The need for advanced data analysis now pervades all areas of science, industry and services. A wide variety of theory and techniques from statistics, data mining, and machine learning is available. Addressing a concrete question or problem in a particular application domain requires multiple non-trivial steps: translating the question to a data analysis problem, selecting a suitable approach to solve this problem, correctly applying that approach, and correctly interpreting the results. In this process, specialist knowledge on data analysis needs to be combined with domain expertise. As data analysis becomes ever more advanced, this becomes increasingly difficult. In an ideal world, data analysis would be declarative and domain-oriented: the user should be able to state the question, rather than describing a solution procedure, and the software should decide how to provide an answer. The user then no longer needs to be, or hire, a specialist in data analysis for every step of the knowledge discovery process. This would make data analysis easier, more efficient, and less error-prone. In this talk, I will discuss contemporary research that is bringing the state of the art in data analysis closer to that long-term goal. This includes research on inductive databases, constraint-based data mining, probabilistic-logical modeling, and declarative experimentation.
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.