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Systems for Managing Uncertain Data


There is an increasing need for tools that facilitate business decision making in the face of massive and uncertain data. The problem of data uncertainty is becoming acute, due to data integration, automated information extraction form text, data anonymization for privacy protection, and the growing importance of RFID and sensor data. We have developed the MCDB relational database system, which supports both real-world BI queries over uncertain data, as well as allowing sophisticated, data-intensive stochastic modeling and prediction to be performed close to the data. We have also ported our techniques to a cloud-computing environment, so that we can exploit massive parallism and handle non-relational data.

Project Contact: Peter Haas

Related Publications

  • Eirinaios Michelakis, Rajasekar Krishnamurthy, Peter J. Haas, Shivakumar Vaithyanathan: Uncertainty management in rule-based information extraction systems. SIGMOD Conference 2009: 101-114.
  • Fei Xu, Kevin S. Beyer, Vuk Ercegovac, Peter J. Haas, Eugene J. Shekita: E = MC3: managing uncertain enterprise data in a cluster-computing environment. SIGMOD Conference 2009: 441-454.
  • Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, Christopher M. Jermaine, Peter J. Haas: MCDB: a monte carlo approach to managing uncertain data. SIGMOD Conference 2008: 687-700.
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