Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPADS.2012.133
Title: Hidden Markov Models for abnormal event processing in transportation data streams
Authors: Lau, J.K.-S. 
Tham, C.-K. 
Keywords: Big data
Event processing
Event-driven architecture
Hidden Markov Models
Metadata
Public transport
Issue Date: 2012
Source: Lau, J.K.-S., Tham, C.-K. (2012). Hidden Markov Models for abnormal event processing in transportation data streams. Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS : 816-821. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPADS.2012.133
Abstract: Making sense of big data and big metadata remains a challenge as more and more data are churned out every day. The problem of adding value to unstructured data requires the application of computationally intensive algorithms to discover useful patterns in the data. In terms of data streams from public transport such as buses, we address the problem of performing time-consuming algorithms to model the data while still being able to process abnormal events in real-time. We propose using Hidden Markov Models (HMMs) for identifying conditions for an abnormal event in bus journeys and methods for isolating HMM computations from real-time event processing. Results show that training HMMs with even noisy metadata can generate models that can recognize an abnormal event in a parallel and distributed manner in the cloud. © 2012 IEEE.
Source Title: Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
URI: http://scholarbank.nus.edu.sg/handle/10635/70463
ISBN: 9780769549033
ISSN: 15219097
DOI: 10.1109/ICPADS.2012.133
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