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Evolutionary computation approach for spatial workload balancing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Evolutionary computation approach for spatial workload balancing. / Abubahia, Ahmed; Bader-El-Den, Mohamed; Haig, Ella.

Proceedings of the Computing Conference 2021. Springer, 2020.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Abubahia, A, Bader-El-Den, M & Haig, E 2020, Evolutionary computation approach for spatial workload balancing. in Proceedings of the Computing Conference 2021. Springer, Computing Conference 2021, London, United Kingdom, 15/07/21.

APA

Abubahia, A., Bader-El-Den, M., & Haig, E. (Accepted/In press). Evolutionary computation approach for spatial workload balancing. In Proceedings of the Computing Conference 2021 Springer.

Vancouver

Abubahia A, Bader-El-Den M, Haig E. Evolutionary computation approach for spatial workload balancing. In Proceedings of the Computing Conference 2021. Springer. 2020

Author

Abubahia, Ahmed ; Bader-El-Den, Mohamed ; Haig, Ella. / Evolutionary computation approach for spatial workload balancing. Proceedings of the Computing Conference 2021. Springer, 2020.

Bibtex

@inproceedings{60c013c351454bc583a332d4da9dac8d,
title = "Evolutionary computation approach for spatial workload balancing",
abstract = "The growing demand for Geographic Information Systems (GIS) calls for high computation reliability to handle vast and complex spatial data processing tasks. A better parallel computing scheme should ensure balanced workload at different data processors to ensure optimal use of computing resources and minimise execution times, which poses more challenges with spatial data due to the nature of having spatial correlations and uneven distributions. In this paper, we propose a spatial clustering approach for workload balance, by using an evolutionary computation method that considers the nature of spatial data, to increase the computation performance for processing GIS polygon-based mapswith massive number of vertices and complex shapes. To evaluate our proposed approach, We proposed two different experimental approaches for comparing our results: (i) Non{merging based experiment, and (ii) merging based experiment. The results demonstrated the advantage of the proposed spatial clustering approach in real GIS map based partitioning scenarios. The advantages and limitations of the proposed approach are discussed and further research directions are highlighted toward a development work by the research community.",
keywords = "computational optimisation, geographic information systems, spatial data, workload balancing, evolutionary computation",
author = "Ahmed Abubahia and Mohamed Bader-El-Den and Ella Haig",
year = "2020",
month = dec,
day = "4",
language = "English",
booktitle = "Proceedings of the Computing Conference 2021",
publisher = "Springer",
note = "Computing Conference 2021 ; Conference date: 15-07-2021 Through 16-07-2021",
url = "https://saiconference.com/Computing",

}

RIS

TY - GEN

T1 - Evolutionary computation approach for spatial workload balancing

AU - Abubahia, Ahmed

AU - Bader-El-Den, Mohamed

AU - Haig, Ella

PY - 2020/12/4

Y1 - 2020/12/4

N2 - The growing demand for Geographic Information Systems (GIS) calls for high computation reliability to handle vast and complex spatial data processing tasks. A better parallel computing scheme should ensure balanced workload at different data processors to ensure optimal use of computing resources and minimise execution times, which poses more challenges with spatial data due to the nature of having spatial correlations and uneven distributions. In this paper, we propose a spatial clustering approach for workload balance, by using an evolutionary computation method that considers the nature of spatial data, to increase the computation performance for processing GIS polygon-based mapswith massive number of vertices and complex shapes. To evaluate our proposed approach, We proposed two different experimental approaches for comparing our results: (i) Non{merging based experiment, and (ii) merging based experiment. The results demonstrated the advantage of the proposed spatial clustering approach in real GIS map based partitioning scenarios. The advantages and limitations of the proposed approach are discussed and further research directions are highlighted toward a development work by the research community.

AB - The growing demand for Geographic Information Systems (GIS) calls for high computation reliability to handle vast and complex spatial data processing tasks. A better parallel computing scheme should ensure balanced workload at different data processors to ensure optimal use of computing resources and minimise execution times, which poses more challenges with spatial data due to the nature of having spatial correlations and uneven distributions. In this paper, we propose a spatial clustering approach for workload balance, by using an evolutionary computation method that considers the nature of spatial data, to increase the computation performance for processing GIS polygon-based mapswith massive number of vertices and complex shapes. To evaluate our proposed approach, We proposed two different experimental approaches for comparing our results: (i) Non{merging based experiment, and (ii) merging based experiment. The results demonstrated the advantage of the proposed spatial clustering approach in real GIS map based partitioning scenarios. The advantages and limitations of the proposed approach are discussed and further research directions are highlighted toward a development work by the research community.

KW - computational optimisation

KW - geographic information systems

KW - spatial data

KW - workload balancing

KW - evolutionary computation

UR - https://saiconference.com/Computing2021/CallforPapers#Guidelines

M3 - Conference contribution

BT - Proceedings of the Computing Conference 2021

PB - Springer

T2 - Computing Conference 2021

Y2 - 15 July 2021 through 16 July 2021

ER -

ID: 25019667