About: Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
Creator	Zheng, Yu Bao, Jie He, Huajun Li, Ruiyuan Li, Tianrui Wang, Rubin
source	ArXiv
abstract	Virus transmission from person to person is an emergency event facing the global public. Early detection and isolation of potentially susceptible crowds can effectively control the epidemic of its disease. Existing metrics can not correctly address the infected rate on trajectories. To solve this problem, we propose a novel spatio-temporal infected rate (IR) measure based on human moving trajectories that can adequately describe the risk of being infected by a given query trajectory of a patient. Then, we manage source data through an efficient spatio-temporal index to make our system more scalable, and can quickly query susceptible crowds from massive trajectories. Besides, we design several pruning strategies that can effectively reduce calculations. Further, we design a spatial first time (SFT) index, which enables us to quickly query multiple trajectories without much I/O consumption and data redundancy. The performance of the solutions is demonstrated in experiments based on real and synthetic trajectory datasets that have shown the effectiveness and efficiency of our solutions.
has issue date	2020-04-11(xsd:dateTime)
has license	arxiv
sha1sum (hex)	23202067b68fabd242f610252e7813b2ef116710
resource representing a document's title	Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
resource representing a document's body	covid:23202067b68fabd242f610252e7813b2ef116710#body_text
is schema:about of	named entity 'isolation' named entity 'system' named entity 'facing' named entity 'event' named entity 'real' named entity 'effectively' named entity 'time' named entity 'transmission' named entity 'STRATEGIES' named entity 'INFECTED' named entity 'VIRUS' named entity 'REDUNDANCY' named entity 'PUBLIC' named entity 'REAL' named entity '2019' named entity 'metrics' named entity 'person' named entity 'trajectory' named entity 'trajectories' named entity 'manage' named entity 'emergency' named entity 'enables' named entity 'I/O' named entity 'correlation' named entity 'drinking water' named entity 'data set' named entity 'sampling rate' named entity 'leaf node' named entity 'cholera' named entity 'Suqian' named entity 'mbr' named entity 'database' named entity 'time complexity' named entity 'scalability' named entity 'outlier' named entity 'Shang' named entity 'XZ2' named entity 'GPS' named entity 'Algorithm' named entity 'COVID-19 pandemic' named entity 'T-Tree' named entity 'HBase' named entity 'time dimension' named entity 'xz2' named entity 'one-dimensional' named entity 'IRQ' named entity 'algorithm' named entity 'pseudocode' named entity 'database' named entity 'incubation period' named entity 'Algorithm' named entity 'GPS' named entity 'matching distance' named entity 'Coronavirus infection' named entity 'database' named entity 'database' named entity 'high-risk' named entity 'algorithm' named entity 'incubation period' named entity 'optimal matching' named entity 'prune' named entity 'IRP' named entity 'algorithm' named entity 'correlation' named entity 'IRQ' named entity 'Euclidean distance' named entity 'correlation' named entity 'IRQ' named entity 'IRP' named entity 'T-Tree' named entity 'XZ2' named entity 'database' named entity 'vaccine' named entity 'Algorithm' named entity 'database' named entity 'database' named entity 'database' named entity 'data redundancy' named entity 'GPS' named entity 'correlation' named entity 'virus' named entity 'quadtree'

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