CiNCT: Compression and Retrieval for Massive Vehicular Trajectories via Relative Movement Labeling

Koide, Satoshi; Tadokoro, Yukihiro; Xiao, Chuan; Ishikawa, Yoshiharu

doi:10.1109/icde.2018.00102

Cited by 17 publications

(18 citation statements)

References 29 publications

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“…As future work, we are interested in exploiting the underlying network topology to obtain a more compact representation of the trips in CTR. In this promising line [68,69] we are working on a succinct representation for the context of public transportation networks. Also, we want to explore ways to improve the compression of the temporal component of CTR.…”

Section: Discussionmentioning

confidence: 99%

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

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Representing the movements of objects (trips) over a network in a compact way while retaining the capability of exploiting such data effectively is an important challenge of real applications. We present a new Compact Trip Representation (CTR) that handles the spatio-temporal data associated with users' trips over transportation networks. Depending on the network and types of queries, nodes in the network can represent intersections, stops, or even street segments.CTR represents separately sequences of nodes and the time instants when users traverse these nodes. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. The temporal component is self-indexed with either a Hu-Tucker-shaped Wavelet-tree or a Wavelet Matrix that solve range-interval queries efficiently. We show how CTR can solve relevant counting-based spatial, temporal, and spatio-temporal queries over large sets of trips. Experimental results show the space requirements (around 50-70% of the space needed by a compact non-indexed baseline) and query efficiency (most queries are solved in the range of 1-1000 microseconds) of CTR.

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Section: Discussionmentioning

confidence: 99%

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

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“…The spatial quality heuristic 33 makes priority queue of trajectory points and removes insignificant and redundant points from original trajectory data. Koide et al 34 represented trajectory points as sequence of road edges and introduced the concept of relative movement labeling and pseudo ranking for reducing data size and fast query processing. Chen et al 35 proposed linear time compression algorithm, which uses bottom up multiresolution approach.…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities

Bashir

Ashraf

Habib

et al. 2020

Trans Emerging Tel Tech

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The urban road networks and vehicles generate exponential amount of spatio-temporal big-data, which invites researchers from diverse fields of interest. Global positioning system devices may transceive data every second thus producing huge amount of trajectory data. Subsequently, it requires optimized computing for various operations such as visualization and mining hidden patterns. This sporadically stored big-data contains invaluable information, which is useful for a number of real-time applications. Compression is a highly important, but knotty task. Optimized compression enables us achieve the desired results in efficient and effective manner by using minimum energy and computational resources without compromising on important information. We present two versions of a compression technique based on the points of intersections (PoI) of urban roads networks. Based on intelligent mining paradigm, we created a compressed lookup lexicon to store the PoIs of dynamically selected region of interests (ROI). An important feature of our lexicon is the key pattern, which is intelligently computed based on the relative geographic position of a spatial geodetic vertex with respect to Euclidean space origin in a given ROI. This compresses trajectories in linear time, making it feasible for mission critical real world applications. Our experimental dataset contained 959 547, 517 436, and 231 740 trajectories for Bikes, Cars, and Taxis, respectively. The Compr 10 reduced these trajectories to 17 428, 11 084, and 6565, respectively. Results of Compr 15 and Compr 20 show promising results. We define the quality of the compression in context of the considered problem. The results show that the proposed technique achieved satisfactory quality of the compression. INTRODUCTIONThe urban roads networks and many types of vehicles running over them generate exponential amount of spatio-temporal big-data, which attracts the keen interest of researchers from diverse fields of research such as power and energy, geographical information systems, data mining, artificial intelligence, and so on. This big-data contains invaluable This is a companion to [10.1002/ett.3886].

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“…Some techniques are to reduce the number of points in a curve [22] or to use features at each point, such as speed and orientation [26]. Both techniques work in free spaces and, when the movement is restricted to networks, it is even possible to get a better compression, like the ones shown in [18,19,20,28].…”

Section: Background and Related Workmentioning

confidence: 99%

“…This is a fundamental difference with our proposal, in which the spatial dimension are coordinates in a two-dimensional space, and not labels. This is also the main difference with CiNCT [20], which boosts CTR in terms of memory storage and query time.…”

Section: Background and Related Workmentioning

confidence: 99%

Faster and Smaller Two-Level Index for Network-Based Trajectories

Rivera¹,

Rodríguez²,

Seco³

2018

String Processing and Information Retrieval

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Two-level indexes have been widely used to handle trajectories of moving objects that are constrained to a network. The toplevel of these indexes handles the spatial dimension, whereas the bottom level handles the temporal dimension. The latter turns out to be an instance of the interval-intersection problem, but it has been tackled by non-specialized spatial indexes. In this work, we propose the use of a compact data structure on the bottom level of these indexes. Our experimental evaluation shows that our approach is both faster and smaller than existing solutions.

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CiNCT: Compression and Retrieval for Massive Vehicular Trajectories via Relative Movement Labeling

Cited by 17 publications

References 29 publications

A compact representation for trips over networks built on self-indexes

A compact representation for trips over networks built on self-indexes

An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities

Faster and Smaller Two-Level Index for Network-Based Trajectories

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