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Count Queries in Probabilistic Spatio-Temporal Knowledge Bases with Capacity Constraints

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Book cover Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10369))

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Abstract

The problem of managing spatio-temporal data arises in many applications, such as location-based services, environment monitoring, geographic information system, and many others. In real life, this kind of data is often uncertain. The SPOT framework has been proposed for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown.

In this paper, we enhance the SPOT framework with capacity constraints, which allow users to better model many real-world scenarios. The resulting formalization is called PST knowledge base. We study the computational complexity of consistency checking, a central problem in this setting. Specifically, we show that the problem is NP-complete and also identify tractable cases. We then consider a relevant kind of queries to reason on PST knowledge bases, namely count queries, which ask for how many objects are in a region at a certain time point. We investigate the computational complexity of answering count queries, and show cases for which consistency checking can be exploited for query answering.

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Authors and Affiliations

  1. University of Maryland, College Park, USA

    John Grant

  2. DIMES Department, Università della Calabria, Rende, Italy

    Cristian Molinaro & Francesco Parisi

Authors
  1. John Grant
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  2. Cristian Molinaro
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  3. Francesco Parisi
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Corresponding author

Correspondence to Cristian Molinaro .

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Editors and Affiliations

  1. IDSIA, Lugano, Switzerland

    Alessandro Antonucci

  2. ONERA, Toulouse, France

    Laurence Cholvy

  3. Aix-Marseille University, Marseille, France

    Odile Papini

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Grant, J., Molinaro, C., Parisi, F. (2017). Count Queries in Probabilistic Spatio-Temporal Knowledge Bases with Capacity Constraints. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_41

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  • DOI: https://doi.org/10.1007/978-3-319-61581-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61580-6

  • Online ISBN: 978-3-319-61581-3

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