Efficient Multi-Attribute Analysis for Trajectories
Fabio Valdés, Ralf Hartmut Güting · 2017
The recent proliferation of positioning devices has boosted the requirement for efficient methods of processing and analyzing large amounts of recorded movement data. Besides the geographic position, for many application domains there is more relevant time-dependent information such as speed, elevation, street names, or transportation modes, depending on the kind of moving object and on the evaluation purpose. In this paper, we present an application of a new framework that efficiently analyzes datasets with several time-dependent attributes of different types, using a highly flexible and expressive pattern language. In contrast to previous variants, the semantics of the language has been changed to make it more expressive and flexible, and the efficiency has been improved.