A scalable Cloud-based system for data-intensive spatial analysis

被引:0
作者
R. O. Sinnott
W. Voorsluys
机构
[1] University of Melbourne,Department of Computing and Information Systems
来源
International Journal on Software Tools for Technology Transfer | 2016年 / 18卷
关键词
e-Infrastructure; Urban research; Cloud computing ; Geospatial systems; Spatial analysis;
D O I
暂无
中图分类号
学科分类号
摘要
Advances in Cloud computing technology and the availability of affordable and easy to use Cloud services are enabling a multitude of scientific applications to use these resources as primary or secondary computing infrastructure. The urban and built environment research domain is one area that can benefit greatly from Cloud computing. The global population growth and increase in the size and population of cities raise many challenges for governments, planners and researchers alike. The Australian Urban Research Infrastructure Network (AURIN—http://www.aurin.org.au) project has been tasked with developing an advanced platform (e-Infrastructure) across Australia to tackle these challenges. The platform leverages large-scale Cloud resources to provide federated data access to, at present over 1100 data sets from major and often definitive government and industry data-rich organisations, and for scalable data processing and visualisation. The original AURIN tools were developed using the object modelling system (OMS) and supported integrated workflows to define and enact/re-enact scientific processes. More recently the work has evolved to focus more on delivery of a workbench offering a rich range of tools delivered through an extensible workflow environment. In this paper, we provide the background to AURIN including the scientific drivers that are shaping the work and the realisation of the Cloud-based AURIN environment. We focus in particular on the workflow environment and show how it seamlessly utilizes the Cloud for urban research processes focused especially on data-intensive spatial analysis. We illustrate the utilisation of this workflow environment across a range of case studies reflecting urban research activities.
引用
收藏
页码:587 / 605
页数:18
相关论文
共 50 条
  • [21] Application of Cloud-based Data Analysis in Applied Mathematics
    Liu, Xiaoyu
    Zhang, Xiang
    2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELLING, AND INTELLIGENT COMPUTING (CAMMIC 2022), 2022, 12259
  • [22] A scalable cloud-based cyberinfrastructure platform for bridge monitoring
    Jeong, Seongwoon
    Hou, Rui
    Lynch, Jerome P.
    Sohn, Hoon
    Law, Kincho H.
    STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2019, 15 (01) : 82 - 102
  • [23] Feasibility and demonstration of a cloud-based RIID analysis system
    Wright, Michael C.
    Hertz, Kristin L.
    Johnson, William C.
    Sword, Eric D.
    Younkin, James R.
    Sadler, Lorraine E.
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2015, 784 : 281 - 286
  • [24] An Innovative Cloud-Based System for the Diachronic Analysis in Numismatics
    Celesti, Antonio
    Salamone, Grazia
    Sapienza, Anna
    Spinelli, Marianna
    Puglisi, Mariangela
    Caltabiano, Maria
    ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, 2017, 10 (04):
  • [25] Implementation of a Cloud-based Blood Pressure Data Management System
    Kuo, Mu-Hsing
    DIGITAL HEALTHCARE EMPOWERING EUROPEANS, 2015, 210 : 882 - 886
  • [26] Improvement of job completion time in data-intensive cloud computing applications
    Ibrahim Adel Ibrahim
    Mostafa Bassiouni
    Journal of Cloud Computing, 9
  • [27] A Cloud-Based Architecture for an Interoperable, Resilient, and Scalable C2 Information System
    Bau, Nico
    Endres, Sven
    Gerz, Michael
    Goekgoez, Fahrettin
    2018 INTERNATIONAL CONFERENCE ON MILITARY COMMUNICATIONS AND INFORMATION SYSTEMS (ICMCIS), 2018,
  • [28] Improvement of job completion time in data-intensive cloud computing applications
    Ibrahim, Ibrahim Adel
    Bassiouni, Mostafa
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2020, 9 (01):
  • [29] Data-intensive Service Mashup Based on Game Theory and Hybrid Fireworks Optimization Algorithm in the Cloud
    Yang, Wanchun
    Zhang, Chenxi
    Mu, Bin
    INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS, 2015, 39 (04): : 421 - 429
  • [30] Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud
    Exposito, Roberto R.
    Taboada, Guillermo L.
    Ramos, Sabela
    Tourino, Juan
    Doallo, Ramon
    COMPUTER JOURNAL, 2016, 59 (03) : 287 - 307