A platform for analytics on social networks derived from organisational calendar data

Dominic Davies-Tagg, Ashiq Anjum, Richard Hill

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we present a social network analytics platform with a NoSQL Graph datastore. The platform was developed for facilitating communication, management of interactions and the boosting of social capital in large organisations. As with the majority of social software, our platform requires a large scale of data to be consumed, processed and exploited for the generation of its automated social networks. The platforms purpose is to reduce the cost and effort attributed to managing and maintaining communication strategies within an organisation through the analytics performed upon social networks generated from existing data. The following paper focuses on the process of acquiring and processing redundant calendar data available to all organisations and processing it into a social network that can be analysed.

LanguageEnglish
Title of host publicationBig-Data Analytics and Cloud Computing
Subtitle of host publicationTheory, Algorithms and Applications
EditorsMarcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu, Lu Liu
PublisherSpringer, Cham
Pages147-165
Number of pages19
ISBN (Electronic)9783319253138
ISBN (Print)9783319253114
DOIs
Publication statusPublished - 13 Jan 2016
Externally publishedYes

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Davies-Tagg, D., Anjum, A., & Hill, R. (2016). A platform for analytics on social networks derived from organisational calendar data. In M. Trovati, R. Hill, A. Anjum, S. Y. Zhu, & L. Liu (Eds.), Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications (pp. 147-165). Springer, Cham. https://doi.org/10.1007/978-3-319-25313-8_10
Davies-Tagg, Dominic ; Anjum, Ashiq ; Hill, Richard. / A platform for analytics on social networks derived from organisational calendar data. Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications. editor / Marcello Trovati ; Richard Hill ; Ashiq Anjum ; Shao Ying Zhu ; Lu Liu. Springer, Cham, 2016. pp. 147-165
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Davies-Tagg, D, Anjum, A & Hill, R 2016, A platform for analytics on social networks derived from organisational calendar data. in M Trovati, R Hill, A Anjum, SY Zhu & L Liu (eds), Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications. Springer, Cham, pp. 147-165. https://doi.org/10.1007/978-3-319-25313-8_10

A platform for analytics on social networks derived from organisational calendar data. / Davies-Tagg, Dominic; Anjum, Ashiq; Hill, Richard.

Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications. ed. / Marcello Trovati; Richard Hill; Ashiq Anjum; Shao Ying Zhu; Lu Liu. Springer, Cham, 2016. p. 147-165.

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

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AB - In this paper, we present a social network analytics platform with a NoSQL Graph datastore. The platform was developed for facilitating communication, management of interactions and the boosting of social capital in large organisations. As with the majority of social software, our platform requires a large scale of data to be consumed, processed and exploited for the generation of its automated social networks. The platforms purpose is to reduce the cost and effort attributed to managing and maintaining communication strategies within an organisation through the analytics performed upon social networks generated from existing data. The following paper focuses on the process of acquiring and processing redundant calendar data available to all organisations and processing it into a social network that can be analysed.

KW - Analytics

KW - Graph database

KW - Social capital

KW - Social graph

KW - Social network

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EP - 165

BT - Big-Data Analytics and Cloud Computing

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A2 - Anjum, Ashiq

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ER -

Davies-Tagg D, Anjum A, Hill R. A platform for analytics on social networks derived from organisational calendar data. In Trovati M, Hill R, Anjum A, Zhu SY, Liu L, editors, Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications. Springer, Cham. 2016. p. 147-165 https://doi.org/10.1007/978-3-319-25313-8_10