Aggregate queries for discrete and continuous probabilistic XML

  • Authors:

  • Serge Abiteboul;T.-H. Hubert Chan;Evgeny Kharlamov;Werner Nutt;Pierre Senellart

  • Affiliations:

  • INRIA Saclay, Orsay Cedex, France;The University of Hong Kong, Hong Kong;Free University of Bozen-Bolzano, Bozen, Italy;Free University of Bozen-Bolzano, Bozen, Italy;Institut Té/lé/com/ Té/lé/com ParisTech, Paris, France

  • Venue:
  • Proceedings of the 13th International Conference on Database Theory
  • Year:
  • 2010

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Abstract

Sources of data uncertainty and imprecision are numerous. A way to handle this uncertainty is to associate probabilistic annotations to data. Many such probabilistic database models have been proposed, both in the relational and in the semi-structured setting. The latter is particularly well adapted to the management of uncertain data coming from a variety of automatic processes. An important problem, in the context of probabilistic XML databases, is that of answering aggregate queries (count, sum, avg, etc.), which has received limited attention so far. In a model unifying the various (discrete) semi-structured probabilistic models studied up to now, we present algorithms to compute the distribution of the aggregation values (exploiting some regularity properties of the aggregate functions) and probabilistic moments (especially, expectation and variance) of this distribution. We also prove the intractability of some of these problems and investigate approximation techniques. We finally extend the discrete model to a continuous one, in order to take into account continuous data values, such as measurements from sensor networks, and present algorithms to compute distribution functions and moments for various classes of continuous distributions of data values.