Range Queries on Multi-Attribute Trajectories

Xu, Jianqiu; Lu, Hua; Güting, Ralf Hartmut

doi:10.1109/tkde.2017.2787711

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…Moving objects were usually abstracted as points (Ranu et al, 2015), single continuous polynomials (Ni and Ravishankar, 2007), or non-regulated sequences of roads in transportation networks (Sandu Popa et al, 2011). Then, these objects were indexed by the tree's derivatives, for example, B + -trees (Sandu Popa et al, 2011), R-trees (Yue et al, 2018), 3D R-trees (Xu et al, 2018), FN (fixed network) R-trees , PA-trees (Ni and Ravishankar, 2007), and grid trees (Yan et al, 2015). These tree-based indices could facilitate efficient queries on moving objects.…”

Section: Related Workmentioning

confidence: 99%

“…Moving object-oriented operators (i.e., range queries, nearestneighbor queries) were derived to support the analysis of moving objects' relationships. The first common operator is range queries, which specify a value of moving objects to fall within a lower and upper boundary Zhan et al, 2015;Xu et al, 2018;Yue et al, 2018), e.g., finding all the objects of a specific traveler between 7 am and 9 am. Filtering and refining are two important steps of range queries.…”

Section: Related Workmentioning

confidence: 99%

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Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For example, the geo-textual objects in trajectories are associated with the location name, the point category (i.e., restaurants, museums) and etc. We unify the semantics 2 as the attributes of the trajectories into the form of a collection of textual keywords. The trajectories including the locations, timestamps, and descriptive attributers are called multi-attributes trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…There have been some literatures [2]- [5] on multi-attribute trajectories. The similarity study is basic and important in trajectory management system.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Similarity Measurements on Multi-Attributes Trajectories Data

et al. 2019

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With the development of global positioning technology, sensor networks, and smart mobile terminal, a large number of trajectory data are accumulated. Trajectory data contains a wealth of information, including spatiality, time series, and other external descriptive attributes (i.e., features, travelling mode, and so on). Trajectory analysis and mining show the great value. The research of trajectory similarity measurement is the basis of trajectory data management and mining, which plays an important role in trajectory computing. Most trajectory similarity work only focuses on the spatial-temporal features. The addition of multi-attributes to the trajectories changes the trajectory similarity. However, there are few researches focusing on multi-attributes trajectory similarity. In this paper, we propose two novel trajectory similarity measurements, i.e. maximum-minimum trajectory distance and sum of minimum trajectory distance and analyze the correlation among the spatial-temporal similarity and textual similarity. Finally, the measurement validity is verified and visualized through clustering, by both a simulation dataset and a real dataset. INDEX TERMS Trajectory similarity measurement, clustering, big trajectory data, trajectory computing.

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Multi-attribute Trajectory Data Management

2020

Handbook of Big Geospatial Data

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Range Queries on Multi-Attribute Trajectories

Cited by 17 publications

References 13 publications

Relative space-based GIS data model to analyze the group dynamics of moving objects

Relative space-based GIS data model to analyze the group dynamics of moving objects

Approximate Similarity Measurements on Multi-Attributes Trajectories Data

Multi-attribute Trajectory Data Management

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