Trajectory Collection and Reconstruction (original) (raw)
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Compression of trajectory data: a comprehensive evaluation and new approach
GeoInformatica, 2013
GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. A key requirement for these algorithms is to minimize the loss of information essential to locationbased applications. This paper presents a new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic-Extended) that provides improved runtime performance and usability. A comprehensive comparison of SQUISH-E with
Compressing Trajectories Using Inter-Frame Coding
With the advances in wireless communications and GPS technology, there is increasing interest in the field of location-aware services. Because of the proliferation of GPS-enabled devices and applications, in this study, we address the scalability issue in trajectory data management. Specifically, we propose a scheme called Inter-Frame Coding (IFC) for lossless compression of trajectory data, and implement two classical database queries based on the scheme. Evaluations of the IFC scheme using real trajectory datasets show that it can achieve a compression ratio of 58%. Moreover, it can reduce the computational complexity of range queries by a factor of 0.45, while maintaining an acceptable execution time in k-nearest neighbor searches. The IFC scheme is simple, efficient, and lossless; thus, it has the potential to facilitate trajectory-based data storage, compression, and computation.
Trajectory Compression under Network Constraints
Lecture Notes in Computer Science, 2009
The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams modeling objects movements, raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Moreover, due to erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques are not designed for network constrained moving objects, while map matching algorithms do not consider compression issues. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an experimental evaluation using a real trajectory dataset.
Algorithms for compressing GPS trajectory data: An empirical evaluation
2010
The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.
A Framework for Efficient and Convenient Evaluation of Trajectory Compression Algorithms
2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013
Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been proposed in the literature, no common benchmarking platform for evaluating their effectiveness exists. This paper presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. This framework supports various compression algorithms and metrics defined in the literature, as well as three synthetic trajectory generators that have different trade-offs. It also has a highly extensible architecture that facilitates the incorporation of new compression algorithms, evaluation metrics, and trajectory data generators. This paper provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this paper describes challenges that arise in the design and implementation of the above framework and our approaches to tackling these challenges. Finally, this paper presents evaluation results that demonstrate the utility of the benchmarking framework.
Lecture Notes in Computer Science
Location and time information about individuals can be captured through GPS devices, GSM phones, RFID tag readers, and by other similar means. Such data can be pre-processed to obtain trajectories which are sequences of spatio-temporal data points belonging to a moving object. Recently, advanced data mining techniques have been developed for extracting patterns from moving object trajectories to enable applications such as city traffic planning, identification of evacuation routes, trend detection, and many more. However, when special care is not taken, trajectories of individuals may also pose serious privacy risks even after they are de-identified or mapped into other forms. In this paper, we show that an unknown private trajectory can be reconstructed from knowledge of its properties released for data mining, which at first glance may not seem to pose any privacy threats. In particular, we propose a technique to demonstrate how private trajectories can be reconstructed from knowledge of their distances to a bounded set of known trajectories. Experiments performed on real data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds.
Trajectory data warehouses: proposal of design and application to exploit data
2007
Abstract. In this paper we are interested in storing and perform OLAP queries about various aggregate trajectory properties. We consider a data stream environment where a set of mobile objects send the data about its location in a irregular and unbounded way, the data volume is stored in a centralized and traditional DW with pre-computed aggregations values (preserving the trajectories privacy).
Similarity-Based Compression of GPS Trajectory Data
2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013
The recent increase in the use of GPS-enabled devices has introduced a new demand for efficiently storing trajectory data. In this paper, we present a new technique that has a higher compression ratio for trajectory data than existing solutions. This technique splits trajectories into sub-trajectories according to the similarities among them. For each collection of similar sub-trajectories, our technique stores only one subtrajectory's spatial data. Each sub-trajectory is then expressed as a mapping between itself and a previous sub-trajectory. In general, these mappings can be highly compressed due to a strong correlation between the time values of trajectories. This paper presents evaluation results that show the superiority of our technique over previous solutions.
A Framework for Trajectory Data Warehousing
2008
Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in data management world, the challenge after storing the data is the implementation of appropriate analytics that could extract useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place in order to transform sampled location data originated e.g. from GPS recordings into trajectories and load them to a moving object database, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.