Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies

Abstract

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure for effectively integrating such data to generate more value and gain insights is still not well-addressed in the research literature. In this paper, we present our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites. We also introduce several methodologies and applications that we are developing on top of the cyberinfrastructure to support diverse community stakeholders in cities.

Publication
In 38th International Conference on Distributed Computing Systems (ICDCS), IEEE.