Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDM.2013.31 | 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) |
Keywords | Field | DocType |
sequence prediction, online learning, system trace, scalability | Sequence prediction,Online machine learning,Online algorithm,Data mining,Data set,Return statement,Computer science,Symbol,Side information,System call,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1550-4786 | 2 | 0.38 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Xiao | 1 | 48 | 10.86 |
| Claudia Eckert | 2 | 76 | 13.13 |