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!

Meet the BDLab faculty and students



Learn about BDLab research projects



Learn about BDLab education tracks



Browse BDLab publications


News News (See More)

  • Amstat News Interviewed BDLab's Director

    Amstat News Interviewed BDLab's director, professor Banaei Kashani, regarding the master’s programs in data science and analytics.

    You can read the full interview here.

    More information regarding the Biostatistics MS Emphasis in Data Science Analytics program can be found here here.

    More information regarding the Computer Science MS Track in Data Science in Biomedicine program can be found here here.

  • IEEE BigData 2017 Paper Acceptance

    Please join us to congratulate Shahab Helmi, our Ph.D. student, who had his paper, titled "Spatiotemporal Range Pattern Queries on Large-scale Co-movement Pattern Datasets", accepted for publication in the highly prestigious IEEE Big Data 2017 conference (acceptance rate: 18%). We wish him many years of great achievements.
    More information about his paper can be found at

 Tweets! (See More)

Readings Readings

See more PHD Comics© here.

Facebook  Facebook (See More)