An Overview on the Convergence of High Performance Computing and Big Data Processing

Mei, Songzhu; Guan, Hongtao; Wang, Qinglin

doi:10.1109/padsw.2018.8644997

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…Open Sci. 8: 210506 computational load; indeed, models such as JUNE would probably not have been possible prior to 2010 without using a prohibitive amount of computing power, see for instance [14].…”

Section: Introductionmentioning

confidence: 99%

J une : open-source individual-based epidemiology simulation

et al. 2021

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We introduce J une , an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. J une provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply J une to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

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Section: Introductionmentioning

confidence: 99%

J une : open-source individual-based epidemiology simulation

et al. 2021

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“…Nowadays, high-performance storage and computation is an important means of researching and solving challenging problems in various fields [26]. Distributed storage and parallel computing frameworks represented by Hadoop, HBase, Spark, and Flink are gradually being used [27].…”

Section: Storage and Querying Of Trajectorymentioning

confidence: 99%

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

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“…The major cost for this level of detail in the model is in computational load; indeed, models such as JUNE would likely not have been possible prior to 2010 without using a prohibitive amount of computing power, see for instance [14].…”

Section: Introductionmentioning

confidence: 99%

JUNE: open-source individual-based epidemiology simulation

Bullock

Cuesta-Lázaro

Quera-Bofarull

et al. 2020

Preprint

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We introduce JUNE, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity, and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. JUNE provides a suite of flexible parameterisations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper we apply JUNE to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

show abstract

An Overview on the Convergence of High Performance Computing and Big Data Processing

Cited by 5 publications

References 58 publications

J une : open-source individual-based epidemiology simulation

J une : open-source individual-based epidemiology simulation

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

JUNE: open-source individual-based epidemiology simulation

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