Building real-world trajectory warehouses

Μαρκετοσ, Γερασιμοσ; Frentzos, Elias; Ntoutsi, Irene; Pelekis, Nikos; Raffaetà, Alessandra; Theodoridis, Yannis

doi:10.1145/1626536.1626539

Cited by 66 publications

(101 citation statements)

References 18 publications

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“…Usually they design automatic filtering methods to remove them. In this context, [Marketos et al 2008] proposes an online approach that filters noisy positions by using the maximum speed of the moving object.…”

Section: Trajectory Data Cleaningmentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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___________________________________________________________________Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for i) constructing trajectories from movement tracks, ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the paper surveys the new privacy issues that rise due to the semantic aspects of trajectories.

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Section: Trajectory Data Cleaningmentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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“…For example, Marketos et al propose a parametric online approach that filters noisy positions (outliers) by taking advantage of the maximum allowed speed of the moving object [Marketos et al 2008]. On the other hand, random errors are small distortions from the true values and their influence is decreased by smoothing methods (e.g., [Jun et al 2006] [Schüssler and Axhausen 2009]).…”

Section: Trajectory Data Modelingmentioning

confidence: 99%

Semantic trajectories

Yan

Chakraborty

Parent

et al. 2013

ACM Trans. Intell. Syst. Technol.

164

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With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this paper lies in a Semantic Model and a Computation and Annotation Platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

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“…There are two types of errors: the outliers which are far away from the true values and need to be removed; the noisy data that should be corrected and smoothed. Several works [22][28] [31] design specific filtering methods to remove outliers and smoothing methods to deal with small random errors. Regarding network-constrained moving objects, a number of map matching algorithms have been designed to refine the raw GPS records [2] [16].…”

Section: Related Workmentioning

confidence: 99%

“…The talk regards data cleaning and compression that precede the online segmentation and semantic trajectory computation procedures. Data cleaning is dealing with trajectory errors, including systematic errors (outlier removal) and random errors (smooth noise) [22][31]; compression considers data reduction because trajectory data grow rapidly and lack of compression sooner or later leads to exceeding system capacity [16][23]; segmen-tation is used for dividing trajectories into episodes where each episode is in some sense homogeneous (e.g. sharing similar velocity, direction etc.)…”

Section: Introductionmentioning

confidence: 99%

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Yan

Giatrakos

Katsikaros

et al. 2011

Advances in Spatial and Temporal Databases

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Abstract. Location data generated from GPS equipped moving objects are typically collected as streams of spatiotemporal x, y, t points that when put together form corresponding trajectories. Most existing studies focus on building ad-hoc querying, analysis, as well as data mining techniques on formed trajectories. As a prior step, trajectory construction is evidently necessary for mobility data processing and understanding, including tasks like trajectory data cleaning, compression, and segmentation so as to identify semantic trajectory episodes like stops (e.g. while sitting and standing) and moves (while jogging, walking, driving etc). However, semantic trajectory construction methods in the current literature are typically based on offline procedures, which is not sufficient for real life trajectory applications that rely on timely delivery of computed trajectories to serve real-time query answers. Filling this gap, our paper proposes a platform, namely SeTraStream, for online semantic trajectory construction. Our framework is capable of providing real-time trajectory data cleaning, compression, segmentation over streaming movement data.

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Building real-world trajectory warehouses

Cited by 66 publications

References 18 publications

Semantic trajectories modeling and analysis

Semantic trajectories modeling and analysis

Semantic trajectories

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

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