MasterMovelets: discovering heterogeneous movelets for multiple aspect trajectory classification

Ferrero, Carlos Andres; Petry, Lucas May; Älvares, Luís Otávio; Silva, Camila Leite da; Zalewski, Willian; Bogorny, Vânia

doi:10.1007/s10618-020-00676-x

Cited by 12 publications

(27 citation statements)

References 21 publications

(12 reference statements)

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“…The emergence of heterogeneous data has generated discussions about Web services used by SDI [12,29,32,33]. These services need to be inter operable, reusable, and light in order to facilitate the execution of complex data analysis required by SDI applications.…”

Section: Micro-services and Micro-service-based Sdimentioning

confidence: 99%

“…Studies addressing STMO-based SDIs can benefit from research on a new type of STMO as well, designated as holistic trajectories, which are defined as a new type of STMO data with different semantic aspects and dimensions-for example, data extracted from sensors and social networks [32]. According to Ferrero et al [33], a holistic trajectory is a sequence of elements <e 1 , e 2 , . .…”

Section: Semantic Sdi and Semantic Annotationmentioning

confidence: 99%

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Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Vidal-Filho

Times

Filho

et al. 2021

IJGI

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The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by an electronic equipment with embedded sensors, causes the STMO to require services and standards for enabling data documentation and ensuring the quality of STMOs. Spatial Data Infrastructures (SDI), on the other hand, provide a shared interoperable and integrated environment for data documentation. The main challenge is how to lead traditional SDIs to evolve to an STMO document due to the lack of specific metadata standards and services for semantic annotation. This paper presents a new concept of SDI for STMO, named SDI4Trajectory, which supports the documentation of different types of STMO—holistic trajectories, for example. The SDI4Trajectory allows us to propose semi-automatic and manual semantic enrichment processes, which are efficient in supporting semantic annotations and STMO documentation as well. These processes are hardly found in traditional SDIs and have been developed through Web and semantic micro-services. To validate the SDI4Trajectory, we used a dataset collected by voluntary users through the MyTracks application for the following purposes: (i) comparing the semi-automatic and manual semantic enrichment processes in the SDI4Trajectory; (ii) investigating the viability of the documentation processes carried out by the SDI4Trajectory, which was able to document all the collected trajectories.

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Section: Micro-services and Micro-service-based Sdimentioning

confidence: 99%

Section: Semantic Sdi and Semantic Annotationmentioning

confidence: 99%

Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Vidal-Filho

Times

Filho

et al. 2021

IJGI

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show abstract

“…The algorithms Movelets [4] and MASTERMovelets [3] discover relevant subtrajectories, called movelets, without the need of extracting other features. The MASTERMovelets achieves a much better accuracy than Movelets.…”

Section: Related Workmentioning

confidence: 99%

“…Trajectory classification can be defined as the technique of discovering the class label of a moving object based on its trajectories [1]. It is important for several application domains as, for instance, identifying the transportation mode of a moving object [2], the strength level of a hurricane [1], a vessel type (cargo, fish, tourism, etc) [1], the owner user of a trajectory [3], etc.…”

Section: Introductionmentioning

confidence: 99%

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Fast discovery of multidimensional subsequences for robust trajectory classification

Portela¹,

Tyska²,

Bogorny³

2021

Preprint

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Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

Portela

Silva

Carvalho

et al. 2021

Intelligent Systems

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MasterMovelets: discovering heterogeneous movelets for multiple aspect trajectory classification

Cited by 12 publications

References 21 publications

Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Fast discovery of multidimensional subsequences for robust trajectory classification

Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

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