Indexing uncertain spatio-temporal data

Abstract

The advances in sensing and telecommunication technologies allow the collection and management of vast amounts of spatio-temporal data combining location and time information. Due to physical and resource limitations of data collection devices (e.g., RFID readers, GPS receivers and other sensors) data are typically collected only at discrete points of time. In-between these discrete time instances, the positions of tracked moving objects are uncertain. In this work, we propose novel approximation techniques in order to probabilistically bound the uncertain movement of objects; these techniques allow for efficient and effective filtering during query evaluation using an hierarchical index structure. To the best of our knowledge, this is the first approach that supports query evaluation on very large uncertain spatio-temporal databases, adhering to possible worlds semantics. We experimentally show that it accelerates the existing, scan-based approach by orders of magnitude.

An extendable framework for managing uncertain spatio-temporal data

“…In particular, the expected error in terms of distance between the real trajectory and the modelled position (which is a random variable) was shown to be vastly reduced by this data integration. For a detailt evaluation of the runtime of queries under the Markov model we refer to the corresponding papers [3,2,7]. Figure 1(a) visualizes the set of spatial states used for the Markov model.…”

“…The map view provides bounding boxes of the uncertain objects approximating the position of each object at the given point of time. The visualized boxes correspond to spatial approximations of our spatio-temporal index structure ( [2]) for the Markov model. The implemented geometric model is described by these bounding boxes as well.…”

“…The shortcomings of these models have been addressed in previous work ( [3,2,7]), by applying models from statistics, namely stochastic processes and Bayesian inference, to treat uncertain moving objects in a probabilistic way. In the following Section 2, the theoretic base of these works is sketched.…”

Minimal spatio-temporal database repairs

“…The unique challenge we address is to go a step further and repair inconsistencies incurred by an imperfect interpolation model. Recent approaches model the motion of a spatio-temporal object by a stochastic process, where each possible world is associated with a probability [3]. However, more complex constraints, e.g., prohibiting objects from being at the same state at the same time, can not be straightforwardly incorporated.…”

Querying Spatiotemporal Data Based on XML Twig Pattern

“…Spatiotemporal data is characterized by both spatial [3] and temporal [4] semantics. With the development of spatiotemporal data, some operations based on spatiotemporal data are researched, such as spatiotemporal data query [5] and spatiotemporal data indexing [6].…”