[bull-ia] EDBT Summer School 2019 – Call for Applications

****************************************************************************

Call for Applications

14th International EDBT Summer School on

Extracting Hidden Knowledge from Heterogeneous Massive Data

2 – 6 September 2019

Saint Germain aux Monts d’Or, in the surroundings of Lyon (France)

https://edbtschool2019.liris.cnrs.fr

****************************************************************************

** Important dates **

Deadline for application: 31 May 2019.

Notification of acceptance: 15 June 2019.

Deadline for registration: 1 July 2019.

Summer school: 2 September – 6 September 2019 (arrival on Sunday September 1 evening).

** Overview **

The theme for the 14th EDBT Summer School is « Extracting Hidden Knowledge from Heterogeneous Massive Data ». The five-day school will be held from 2 – 6 September 2019 in St Germain au Mont D’Or​,  a town in the surroundings of Lyon, France. The school is hosted in the Domaine des Hautannes, which is located in a park of 2.5 hectares featuring a beautiful classified forest and revolves around a bourgeois house and its outbuildings.

The 2019 summer school follows the successful tradition of previous EDBT schools: stimulating lectures by leading researchers in the field, competitive research challenges to build upon and extend the lectures, and a lively scientific and social program.

We invite advanced graduate students, postdocs, and other academic and industrial researchers interested in an intensive study of the state-of-the-art techniques for managing data heterogeneity and extracting knowledge from such data to participate in the summer school. Application and registration details can be found on the school website: https://edbtschool2019.liris.cnrs.fr

Participants will receive on request a certificate stating that the course activity is equivalent to 2 ETCS. Some universities may accept such a certificates towards the coursework required for their PhD program. The decision on the recognition of the ECTS depends exclusively on the participant’s institute.

** Speakers and lecture topics **

Aristides Gionis, with Polina Rozenshtein. Aalto University – Finland. “Mining Temporal Networks”.

Ihab Ilyas. University of Waterloo – Canada. “Data Curation and Machine Learning”.

Benny Kimelfeld. Technion – Israel. “Information extraction ​ with ​ document spanners and Big data analytics with logical formalisms”.

Markus Krötzsch. University of Dresden – Germany. “Ontologies and Ontology Reasoning for Property Graphs”.

Renée Miller. Northeastern University – USA “Data Curation and Integration at the time of Open Data”.

Erhard Rahm, with Eric Peukert . University of Leipzig – Germany. “Entity Resolution for Large-Scale Data”.

** Motivation **

One of the fundamental characteristics of Big Data is variety, which arises from data having different formats and structures, but also from the heterogeneity of applications and users involved in the data processing pipeline. It is crucial to tame data variety to help the design, the study and the development of the next generation data management and analytics tools.

The topic of this summer school covers the many different data management challenges concerning variety that need to be seen under new lenses and call for a radical rethinking due to the unprecedented scale of Big Data. These problems span from information and pattern extraction to information integration, data curation, entity resolution, query answering, ontology management and learning. One of the particular novelties of this edition is the fact that lectures on these topics will be given under different perspectives, at the intersection of the Database community with other communities, such as Knowledge Representation, Artificial Intelligence and Machine Learning. This will allow PhD students and other scientists to enhance their knowledge and have fruitful interactions with other fields.

The school will also touch certain issues related to the Big Data veracity since the collection of data from many different heterogeneous sources, unavoidably creates data quality challenges that have to be handled in orchestration with the heterogeneity aspects.

** Chairs **

Angela Bonifati. Lyon 1 University (France). angela.bonifati@univ-lyon1.fr

Stijn Vansummeren. University of Brussels (Belgium). stijn.vansummeren@ulb.ac.be

Yannis Velegrakis. Utrecht University (The Netherlands). i.velegrakis@uu.nl

Please contact the chairs for any questions or comments.

** Website **

Further details can be found on the school website:

https://edbtschool2019.liris.cnrs.fr