Index-supported pattern matching on tuples of time-dependent values

Lately, the amount of mobility data recorded by GPS-enabled (and other) devices has increased drastically, entailing the necessity of efficient processing and analysis methods. In many cases, not only the geographic position, but also additional time-dependent information are traced and/or generated, according to the purpose of the evaluation. For example, in the field of animal behavior research, besides the position of the monitored animal, biologists are interested in further data like the altitude or the temperature at every measuring point. Other application domains comprise the names of streets, places of interest, or transportation modes that can be recorded along with the geographic position of a person. In this paper, we present in detail a framework for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension of our previous work, a comprehensive framework for pattern matching on symbolic trajectories with index support. For an efficient processing of different data types, a variable number of indexes of four different types that correspond to the data types of the attributes are applied. We demonstrate the expressiveness and efficiency of our approach by querying a real dataset representing taxi trips in Rome and, particularly, with a broad series of experiments using trajectories generated by BerlinMOD combined with geological raster data.