The first is that many queries are often over specified leading to empty answers. We propose a principled optimization-based interactive query relaxation framework for such queries. The framework computes dynamically and suggests alternative queries with less conditions to help the user arrive at a query with a non-empty answer, or at a query for which it is clear that independently of the relaxations the answer will always be empty.
The second issue is the lack of expertise from the user to accurately describe the requirements of the elements of interest. The user may though know examples of elements that would like to have in the results. We introduce a novel form of query paradigm in which queries are not any more specifications of what the user is searching for, but simply a sample of what the user knows to be of interest. We refer to this novel form of queries as Exemplar Queries.
Yannis Velegrakis is an associate professor at the Department of Information Engineering and Computer Science of the University of Trento, and the leader of the Data and Information Management group. He holds a PhD degree in Computer Science from the University of Toronto. His research areas of expertise include large scale integration of highly heterogeneous and distributed data, efficient and effective query answering, social data analytics, and Big Data. Prior to joining the University of Trento, he was a researcher at the AT&T Research Labs in the United States. He has also spent time as a visitor at the University of California, Santa-Cruz, the IBM Almaden Research Center, and the Center of Advanced Studies of the IBM Toronto Lab. He was a member of the committee for the CIMI cultural profile of the ANSI/NISO Z39.50 standard. He has served in many program committees of national and international conferences and as reviewer for numerous international journals. He has been a general chair for VLDB 2013, WebDB 2012, DESWEB 2010/11 and SWAE2007. He holds 2 US patents and has been a Marie Curie Fellow for the period 2006-2008.