Spatial Probabilistic Temporal Databases

Research in spatio-temporal probabilistic reasoning examines algorithms for handling data such as cell phone triangulation, GPS systems, movement prediction software, and other inexact but useful data sources. In this thesis I describe a probabilistic model theory for such data. The Spatial PrObabilistic Temporal database framework (or SPOT database framework) provides methods for interpreting, checking consistency, automatically revising, and querying such databases. This thesis examines two different semantics within the SPOT framework and presents polynomial-time consistency checking algorithms for both. It introduces several revision techniques for repairing inconsistent databases and compares them to the AGM Axioms for belief state revision; finding an algorithm that, by only changing the probability bounds in the SPOT atoms, can repair a SPOT database in polynomial time while still satisfying the AGM axioms. Also included is an investigation into optimistic and cautious versions of a selection query that returns all objects in a given region with at least (or at most) a certain probability. For these queries, I introduce an indexing structure akin to the R-tree called a SPOT tree, and show experiments where indexing speeds up selection with both artificial and real-world data. I also introduce query preprocessing techniques that bound the sets of solutions with both circumscribing and inscribing regions, and discover these to also provide query time improvements in practice. By covering semantics, consistency checking, database revision, indexing, and query preprocessing techniques for SPOT database, this thesis provides a significant step towards a SPOT database framework that may be applied to the sorts of real-world problems in the impressive amount of semi-accurate spatio-temporal data available today

Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting.In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define ?dimension-aware? counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.Fil: Grant, John. Towson University; Estados UnidosFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Molinaro, Cristian. Università della Calabria; ItaliaFil: Parisi, Francesco. Università della Calabria; Itali