Machine Learning for Machine Data from a CATI Network
This work addresses the challenge of monitoring and classifying uncommon errors in survey data systems, with potential applications in areas like cyberattack detection, though it appears incremental in method.
The paper tackled the problem of predicting rare error events in high-volume, semi-structured machine log files from a CATI network, achieving high-accuracy predictions using NLP and data-mining techniques without needing source code or documentation.
This is a machine learning application paper involving big data. We present high-accuracy prediction methods of rare events in semi-structured machine log files, which are produced at high velocity and high volume by NORC's computer-assisted telephone interviewing (CATI) network for conducting surveys. We judiciously apply natural language processing (NLP) techniques and data-mining strategies to train effective learning and prediction models for classifying uncommon error messages in the log---without access to source code, updated documentation or dictionaries. In particular, our simple but effective approach of features preallocation for learning from imbalanced data coupled with naive Bayes classifiers can be conceivably generalized to supervised or semi-supervised learning and prediction methods for other critical events such as cyberattack detection.