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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An eigenspace update algorithm for image analysis
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Making large-scale support vector machine learning practical
Advances in kernel methods
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Advances in kernel methods
The "DGX" distribution for mining massive, skewed data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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Critical event prediction for proactive management in large-scale computer clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of online failure prediction methods
ACM Computing Surveys (CSUR)
Research on event prediction algorithm based on event sequence semantic
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Learning to predict significant events from sequences of data with categorical features is an important problem in many application areas. We focus on events for system management, and formulate the problem of prediction as a classification problem. We perform co-occurrence analysis of events by means of Singular Value Decomposition (SVD) of the examples constructed from the data. This process is combined with Support Vector Machine (SVM) classification, to obtain efficient and accurate predictions. We conduct an analysis of statistical properties of event data, which explains why SVM classification is suitable for such data, and perform an empirical study using real data.