Abstract: Due to the pervasiveness of the cloud today, due to the fact that data is becoming a commodity similar to electricity, that personal data is frequently exchanged for a “free” service, and as data plays a fundamental role in Internet of Things contexts and applications, the question arises of whether a price tag should be attached to data and, if so, what it should say. More fundamentally, the question is whether “price” as a single piece of information on the value or quality of an object or a service is outdated and should be replaced by relevant data. In this talk, these questions are studied from various angles and areas. Special attention is paid to marketplaces where everybody can trade data, or which are at least data-rich.

Short bio: Gottfried Vossen is a Professor of Computer Science in the Department of Information Systems at the University of Muenster in Germany. He is a Fellow of the German Computer Science Society and an Honorary Professor at the University of Waikato Management School in Hamilton, New Zealand. He received his master’s and Ph.D. degrees as well as the German Habilitation from RWTH Aachen University in Germany, and is an Editor-in-Chief of Elsevier's Information Systems - An International Journal. His current research interests include conceptual as well as application-oriented challenges concerning databases, information systems, business process modelling, Smart Web applications, cloud computing, and big data.

Abstract: Formulating database queries in terms of SQL is often a challenge for journalists, business administrators, biologists, healthcare professionals, and the growing number of non-database experts that are required to access and explore data. Moreover, writing accurate SQL queries is especially problematic when users lack sufficient knowledge about either the query language or the data domain. In these situations, query formulation becomes a highly interactive, time-consuming process and yields results that frequently do not meet user preferences and information needs. These problems have motivated the development of Query By Example (QBE) systems in which users are not required to possess any database-specific knowledge nor programming skills. Instead, users are asked to provide a set of data examples that satisfy their mental query and the QBE system tries to (semi-)automatically construct (i.e., search for) an accurate query, if one exists. In this talk, we review state-of-the-art QBE approaches and describe their advantages and limitations. We also demonstrate how machine learning algorithms can be applied in the context of QBE and show the effectiveness of three specific algorithms, namely, Greedy Search, Genetic Programming, and CART decision trees in learning queries in two distinct databases. Finally, we provide a research agenda describing future research development on QBE in the context of the democratization of data retrieval and exploration.

Short bio: Denis Martins is a Ph.D. candidate at the Databases and Information Systems group of the University of Münster, Germany. His research interests focus on the application of Computational Intelligence and Computational Semiotics for improving the democratization of data retrieval in Big Data scenarios. He holds a Master’s degree in Computer Engineering from the University of Pernambuco, Brazil, where he researched hybrid computational approaches for intelligent decision-making support.