Big Data Management and Mining Laboratory

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, computational genetics, IoT, 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 TEAM

Our Team!

Meet the BDLab faculty and students

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RESEARCH

Research

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Education

Education

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Publications.

Publications

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News News (See More)

  • ICDE 2020 Paper Acceptance

    Please join us to congratulate Shahab Helmi, our Ph.D. student, who had his paper, titled "Multiscale Frequent Co-movement Pattern Mining", accepted for publication in the ICDE 2020 conference. We wish him many years of great achievements.
    More information about his paper can be found here.

  • DMBIH 2019 Paper Acceptance

    Please join us to congratulate Evan Stene, our Ph.D. student, who had his paper, titled "SeAlM: A Query Cache Optimization Technique for Next Generation Sequence Alignment", accepted for publication in the DMBIH 2019 conference. We wish him many years of great achievements.
    More information about his paper can be found here.

  • Latisha Konz Graduated!

    We are thrilled to announce that Latisha successfully defended her master's thesis titled: "ST-DeepGait: A Spatiotemporal Deep Learning Approach for Human Gait Recognition". She has continued on to Lockheed Martin where she is now a software engineer. We wish her the best of luck!

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Readings Readings

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