Titre : A Context-Driven Data Visualization Engine for Improved Citizen Service and Government Performance 

Auteurs : Feras Batarseh, Jay Gendron, Rafael Laufer, Mythili Madhavaram, Abhinav Kumar,  

Revue : Modeling and Using Context 

Numéro : Issue 1 

Volume : 2 

Date : 2018/11/20 

DOI : 10.21494/ISTE.OP.2018.0303 

ISSN : 2514-5711 

Résumé : Every day, the US government creates and consumes significant amounts of data. Federal agencies are finally riding the wave of Big Data Analytics for solving problems such as improving citizen service. Domestic tranquility, customer satisfaction, transparency, and providing quality service are among the many critical goals that most government agencies aim to achieve through citizen service. Federal, State, County, and City governments are constantly challenged by the overwhelming number of service requests from citizens, organizations, scholars, the media, and many other entities. Governments, however, are constantly facing accountability issues that can call into question their role and efficiency. This paper, through the use of data science and context, introduces a novel engine that could be used at any federal agency to improve citizen service, evaluate performance metrics, and provide pointers to enhance satisfaction rates. The model used in the engine (called iGPS) is deployed through a number of real-world citizen’s complaints datasets. Insightful and actionable data visualizations are introduced to federal employees depending on their context. - The goal is to aid them in decision-making. The model is tested for the first time (through six governmental datasets from 46 agencies), machine learning models are developed, visualizations are built, the system is deployed at the US government, and experimental results are recorded and presented. 

Éditeur : ISTE OpenScience