Wenfei Fan
The University of Edinburgh

Querying Big Social Data

Big data poses new challenges to query answering, from computational complexity theory to query evaluation techniques. Several questions arise. What query classes can be considered tractable in the context of big data? How can we make query answering feasible on big data?
What should we do about the quality of the data, the other side of big data? This talk aims to provide an overview of recent advances in tackling these questions, using social network analysis as an example.

Marcelo Arenas
Pontificia Universidad Catolica de Chile

Querying Semantic Web Data with SPARQL (and SPARQL 1.1)

The Semantic Web is the initiative of the W3C to make information on the Web readable not only by humans but also by machines. RDF is the data model for Semantic Web data, and SPARQL is the standard query language for this data model. 
In the last ten years, we have witnessed a constant growth in the amount of RDF data available on the Web, which have motivated the theoretical study of some fundamental aspects of SPARQL and the development of efficient mechanisms for implementing this query language.  In this talk, we survey some of the main results about the theory of RDF and SPARQL, putting emphasis on some of the new features of the language (such as navigation and federation) that were recently introduced in SPARQL.

Christopher Ré
Stanford University

A Tutorial on Trained Systems: A New Generation of Data Management Systems?

A new generation of data processing systems, including web search, Google’s Knowledge Graph, IBM’s DeepQA, and several different recommendation systems, combine rich databases with software driven by machine learning. The spectacular successes of these trained systems have been among the most notable in all of computing and have generated excitement in health care, finance, energy, and general business. But building them can be challenging even for computer scientists with PhD-level training. This tutorial will describe some of the recent progress on trained systems from both industrial and academic systems. It will also contain a walkthrough of examples of trained systems that are in daily use by scientists in Geoscience, PaleoBiology, and English Literature.
Papers, software, virtual machines that contain installations of our software, links to applications that are discussed in this talk, and our list of collaborators are available from http://www.cs.wisc.edu/hazy. We also have a YouTube channel