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Data Management

Data Management is a diverse research area focusing on efficient, scalable, usable, and secure management of big data. Classically, data management has focused on structured data, but it now also includes a variety of complex data types and settings. These include textual data, semi-structured data, graph-structure data, and continuous data streams, as well as data (regardless of type) that is incomplete, imprecise, or which represents probabilistic assertions rather than known facts.

Data Management research is increasingly multi-disciplinary, including aspects of theory, machine learning, operating systems, programming languages, information retrieval, and many others.  Research problems in this area focus on

  • Developing algorithms and systems support to handle voluminous data scalably and efficiently;
  • Representing and handling data uncertainty;
  • Extending database functionality to support increasingly complex operations driven by modern applications;
  • Enhancing the experience of users who interact with varied datasets for purposes of data exploration, insight discovery, and decision support;
  • Computationally modeling text and user information needs;
  • Supporting societal goals of privacy, diversity, and fairness in data-driven applications;
  • Ensuring proper handling of and access to data in modern data-sharing platforms.  

At its core, Data Management seeks to enhance the capabilities of systems that deal with data, and, together with statistics and machine learning, is one of the pillars of the emerging discipline of Data Science.