Simplifying big data analytics systems with a reference architecture
Authors: Sang, G.M., Xu, L. and de Vrieze, P.
Journal: IFIP Advances in Information and Communication Technology
Volume: 506
Pages: 242-249
ISBN: 9783319651507
ISSN: 1868-4238
DOI: 10.1007/978-3-319-65151-4_23
Abstract:The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services.
https://eprints.bournemouth.ac.uk/29353/
Source: Scopus
Simplifying Big Data Analytics Systems with a Reference Architecture
Authors: Sang, G.M., Xu, L. and de Vrieze, P.
Journal: COLLABORATION IN A DATA-RICH WORLD
Volume: 506
Pages: 242-249
eISSN: 1868-422X
ISSN: 1868-4238
DOI: 10.1007/978-3-319-65151-4_23
https://eprints.bournemouth.ac.uk/29353/
Source: Web of Science (Lite)
Simplifying Big Data Analytics System with A Reference Architecture
Authors: Sang G, Xu, L. and de Vrieze, P.
Conference: 18th IFIP Working Conference on Virtual Enterprises (PRO-VE 2017)
Dates: 18-20 September 2017
Journal: Springer
Abstract:The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services.
https://eprints.bournemouth.ac.uk/29353/
Source: Manual
Simplifying Big Data Analytics System with A Reference Architecture
Authors: Sang, G., Xu, L. and de Vrieze, P.T.
Conference: 18th IFIP Working Conference on Virtual Enterprises (PRO-VE 2017)
Abstract:The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services.
https://eprints.bournemouth.ac.uk/29353/
Source: BURO EPrints