At BDLab (Big Data Management and
Mining Laboratory), we have organized our research and education around 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 V5 challenges. Toward
this end, we have experienced with a number of data-driven decision-making
systems (DDSs) from various application areas, such as health informatics, oil
recovery optimization, real-time surveillance, intelligent transportation, and
scientific computing. The Data Science track complements the Data Management
and Mining track by providing practical real-world problems, which 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). Our research at BDLab has been
supported by grants from both governmental agencies (NSF/CENS, NIH/CTSI,
DOT/METRANS, DOJ/NIJ, NASA/JPL) and industry (Google, IBM, Chevron, NGC).
For research and education purposes, BDLab is equipped with an extensive computing platform consisting of numerous data management and mining software tools as well as supporting equipment. Our server is a PowerEdge R920 Database Server (2x Intel Xeon E7-4820 v2 Processor, 16GB RDIMM memory, and 4TB of SSD and SAS storage). We have a 16 node, 128 core commodity desktop cluster build from Dell Optiplex 790 Desktop workstations (i7, 8G RAM, 8 hyper-threaded cores) running Apache Ambari and Hortonworks Hadoop Data Platform (HDP). We also have five XPS 8700 workstations each with 16GB Dual Channel DDR3 memory and 2TB SATA HDD. More importantly, the lab is staffed with students that are skillful and knowledgeable in both data science and data management/mining areas.
For more information, about faculty, publications, education tracks and courses, visit the BDLab website.