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Potential Big Data Future Challenges for the GoodTurn System

Authors:
Katherine Snyder
Kevin Daimi
Jacob Vanassche

Keywords: GoodTurn System; Big Data Analytics; Big Data Lifecycle; Prediction; Classification; Clustering

Abstract:
GoodTurn is an application designed and implemented by the University of Detroit Mercy through a grant from Ford Motor Company. It is a goods-moving system. In a manner similar to Uber, the application is aimed at facilitating and managing Ford employees’ donation of their time and vehicles to assist the community by moving their goods and resources. The stakeholders—drivers, donors, and nonprofit/nongovernment organizations (NPO/NGO)— will use their iPhones and Android-based phones to connect to the application free of any charge. Increasing amounts of data are currently being generated. It is anticipated that the data will exceed one terabyte in the next few years. To address the benefits of the availability of future big data, this paper will present potential future challenges of the resulting big data’s analytics. In particular, these challenges will address the decision-making needs of Ford, NPOs/NGOs, drivers, and donors. The paper will not address the implementation of any actual big data analytics using tools as the data is not yet completely developed. However, once the anticipated big data is generated, appropriate tools will be employed to obtain the needed knowledge and uncover the value of the stored data.

Pages: 16 to 23

Copyright: Copyright (c) IARIA, 2018

Publication date: May 20, 2018

Published in: conference

ISSN: 2308-4421

ISBN: 978-1-61208-636-1

Location: Nice, France

Dates: from May 20, 2018 to May 24, 2018