Back to Search
Start Over
Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander
- Source :
- BigData Congress, 2015 IEEE International Congress on Big Data
- Publication Year :
- 2015
- Publisher :
- IEEE, 2015.
-
Abstract
- The Internet of Things (IoT) is now shaping our cities to make them more connected, convenient, and intelligent. However, this change will highly rely on extracted values and insights from the big data generated by our cities via sensors, devices, and human activities. Many existing studies and projects have been done to make our cities smart, focusing more on how to deploy various sensors and devices and then collect data from them. However, this is just the first step towards smart cities and next step will be to make good use of the collected data and enable context-awareness and intelligence into all kinds of applications and services via a flexible big data platform. In this paper, we introduce the system architecture and the major design issues of a live City Data and Analytics Platform, namely CiDAP. More importantly, we share our experience and lessons learned from building this practical system for a large scale running smart city test bed, SmartSantander. Our work provides a valuable example to future Smart City platform designers so that they can foresee some practice issues and refer to our solution when building their own smart city data platforms.
- Subjects :
- business.industry
Computer science
Scale (chemistry)
Big data
020206 networking & telecommunications
02 engineering and technology
Data science
World Wide Web
Work (electrical)
Analytics
020204 information systems
Server
Smart city
11. Sustainability
0202 electrical engineering, electronic engineering, information engineering
Systems architecture
Internet of Things
business
Subjects
Details
- ISBN :
- 978-1-4673-7278-7
- ISBNs :
- 9781467372787
- Database :
- OpenAIRE
- Journal :
- 2015 IEEE International Congress on Big Data
- Accession number :
- edsair.doi.dedup.....1bacc7a7e86e8d4ca03b8f07e33ee2ee
- Full Text :
- https://doi.org/10.1109/bigdatacongress.2015.91