Rule-based Conditioning of Probabilistic Data

van Keulen, M. (Speaker)

    Activity: Talk or presentationOral presentation


    Probabilistic data integration (PDI) is a specific kind of data integration where integration problems such as inconsistency and uncertainty are handled by means of a probabilistic data representation. The approach is based on the view that data quality problems (as they occur in an integration process) can be modeled as uncertainty and this uncertainty is considered an important result of the integration process.
    The PDI process contains two phases: (i) a quick partial integration where certain data quality problems are not solved immediately, but explicitly represented as uncertainty in the resulting integrated data stored in a probabilistic database; (ii) continuous improvement by using the data - a probabilistic database can be queried directly resulting in possible or approximate answers - and gathering evidence (e.g., user feedback) for improving the data quality. A probabilistic database is a specific kind of DBMS that allows storage, querying and manipulation of uncertain data. It keeps track of alternatives and the dependencies among them.
    Period4 Oct 2018
    Event title12th International Conference on Scalable Uncertainty Management 2018
    Event typeConference
    Conference number12
    LocationMilan, Italy
    Degree of RecognitionInternational


    • data cleaning
    • data integration
    • information extraction
    • probabilistic databases
    • probabilistic programming