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• W2807199323 abstract "One of the striking developments since the beginning of this century is the proliferation of positioning technology devices in vehicles or with people. Large amounts of mobility data are recorded every day, entailing the necessity of efficient processing and analysis methods. Therefore, the management of such data, also called trajectories, has become a very active research field, particularly in the scope of spatiotemporal database management systems.Methods of associating semantic information to raw geographic trajectories by relating them to the spatial environment have been studied extensively. A question is how the resulting trajectories can be represented and especially queried in a convenient and efficient way. Moreover, in many cases, not only the geographic positions, but also additional time-dependent information regarding the moving entity 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 such as the altitude or the temperature at every measuring point. Other application domains comprise medical, logistic, or investigative information that can be recorded along with (or instead of) the geographic position of a person, animal, vehicle, etc.In this thesis, we propose a systematic and comprehensive study of annotated trajectory databases. A simple generic model called symbolic trajectory is defined to capture a wide range of meanings, either derived from a geometric trajectory or related to other time-dependent values. In its most essential form, a symbolic trajectory is just a time-dependent label; variants contain sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well established framework of data types and operations formoving objects. Symbolic trajectories can represent, for example, the names of traversed roads obtained by map matching, transportation modes (walk, train, bus, etc.), a speed profile in symbolic form (e.g., fast, very fast, moderate), cells of a cellular network, certain behaviors of animals, names of restaurants within a certain distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories.Besides the model, the main technical contribution of the thesis is a language for pattern matching and rewriting of symbolic trajectories, including efficient matching algorithms. A symbolic trajectory can be represented as a sequence of pairs of a time interval and a label. Such a pair is called a unit. A pattern consists of unit patterns (specifications for time interval and/or label) and wildcards, matching units and sequences of units, respectively, as well as regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment which makes the language extensible and highly expressive.We formally define the data model and syntax and semantics of the pattern language. Query operations are offered to integrate pattern matching, rewriting, and classification of symbolic trajectories into a DBMS querying environment. The implementation of the model using finite state machines and two indexes is described in detail. An experimental evaluation demonstrates the efficiency of the implementation. In particular, it shows considerable improvements in storage space and response time in a comparison of symbolic and geometric trajectories for several queries that can be executed on both types of trajectories.We also present in detail an approach for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension and generalization of our work on symbolic trajectories. 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. Again, we demonstrate the expressiveness and efficiency of our approach with a broad series of experiments using generated as well as real trajectories combined with geological raster data.A separate chapter is dedicated to a choice of application examples for pattern matching on symbolic trajectories and on tuples of time-dependent values." @default.
• W2807199323 created "2018-06-13" @default.
• W2807199323 creator A5057325316 @default.
• W2807199323 date "2017-01-01" @default.
• W2807199323 modified "2023-09-23" @default.
• W2807199323 title "Framework for Pattern Matching on Symbolic Trajectories and Tuples of Time-dependent ValuesA" @default.
• W2807199323 hasPublicationYear "2017" @default.
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