DocumentCode :
3006791
Title :
Data Abstraction and Visualisation in Next Step: Experiences from a Government Services Delivery Trial
Author :
Bista, Sanat Kumar ; Nepal, Surya ; Paris, Cecile
Author_Institution :
ICT Centre, CSIRO, Canberra, ACT, Australia
fYear :
2013
fDate :
June 27 2013-July 2 2013
Firstpage :
263
Lastpage :
270
Abstract :
Online Communities offering support services from a government body can attract a large number of participants and thus grow quickly in terms of interaction volume. This big data can pose a challenge to the community members as well as moderators in reviewing the development of discussions and in understanding the social behaviour of the community. For the data to be useful, it is thus important to employ appropriate data abstraction and visualisation tools. We implemented a gamification based data abstraction and social behaviour based visualisation method in the context of Next Step, a year long trial conducted in partnership with the Australian Government Department of Human Services to deliver support services to citizens in a transitioning phase. The trial has been useful in drawing lessons that could be important for a scaled-up implementation of such government services. In this paper, we present our experiences from the implementation and discuss future prospects.
Keywords :
behavioural sciences; data structures; data visualisation; government data processing; social sciences computing; Australian Government Department of Human Services; data abstraction; data visualisation; government services delivery trial; online communities; social behaviour; Communities; Context; Data handling; Data visualization; Government; Information management; Market research; Data Abstraction; Data Visualisation; Gamificaiton; Government Services Delivery; Online Community; Social Behaviour;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
Type :
conf
DOI :
10.1109/BigData.Congress.2013.42
Filename :
6597146
Link To Document :
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