PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

 

e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 25 (S) Oct. 2017 / JST-S0397-2011

 

Automated Update of Crowdsourced Data in Participatory Sensing: An Application for Crowdsourced Price Information

Fakhrul Syafiq, Huzaifah Ismail, Hazleen Aris and Syakiruddin Yusof

Pertanika Journal of Science & Technology, Volume 25, Issue S, October 2017

Keywords: Automated algorithm, big data, crowdsourcing application, crowdsourced data, data deletion, data management, price information

Published on: 18 Jan 2018

Widespread use of mobile devices has resulted in the creation of large amounts of data. An example of such data is the one obtained from the public (crowd) through open calls, known as crowdsourced data. More often than not, the collected data are later used for other purposes such as making predictions. Thus, it is important for crowdsourced data to be recent and accurate, and this means that frequent updating is necessary. One of the challenges in using crowdsourced data is the unpredictable incoming data rate. Therefore, manually updating the data at predetermined intervals is not practical. In this paper, the construction of an algorithm that automatically updates crowdsourced data based on the rate of incoming data is presented. The objective is to ensure that up-to-date and correct crowdsourced data are stored in the database at any point in time so that the information available is updated and accurate; hence, it is reliable. The algorithm was evaluated using a prototype development of a local price-watch information application, CrowdGrocr, in which the algorithm was embedded. The results showed that the algorithm was able to ensure up-to-date information with 94.9% accuracy.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID

JST-S0397-2011

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