@article {10.3844/ajeassp.2017.218.228,
article_type = {journal},
title = {Development of a Dashboard for a Local Food Bank},
author = {Desai, Yogeeta and Jiang, Steven and Davis, Lauren},
volume = {10},
number = {1},
year = {2017},
month = {Mar},
pages = {218-228},
doi = {10.3844/ajeassp.2017.218.228},
url = {https://thescipub.com/abstract/ajeassp.2017.218.228},
abstract = {Hunger relief is one of the major needs during humanitarian emergencies. It presents significant challenges to aid organizations trying to manage data, information and knowledge about the situation or event. Food banks receive donations from a variety of sources to meet their demand. The distribution of donated food to meet the unmet hunger needs is a critical issue faced by the food banks across the nation. This paper presents a methodology to apply interactive dashboards to a food bank's decision making process. The first step of this research focuses on understanding the existing decision making process of a food bank. Data from a local food bank was used for this research. Data mining tools were employed to develop predictive models for food bank decision making. Appropriate visualization techniques were identified that can be used to visualize the data mining results. An interactive dashboard was developed and evaluated to enable effective decision making. The results indicated that interactive dashboards were highly effective as compared to the traditional data retrieval system for decision making in food bank operations. The new methodology can be extended to other food banks and hunger-relief organizations which deal with big data.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}