Data Hub Architecture for Smart Cities

Type
Conference Paper

Authors
Koh, Jason
Sandha, Sandeep
Balaji, Bharathan
Crawl, Daniel
Altintas, Ilkay
Gupta, Rajesh
Srivastava, Mani

KAUST Grant Number
KAUST Sensor Initiative

Date
2017-11-06

Abstract
Today large amount of data is generated by cities. Many of the datasets are openly available and are contributed by different sectors, government bodies and institutions. The new data can affect our understanding of the issues faced by cities and can support evidence based policies. However usage of data is limited due to difficulty in assimilating data from different sources. Open datasets often lack uniform structure which limits its analysis using traditional database systems. In this paper we present Citadel, a data hub for cities. Citadel’s goal is to support end to end knowledge discovery cyber-infrastructure for effective analysis and policy support. Citadel is designed to ingest large amount of heterogeneous data and supports multiple use cases by encouraging data sharing in cities. Our poster presents the proposed features, architecture, implementation details and initial results.

Citation
Koh, J., Sandha, S., Balaji, B., Crawl, D., Altintas, I., Gupta, R., & Srivastava, M. (2017). Data Hub Architecture for Smart Cities. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. doi:10.1145/3131672.3137001

Acknowledgements
This research is funded in part by the National Science Foundation under awards # IIS-1636916, IIS-1636879, IIS-1636936, OAC-1640813, CI-1331615 and CSR-1526841, and by the King Abdullah University of Science and Technology under KAUST Sensor Initiative. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the funding agencies.

Publisher
Association for Computing Machinery (ACM)

Conference/Event Name
15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017

DOI
10.1145/3131672.3137001

Additional Links
https://dl.acm.org/doi/10.1145/3131672.3137001

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