Abstract
Social media has become an important platform to gauge public opinion on topics related to our daily lives. In practice, processing these posts requires big data analytics tools since the volume of data and the speed of production overwhelm single-server solutions. Building an application to capture and analyze posts from social media can be a challenge simply because it requires combining a set of complex software tools that often times are tricky to configure, tune, and maintain. In many instances, the application ends up being an assorted collection of Java/Scala programs or Python scripts that developers cobble together to generate the data products they need. In this paper, we present the Twitter Health Surveillance (THS) application framework. THS is designed as a platform to allow end-users to monitor a stream of tweets, and process the stream with a combination of built-in functionality and their own user-defined functions. We discuss the architecture of THS, and describe its implementation atop the Apache Hadoop Ecosystem. We also present several lessons learned while developing our current prototype.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings. IEEE International Congress on Big Data
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
