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Moving objects databases (MOD) manage trajectory information of vehicles, animals, and other mobile objects. A crucial problem is how to efficiently track an object's trajectory in real-time, in particular if the trajectory data is sensed at the mobile object and thus has to be communicated over a wireless network.

We propose a family of tracking protocols that allow trading the communication cost and the amount of trajectory data stored at a MOD off against the spatial accuracy. With each of these protocols, the MOD manages a simplified trajectory that does not deviate by more than a certain accuracy bound from the actual movement. Moreover, the different protocols enable several trade-offs between computational costs, communication cost and the reduction of the trajectory data: Connection-Preserving Dead Reckoning (CDR) minimizes the communication cost using dead reckoning, a technique originally designed for tracking an object's current position. Generic Remote Trajectory Simplification (GRTS) further separates between tracking of the current position and simplification of the past trajectory and can be realized with different line simplification algorithms. For both protocols, we discuss how to bound the space consumption and computing time at the moving object and thereby present an effective compression technique to optimize the reduction performance of real-time line simplification in general.

Our evaluations with hundreds of real GPS traces show that a realization of GRTS with a simple simplification heuristic reaches 85 to 90% of the best possible reduction rate, given by retrospective offline simplification. A realization with the optimal line simplification algorithm by Imai and Iri even reaches more than 97% of the best possible reduction rate.

Scaling heterogeneous information systems (HIS) to thousands of sources poses particular challenges to source discovery. It requires a powerful formalism for describing the contents of the sources in a concise manner and for formulating compatible queries as well as a suitable structure for indexing and retrieving the source descriptions efficiently.

We propose an extended logic-based description formalism for large-scale HIS with structured sources and a shared ontology. The formalism refines existing approaches that describe the sources by constraints on the attribute value ranges in several ways: It allows for complex, nested descriptions based on defined classes. It supports alternative descriptions to express that a source may be discovered by different combinations of constraints. Finally, it allows to adjust between positive matching, similar to keyword-based discovery, and negative matching, as used in existing logic-based approaches.

We further propose the SDC-Tree for indexing such source descriptions. To allow for efficient discovery, the SDC-Tree features multidimensional indexing capabilities for the different attributes and the IS-A hierarchy of the shared ontology, but also incorporates the existence or absence of constraints. For this purpose, it supports three different types of node split operations which exploit the expressiveness of the description formalism. Therefore, we also propose a generic split algorithm which can be used with arbitrary ontologies.

Moving objects databases (MODs) have been proposed for managing trajectory data, an important kind of information for pervasive applications. To save storage capacity, a MOD generally stores simplified trajectories only. A simplified trajectory approximates the actual trajectory of the mobile object according to a certain accuracy bound.

In order to minimize the costs of communicating position information between mobile object and MOD, the trajectory simplification should be performed by the mobile object. To assure that the MOD always has a valid simplified trajectory of the remote object, we propose the generic remote trajectory simplification protocol (GRTS) allowing for computing and managing a simplified trajectory in such a system in real-time.

We show how to combine GRTS with existing line simplification algorithms for computing the simplified trajectory and analyze trade-offs between the different algorithms. Our evaluations show that GRTS outperforms the two existing approaches by a factor of two and more in terms of reduction efficiency. Moreover, on average, the reduction efficiency of GRTS is only 12% worse compared to optimal offline simplification.