Package elki.data.uncertain

Class WeightedDiscreteUncertainObject

  • All Implemented Interfaces:
    FeatureVector<java.lang.Double>, SpatialComparable, DiscreteUncertainObject, UncertainObject
    @Reference(authors="N. Dalvi, C. R\u00e9, D. Suciu",title="Probabilistic databases: diamonds in the dirt",booktitle="Communications of the ACM 52, 7",url="https://doi.org/10.1145/1538788.1538810",bibkey="DBLP:journals/cacm/DalviRS09") @Reference(authors="O. Benjelloun, A. D. Sarma, A. Halevy, J. Widom",title="ULDBs: Databases with uncertainty and lineage",booktitle="Proc. of the 32nd Int. Conf. on Very Large Data Bases (VLDB)",url="http://www.vldb.org/conf/2006/p953-benjelloun.pdf",bibkey="DBLP:conf/vldb/BenjellounSHW06") @Reference(authors="Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Florian Verhein, Andreas Z\u00fcfle",title="Probabilistic frequent itemset mining in uncertain databases",booktitle="Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining",url="https://doi.org/10.1145/1557019.1557039",bibkey="DBLP:conf/kdd/BerneckerKRVZ09")
public class WeightedDiscreteUncertainObject
extends AbstractUncertainObject
implements DiscreteUncertainObject
    Weighted version of discrete uncertain objects.
    • Every object is represented by a finite number of discrete samples.
    • Every sample has a weight associated with it.
    • Samples with higher weight are more likely to be returned by drawSample(java.util.Random).
    References:

    This is called the block independent-disjoint (BID model) in:

    N. Dalvi, C. Ré, D. Suciu
    Probabilistic databases: diamonds in the dirt
    Communications of the ACM 52, 7

    This is also known as the X-Tuple model in:

    O. Benjelloun, A. D. Sarma, A. Halevy, J. Widom
    ULDBs: Databases with uncertainty and lineage
    In Proc. of the 32nd Int. Conf. on Very Large Data Bases (VLDB)

    If only a single sample is provided, this can be used to model existential uncertainty as in:

    N. Dalvi, D. Suciu
    Efficient query evaluation on probabilistic databases.
    The VLDB Journal, 16(4)

    and:

    Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Florian Verhein, Andreas Züfle
    Probabilistic frequent itemset mining in uncertain databases.
    In Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining.

    Since:
    0.7.0
    Author:
    Alexander Koos, Erich Schubert