Diego Figueira is a CNRS researcher at LaBRI, Bordeaux. He has done postdocs at Edinburgh University and Warsaw University, and he obtained his PhD from ENS Cachan in 2010. His current area of work includes computational logic, database theory, finite model theory and infinite state systems.
This tutorial shows some fundamental results linking logic, complexity and query languages. It will serve as an introduction to some more advanced material, and it will not assume any prior knowledge on database theory or logic. It will cover basic definitions and results such as Relational Algebra, First-order logic, Conjunctive queries; data and query complexities; decidability and complexity of evaluation/satisfiability problems. The goal is to give a general idea of why studying logics is crucial for a deeper understanding of database query languages.
Wim Martens is a professor of computer science at University of Bayreuth, Germany. He did his PhD on Static analysis of XML Transformation and Schema languages under Prof. Frank Neven at University of Hasselt. His research interests include Foundations of data processing on the Internet, Formal Languages, Automata Theory, Logic, Complexity, and Database Theory.
Regular expressions are a fundamental ingredient that is common to many areas of computer science. They are an excellent connector between theory and practical applications. They have been around since the birth of the field and they are abundantly present in a wide range of applications today. In this lecture, we will review some basic properties of regular expressions, their applications (with an emphasis on data processing on the Web) and algorithms for processing them.
Guy Van den Broeck obtained his Ph.D. in Computer Science from KU Leuven, Belgium, in 2013. He was a postdoctoral researcher in the automated reasoning lab at the University of California, Los Angeles in 2014. He currently works as a postdoctoral researcher at KU Leuven and is supported by the Research Foundation-Flanders. His research interests are in artificial intelligence, machine learning, logical and probabilistic automated reasoning and statistical relational learning. His work was awarded the ECCAI AI Dissertation Award 2014, Scientific prize IBM Belgium for Informatics 2014, and Alcatel-Lucent Innovation Award 2009. He received the best student paper award at ILP 2011 and a best paper honorable mention at AAAI 2014. He co-organized the 4th International Workshop on Statistical Relational AI (StarAI) at AAAI 2014.
Statistical relational models combine aspects of first-order logic, databases and probabilistic graphical models, enabling them to represent complex logical and probabilistic relations between large numbers of objects. This tutorial will review various relational representations of uncertainty, including Markov logic, probabilistic databases, and probabilistic programming languages. We will discuss algorithms for learning these representations from data (in this case, from a relational database), as well as the typical applications. We will also study algorithms for probabilistic inference (i.e., drawing conclusions on the probability of events), and in particular lifted inference algorithms, which exploit the relational and logical structure present in these models.
Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is Full Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. He has published more than 200 papers in top venues and is co-author of the book Data Mining and Analysis - Fundamental Concepts and Algorithms published by Cambridge University Press in 2014. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance.
Data mining encapsulates the process of exploring and extracting insights from large collections of data, and is commonly applied for the purposes of scientific discovery, business intelligence, processing Web data, and so forth. This talk will introduce four high-level paradigms in data mining; namely, combinatorial, probabilistic, algebraic and graph-based paradigms. For each paradigm, a number of concrete data mining algorithms will be presented. The talk will be based on the book "Data Mining and Analysis" recently published by Cambridge Press (available online as a free download for personal use).