On Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications

Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

Social sensing is a new big data application paradigm for Cyber-Physical Systems (CPS), where a group of individuals volunteer (or are recruited) to report measurements or observations about the physical world at scale. A fundamental challenge in social sensing applications lies in discovering the correctness of reported observations and reliability of data sources without prior knowledge on ei...

Scalable and Robust Group Discovery on Large Transactional Data

The need for time-critical analysis and understanding of the underlying group structure from transactional data has been growing in domains such as law enforcement and customs. Kubica et al. (2003) proposed k-groups, an algorithm based on probabilistic generative model for discovering underlying groups in data. Even though k-groups is reported to be signficantly faster than its predecessor GDA ...

Scalable Data Processing for Community Sensing Applications

Participatory Sensing is a new computing paradigm that aims to turn personal mobile devices into advanced mobile sensing networks. For popular applications, we can expect a huge number of users to both contribute with sensor data and request information from the system. In such scenario, scalability of data processing becomes a major issue. In this paper, we present a system for supporting part...

Visualizing Communication on Social Media: Making Big Data Accessible

The broad adoption of the web as a communication medium has made it possible to study social behavior at a new scale. With social media networks such as Twitter, we can collect large data sets of online discourse. Social science researchers and journalists, however, may not have tools available to make sense of large amounts of data or of the structure of large social networks. In this paper, w...

Processing unstructured documents and social media using Big Data techniques