DBMS Research Group

   About us

The Parallel DBMS group at UH focuses on developing scalable and parallel algorithms to analyze large data sets with machine learning and statistical models (e.g. clustering, classification, regression, dimensionality reduction, variable/feature selection, time series), cubes (ad-hoc queries, decision support systems) and graphs (paths, cliques, vertex neighborhood). In the past we worked on sequential data mining algorithms and parallel row DBMSs with applications in corporate data warehouses and medical data. Our approach is radically different from most database research since our techniques are developed on how to integrate algorithms with parallel DBMSs having column and array based storage. Our work also includes R language and streams.

   Research Topics

  • Parallel algorithms (machine learning, graphs, cubes).
  • Analytics inside parallel DBMSs and Hadoop (MapReduce, Spark).
  • Eliminating RAM and parallel processing limitations from math packages (R and Matlab).
  • Query optimization: recursive queries, cubes, skylines, pivoting.
  • Semi-structured data: text, web pages, documents, ontologies.
  • Software engineering: ER modeling, data quality, debugging source code.
  • Applications: medicine, bioinformatics, corporate data warehouses, physics, network monitoring.
  •    Director

    Dr. Carlos Ordonez

    Associate Professor
    Department of Computer Science
    University of Houston
    Houston TX, 77204
    firstname AT central DOT uh DOT edu

    University of Houston - Computer Science Department - Parallel DBMS Research Group 2017