Trajic: An Effective Compression System for Trajectory Data

Nibali, Aiden; He, Zhen

doi:10.1109/tkde.2015.2436932

Cited by 52 publications

(34 citation statements)

References 15 publications

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“…Trajic [30] uses a strategy related to delta compression. It uses a predictor of the next point of a trajectory and an encoder of the residuals, that is, the difference between the prediction and the actual point.…”

Section: Compressing Trajectoriesmentioning

confidence: 99%

GraCT: A Grammar-based Compressed Index for Trajectory Data

Brisaboa

Gómez-Brandón

Navarro

et al. 2019

Information Sciences

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We introduce a compressed data structure for the storage of free trajectories of moving objects (such as ships and planes) that efficiently supports various spatio-temporal queries. Our structure, dubbed GraCT, stores the absolute positions of all the objects at regular time intervals (snapshots) using a k 2 -tree, which is a space-and time-efficient version of a region quadtree. Positions between snapshots are represented as logs of relative movements and compressed using Re-Pair, a grammar-based compressor. The nonterminals of this grammar are enhanced with MBR information to enable fast queries.The GraCT structure of a dataset occupies less than the raw data compressed with a powerful traditional compressor such as p7zip. Further, instead of requiring full decompression to access the data like a traditional compressor, GraCT supports direct access to object trajectories or to their position at specific time instants, as well as spatial range and nearest-neighbor queries on time instants and/or time intervals.Compared to traditional methods for storing and indexing spatio-temporal data, GraCT requires two orders of magnitude less space, and is competitive in query times. In particular, thanks to its compressed representation, the GraCT structure may reside in main memory in situations where any classical uncompressed index must resort to disk, thereby being one or two orders of magnitude faster.Indexes that handle free trajectories of points in the space are also called spatiotemporal indexes. One type spatio-temporal index builds on a classic multidimensional spatial index, in the form of a temporally augmented R-tree [13]. While the R-tree uses two-dimensional Minimum Bounding Rectangles (MBRs) to enclose the spatial objects stored in the database, the 3DR-tree [42] uses Minimum Bounding Boxes (MBBs), where the third dimension is time, to enclose the segments of the trajectories. The problem is that the three-dimensional space covered by an MBB can be large, even in small segments, resulting in high levels of overlap and limited power of discrimination. The STR-Tree [32] is an extension of the 3DR-tree designed to overcome this problem, by modifying the insertion/split strategy that produces the MBBs. The same work also proposes an index called TB-tree, where several segments of a trajectory are bundled into partial trajectories that are inserted as MBBs of an R-tree.A second kind of index is a versioned R-tree, which creates an R-tree for each timestamp and a B-tree to select the relevant R-trees. Creating an R-tree for each timestamp requires large amounts of space. To overcome this, instead of storing the complete R-tree for each timestamp, these techniques store only the part of the R-tree that is different from the R-tree in the previous timestamp. Examples of indexes of this type are MR-tree [46], HR-tree [27], HR+-tree [39] and MV3R-tree [40]. A third family of indexes, called grid-based indexes, partition the space and build a temporal index for each of the spatial partitions. The Scalable and Efficie...

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Section: Compressing Trajectoriesmentioning

confidence: 99%

GraCT: A Grammar-based Compressed Index for Trajectory Data

Brisaboa

Gómez-Brandón

Navarro

et al. 2019

Information Sciences

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“…(1) Lossless compression methods enable exact reconstruction of the original data from the compressed data without information loss. For example, delta compression [19] is a lossless compression technique, which has zero error and a time complexity of O(n), where n is the number of data points in a trajectory. The limitation of lossless compression lies in that its compression ratio is relatively poor [19].…”

Section: Related Workmentioning

confidence: 99%

One-pass error bounded trajectory simplification

Lin

Zhang

et al. 2017

Proc. VLDB Endow.

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Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are an effective approach to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. However, existing LS algorithms are not sufficient for the needs of sensors in mobile devices. In this study, we first develop a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once. We then propose an aggressive one-pass error bounded trajectory simplification algorithm (OPERB-A), which allows interpolating new data points into a trajectory under certain conditions. Finally, we experimentally verify that our approaches (OPERB and OPERB-A) are both efficient and effective, using four real-life trajectory datasets.

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“…Transmitting and storing raw trajectory data consumes too much network bandwidth and storage capacity [2, 5, 15-17, 20, 22-24, 27, 34]. It is known that these issues can be resolved or greatly alleviated by trajectory compression techniques via removing redundant data points of trajectories [2,4,5,7,10,12,[15][16][17][18]20,23,24,27], among which the piece-wise line simplification technique is widely used [2, 4, 5, 7, 15-17, 20, 23], due to its distinct advantages: (a) simple and easy to implement, (b) no need of extra knowledge and suitable for freely moving objects, and (c) bounded errors with good compression ratios [15,27].…”

Section: Introductionmentioning

confidence: 99%

One-pass trajectory simplification using the synchronous Euclidean distance

Lin

Jiang

et al. 2019

The VLDB Journal

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Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. To attack this issue, one-pass line simplification (LS) algorithms have are been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (SED), and cannot support spatio-temporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (CISED-S and CISED-W) using SED, based on a novel spatio-temporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms CISED-S and CISED-W are on average 3 times faster than SQUISH-E (the most efficient existing LS algorithm using SED). In terms of compression ratios, algorithms CISED-S and CISED-W are comparable with and 19.6% better than DPSED (the most effective existing LS algorithm using SED) on average, respectively, and are 21.1% and 42.4% better than SQUISH-E on average, respectively.

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Trajic: An Effective Compression System for Trajectory Data

Cited by 52 publications

References 15 publications

GraCT: A Grammar-based Compressed Index for Trajectory Data

GraCT: A Grammar-based Compressed Index for Trajectory Data

One-pass error bounded trajectory simplification

One-pass trajectory simplification using the synchronous Euclidean distance

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