Efficient online sequence prediction with side information

Han Xiao, Claudia Eckert

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Sequence prediction is a key task in machine learning and data mining. It involves predicting the next symbol in a sequence given its previous symbols. Our motivating application is predicting the execution path of a process on an operating system in real-time. In this case, each symbol in the sequence represents a system call accompanied with arguments and a return value. We propose a novel online algorithm for predicting the next system call by leveraging both context and side information. The online update of our algorithm is efficient in terms of time cost and memory consumption. Experiments on real-world data sets showed that our method outperforms state-of-the-art online sequence prediction methods in both accuracy and efficiency, and incorporation of side information does significantly improve the predictive accuracy.

Original languageEnglish
Article number6729627
Pages (from-to)1235-1240
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
StatePublished - 2013
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: 7 Dec 201310 Dec 2013


  • online learning
  • scalability
  • sequence predictio
  • system trace


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