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Big Data Management and Mining Laboratory (BDLab)

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.