Research and education in Data Science, Big Data Management and Mining is organized in two tracks: a Data Science track, and a Data Management and Mining track.
With the Data Science track, we engage with real-world problems that can benefit from data-driven solutions (consisting of all data scientific life-cycle components), given various combinations of the Big Data challenges. Toward this end, we have experienced with a number of data-driven decision-making systems from various application areas, such as health informatics, computational genetics, IoT, intelligent transportation, and geospatial intelligence.
The Data Science track complements the Data Management and Mining track by providing practical real-world problems that we generalize, formalize, and rigorously study as novel data management and mining problems. In particular, we have special interest in the following areas (among others): spatiotemporal data management and mining, graph data management and mining, high-throughput data management and mining using modern hardware, and next generation database engines (or NewSQL).