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A framework for efficient multi-attribute movement data analysis

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Abstract

In the first two decades of this century, the amount of movement and movement-related data has increased massively, predominantly due to the proliferation of positioning features in ubiquitous devices such as cellphones and automobiles. At the same time, there is a vast number of requirements for managing and analyzing these records for economic, administrative, and private purposes. Since the growth of data quantity outpaces the efficiency development of hardware components, it is necessary to explore innovative methods of extracting information from large sets of movement data. Hence, the management and analysis of such data, also called trajectories, have become a very active research field. In this context, the time-dependent geographic position is only one of arbitrarily many recorded attributes. For several applications processing trajectory (and related) data, it is helpful or even necessary to trace or generate additional time-dependent information, according to the purpose of the evaluation. For example, in the field of aircraft traffic analysis, besides the position of the monitored airplane, also its altitude, the remaining amount of fuel, the temperature, the name of the traversed country and many other parameters that change with time are relevant. Other application domains consider the names of streets, places of interest, or transportation modes which can be recorded during the movement of a person or another entity. In this paper, we present in detail a framework for analyzing large datasets having any number of time-dependent attributes of different types with the help of a pattern language based on regular expression structures. The corresponding matching algorithm uses a collection of different indexes and is divided into a filtering and an exact matching phase. Compared to the previous version of the framework, we have extended the flexibility and expressiveness of the language by changing its semantics. Due to storage adjustments concerning the applied index structures and further optimizations, the efficiency of the matching procedure has been significantly improved. In addition, the user is no longer required to have a deep knowledge of the temporal distribution of the available attributes of the dataset. The expressiveness and efficiency of the novel approach are demonstrated by querying real and synthetic datasets. Our approach has been fully implemented in a DBMS querying environment and is freely available open source software.

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Notes

  1. Note that wgs is a database object of the type \({{{\underline{geoid}}}}\), required for precise computations on the earth’s surface.

  2. The spatial data type \({{{\underline{line}}}}\) represents (a linear approximation of) a continuous curve in the Euclidean plane.

  3. Let dortmund and center be database objects of the types \({{{\underline{region}}}}\) and \({{{\underline{point}}}}\) representing the shape and the city center of Dortmund, respectively.

  4. The leftclosed/rightclosed flags displayed here may vary: In the first case, the interval is lc/rc iff \(\pi _{j-1}\)/\(\pi _j\) is lc/rc; in the second case, the interval is rc iff \(\pi _1\) is not lc; in the third case, the interval is lc iff \(\pi _m\) is not rc.

  5. We assume that london is a database object representing the shape of the city of London.

  6. The spatial region object parc describes the shape of the Parc Naturel Régional de Lorraine.

  7. The script OrderedRelationGraphFromFullOSMImport.SEC is located in the directory secondo/bin/Scripts.

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  1. Database Systems for New Applications, Fernuniversität Hagen, 58084, Hagen, Germany

    Fabio Valdés & Ralf Hartmut Güting

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  1. Fabio Valdés
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Correspondence to Fabio Valdés.

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Valdés, F., Güting, R.H. A framework for efficient multi-attribute movement data analysis. The VLDB Journal 28, 427–449 (2019). https://doi.org/10.1007/s00778-018-0525-6

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