Profiling Spatial Collectives

Wood, Zena

doi:10.1007/978-3-319-02621-3_7

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Motivated by this, Li et al (2010) relax the temporal constraints and propose the notion of swarm intended as group of objects that move together on disjointed time instants, while Zheng et al (2014) introduce the notion of gathering, which is characterized by core members, which stick to the group, and by objects can enter and leave. Temporal discontinuity has also been studied in Wood (2013) by gathering individuals that move with continuous or intermittent spatial coherence. Three definitions of spatial coherence have been proposed: the first two types maintain the classical connotation of closeness (shared location), while the third type works on the reciprocal behavior of the individuals.…”

Section: Introductionmentioning

confidence: 99%

Using interactions and dynamics for mining groups of moving objects from trajectory data

Loglisci

2017

International Journal of Geographical Information Science

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Recent advances in tracking technology enable the gathering of spatio-temporal data in the form of trajectories. Analyzing trajectories can convey knowledge useful for prominent applications and designing computational solutions for mining groups of moving objects may turn out to be a valuable means for a wide class of problems related to mobility. The task of group mining has been investigated by considering mostly the spatial closeness and similarity of the trajectories, while little attention has been paid to the relationships between the trajectories and time-changing nature of the trajectories. The relationships may provide evidence of interactions between the moving objects. The time-changing nature may provide evidence of dynamics of the movements. Therefore, interactions and dynamics can be sources of information that one can consider to discover new forms of groups. In fact, groups of objects may be of interest not only when the objects move together or move close from each other, but also when they come from different places, change direction, join together and then move away from each other. Motivated by this, we introduce the concept of crews and propose a computational solution to discover crews. A crew gathers moving objects with similar interactions and similar dynamics. The proposed computational solution relies on i) new movement parameters, which explicitly consider interactions and dynamics, and ii) a distance-free clustering algorithm, which groups objects based on the similarity of the movement parameters. We conduct extensive experiments on real-world trajectory data, present a quantitative evaluation of the quality of the crews and perform comparisons with a baseline algorithm and with an algorithm of group pattern mining. The empirical results provide interesting insights on the relevance of some parameters in the construction of the crews.

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Section: Introductionmentioning

confidence: 99%

Using interactions and dynamics for mining groups of moving objects from trajectory data

Loglisci

2017

International Journal of Geographical Information Science

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Special Issue on Spatio-Temporal Theories and Models for Environmental, Urban and Social Sciences: Where Do We Stand?

Claramunt

Stewart

2015

Spatial Cognition & Computation

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. To cite this version :Christophe CLARAMUNT, Kathleen STEWART -Special issue on spatio-temporal theories and models for environmental, urban and social sciences: where do we stand ? -Spatial Cognition and Computation -Vol. 15, n°2, p.61-67 -2015 Any correspondence concerning this service should be sent to the repository Administrator : archiveouverte@ensam.eu

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Normalization of Kazakh Texts

Abdussaitova¹,

Amangeldiyeva²

2019

Proceedings of the Student Research Workshop Associated With RANLP 2019

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Kazakh, like other agglutinative languages, has specific difficulties on both recognition of wrong words and generation the corrections for misspelt words. The main goal of this work is to develop a better algorithm for the normalization of Kazakh texts based on traditional and machine learning methods, as well as the new approach which is also considered in this paper. The procedure of election among methods of normalization has been conducted in a manner of comparative analysis. The results of the comparative analysis turned up successful and are shown in detail.

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Profiling Spatial Collectives

Cited by 3 publications

References 13 publications

Using interactions and dynamics for mining groups of moving objects from trajectory data

Using interactions and dynamics for mining groups of moving objects from trajectory data

Special Issue on Spatio-Temporal Theories and Models for Environmental, Urban and Social Sciences: Where Do We Stand?

Normalization of Kazakh Texts

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