Seminar by Prof. Hendrik Blockeel on "Declarative data analysis"

03 March 2015

Abstract

With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated.  With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions.  In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem.  This combination of broad and deep knowledge is not sustainable.

The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system.  The user should be able to simply describe the problem, formulate a question, and let the system take it from there.  To achieve this, we need to find answers to questions such as:  what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them?  In this talk, I will discuss recent and ongoing research in this direction.  The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.

Bio

Hendrik Blockeel is a professor at the Computer Science department of KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands.  His research interests lie mostly in machine learning and data mining.  He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis.  He is an action editor for Machine Learning and serves on the editorial board of several other journals.  He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM.  He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.