-authored several books. He has also cofounded several silicon valley startups, including www.netseer.com and www.stieleeye.com.

18 October 2017, 12:00, C47

Yun Sing Koh, University of Auckland
Using Volatility in Concept Drift Detection and Capturing Recurrent Concept Drift in Data Streams (slides)

Abstract: Much of scientific research involves the generation and testing of hypotheses that can facilitate the development of accurate models for a system. In machine learning the automated building of accurate models is desired. However traditional machine learning often assumes that the underlying models are static and unchanging over time. In reality there are many applications that analyse data streams where the underlying model or system changes over time. This may be caused by changes in the conditions of the system, or a fundamental change in how the system behaves. In this talk, I will present a change detector called SEED, and how we capture stream volatility. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. I will also present a drift prediction algorithm to predict the location of future drift points based on historical drift trends which we model as transitions between stream volatility patterns. Our method uses a probabilistic network to learn drift trends and is independent of the drift detection technique. I will then present a meta-learner, Concept Profiling Framework (CPF) that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour.

Biography: Yun Sing Koh is a Senior Lecturer at the Department of Computer Science, The University of Auckland, New Zealand. She completed her PhD at the Department of Computer Science, University of Otago, New Zealand in 2007. Her current research interest is in the area of data mining and machine learning, specifically data stream mining and pattern mining.

12 September 2017, 12:00, C47

Bob Durrant, University of Waikato
Random Projections for Dimensionality Reduction (slides)

12 July 2017, 12:00, C47

Amin Mantrach, Criteo Research
Deep Character-Level Click-Through Rate Prediction for Sponsored Search (slides)

31 May 2017, 12:00, C48

Quentin Lobbé, Télécom ParisTech
An exploration of web archives beyond the pages : Introducing web fragments (slides)
Mikaël Monet, Télécom ParisTech
Probabilistic query evaluation: towards tractable combined complexity (slides)

26 April 2017, 12:00, C47

Themis Palpanas, LIPADE, Paris Descartes University
Riding the Big IoT Data Wave: Complex Analytics for IoT Data Series (slides)

8 March 2017, 12:00, C47

Thomas Bonald, Télécom ParisTech
Community detection in graphs (slides)

27 February 2017, 12:00, C46

Laurent Decreusefond, Télécom ParisTech
Stochastic geometry, random hypergraphs, random walks (slides)

26 January 2017, 12:00, C47

Nofar Carmeli, Technion
Efficiently Enumerating Tree Decompositions (slides)

11 January 2017, 12:00, C47

Simon Razniewski, Free University of Bozen-Bolzano
Query-driven Data Completeness Assessment (slides)

14 December 2016, 12:00, C47

Fabian M. Suchanek, Télécom ParisTech
A hitchhiker’s guide to Ontology (slides)

23 November 2016, 12:00, C47

Ngurah Agus Sanjaya ER, Télécom ParisTech
Set of T-uples Expansion by Example (slides)
Qing Liu, National University of Singapore
Top-k Queries over Uncertain Scores (slides)

26 October 2016, 12:00, C46

Maria Koutraki, Université Paris-Saclay
Approaches towards unified models for integrating Web knowledge bases. (slides)

From November 2013 to September 2016

During this time, the DBWeb seminar was held as part of the IC2 group seminar. These seminars are listed on the IC2 seminar Web page.

10 September 2013, 14:00, C49

Antoine Amarilli
Taxonomy-Based Crowd Mining (slides)
Jean-Louis Dessalles
Relevance (slides)

14 January 2013, 10:00, B549

Vincent Lepage, Cinequant
Cinequant, datamining pour le monde réel
Jean Marc Vanel, Déductions SARL
EulerGUI, un outil libre pour le Web Sémantique et l'inférence

04 December 2012, 10:00, C017

Jean-Louis Dessalles
Why spend (so much) time on the social Web? A model of investment in communication
François Rousseau
Short talk and brainstorming on graph based text representation and mining

20 November 2012, 10:00, C017

Mohamed-Amine Baazizi
Static analysis for optimizing the update of large temporal XML documents
Christos Giatsidis
S-cores and degeneracy based graph clustering

6 November 2012, 10:00, C49

Jonathan Michaux, Télécom ParisTech
Interaction safety in Web service orchestrations (slides)
Georges Gouriten
Brainstorming on knowledge-based content suggestions on the social Web

16 October 2012, 10:00, C49

Clémence Magnien, Université Pierre et Marie Curie
Measuring, studying, and modelling the dynamics of Internet topology
Imen Ben Dhia
Evaluating reachability queries over large social graphs (slides)

2 October 2012, 10:00, C017

Idrissa Sarr, Université Cheikh Anta Diop
Dealing with the disappearance of nodes in social networks (slides)
Damien Munch
“Eating cake during a scientific talk:” Can we reverse-engineer natural language aspectual processing? (slides)

18 September 2012, 10:00, C017

Silviu Maniu
Context-Aware Top-k Processing using Views
Asma Souihli
Optimizing Approximations of DNF Query Lineage in Probabilistic XML (slides)

4 September 2012, 10:00, C017

Antoine Amarilli
Advances in holistic ontology alignment (slides)
Yannis Papakonstantinou, University of California, San Diego
Declarative, optimizable data-driven specifications of web and mobile applications