Data Management

Data Management is a diverse research area focusing on the 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. 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.

Related faculty

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James Allan

Information retrieval, event-based information organization, controversy and misinformation detection.

Laura Haas

Information integration, database and knowledge-based systems, data science, data analytics and applications, information-centric user interaction.

Peter Haas

Information management; mining, analytics, and exploration of massive data; probabilistic database systems; machine learning.

Alexandra Meliou

Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias.

Gerome Miklau

Database research with an emphasis on privacy and security; database theory; semi-structured data.

Marco Serafini

Systems for ML, Data Management Systems, Distributed Systems.

Related centers & labs

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Center for Data Science

Develops and applies methods to collect and analyze large-scale data, and to make discoveries and decisions using those analyses.