Spatial Probabilistic Temporal Databases
نویسنده
چکیده
Title of Dissertation: Spatial Probabilistic Temporal Databases Austin Parker, Doctor of Philosophy, 2008 Dissertation directed by: Professor VS Subrahmanian Department of Computer Science 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. Spatial Probabilistic Temporal Databases
منابع مشابه
An Overview on Querying and Learning in Temporal Probabilistic Databases
Probabilistic databases store, query and manage large amounts of uncertain information in an efficient way. This paper summarizes my thesis which advances the state-of-the-art in probabilistic databases in three different ways: First, we present a closed and complete data model for temporal probabilistic databases. Queries are posed via temporal deduction rules which induce lineage formulas cap...
متن کاملTowards The Use of Probabilistic Spatial Relation Databases in Business Process Modeling
In this paper, the authors combine the methods of probabilistic databases, GIS (Geographical Information Systems) and business process modelling to evaluate the agricultural activities. The authors provide a technique to evaluate the risk of agricultural activities among the hydrological objects (lakes, rivers, etc.) and agricultural plots. The spatial relation is an important information neede...
متن کاملEfficient querying and learning in probabilistic and temporal databases
Probabilistic databases store, query, and manage large amounts of uncertain information. This thesis advances the state-of-the-art in probabilistic databases in three different ways: 1. We present a closed and complete data model for temporal probabilistic databases and analyze its complexity. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and u...
متن کاملQuerying Nested Historical Relations in Heterogeneous Databases Environment
We study schema integration problems for consolidating historical information from nested relational databases in heterogeneous databases environment. These nested relations are for supporting complex objects. In heterogeneous databases systems, probabilistic partial values have been used to resolve some schema integration problems. In this paper, we extend the concept of probabilistic partial ...
متن کاملProbabilistic Databases ∗ Dan
Many applications today need to manage large data sets with uncertainties. In this paper we describe the foundations of managing data where the uncertainties are quantified as probabilities. We review the basic definitions of the probabilistic data model and present some fundamental theoretical results for query evaluation on probabilistic databases. 1 The Quest for Probabilistic Databases Comm...
متن کامل