Title: Towards social-media streams search and filtering
This is a 60 months fully funded Dual-Degree Doctoral scholarship at Carnegie Mellon University/Language Technologies Institute and the Universidade NOVA Lisboa.
Scope: Nowadays, streams of Web user data are mostly discarded by current Web information systems. User location, devices, services and other sensors hide specific information consumption patterns that could be identified by online services to better answer consumer needs. Most of this data is only useful during a short period of time and is related to short-lived events, far shorter than the time a batch and non-distributed data mining algorithm needs to timely process large-scale data.
Such velocity and variety of the data streams call new live indexing and search techniques. Descriptive statistics must be collected and used to filter data and create sub-streams for live indexing and analysis. A large variety of big data sources can be explored to infer events, supported by multiple sources of evidence. The applicant will investigate stream analytics and filtering techniques to monitor and index multiple real-time Social-Media streams.
This scholarship is offered in the context of the GoLocal project.
Recruitment: Prospective candidates should have a BSc in Computer Science be fluent in English and have a good background in text information processing and fast active-learning classifiers. Programming skills: Python, Java, C++. Applicants should have EU nationality or live in Portugal for more than 2 years.
How to apply: Submissions of applications will start in October. Please contact us for more details.