Graph analysis of network flow connectivity behaviors

Hu, Hai‐Jie; Zhai, Xuemeng; Wang, Mingda; Hu, Guangmin

doi:10.3906/elk-1808-148

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Thus, graph base and topological data mining provide knowledge representation base on graph analysis to construct novel queries for the analysis of complex and dynamic network-related problems. Such as analyses of network connectivity behaviour [ 29 ], patterns and impacts of network systems, continuous Spatio-temporal trajectory joins [ 30 ], etc. Here we discuss the construction of a generalised index data structure for processing the trajectory of AP based on graph analysis.…”

Section: Methodsmentioning

confidence: 99%

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

Adu-Gyamfi

Zhang

Ansah

2020

BMC Med Inform Decis Mak

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Background A pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP. Methods In this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance. Results The EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic. Conclusions The EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic.

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Section: Methodsmentioning

confidence: 99%

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

Adu-Gyamfi

Zhang

Ansah

2020

BMC Med Inform Decis Mak

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“…Thus, graph base and topological data mining provide knowledge representation base on graph analysis to construct novel queries for the analysis of complex and dynamic network related problems. Such as analyses of network connectivity behavior (Hu, et al, 2019), patterns and impacts of network systems, continuous spatio-temporal trajectory joins (Bakalov & Tsotras, 2006), etc. Here we discuss the construction of a generalised index data structure for processing the trajectory of AP based on graph analysis.…”

Section: Graph Analysis and Index Data Structurementioning

confidence: 99%

Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

Adu-Gyamfi¹,

Zhang

2021

Journal of Information Technology Research

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Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.

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“…traffic dispersion graph -TDG; glej npr. Iliofotou idr., 2007;Le idr., 2011;Cheema, 2013;Hu idr., 2018).…”

Section: Zaznavanje Anomalij V Omrežnem Prometuunclassified