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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • John Grant
    • 1
  • Cristian Molinaro
    • 2
  • Francesco Parisi
    • 2
  1. 1.University of MarylandCollege ParkUSA
  2. 2.DIMES DepartmentUniversità della CalabriaRendeItaly

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