C4.5: programs for machine learning
C4.5: programs for machine learning
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
DS '99 Proceedings of the Second International Conference on Discovery Science
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Run-time correlation engine for system monitoring and testing
ICAC-INDST '09 Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
Scalable Run-Time Correlation Engine for Monitoring in a Cloud Computing Environment
ECBS '10 Proceedings of the 2010 17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improving software diagnosability via log enhancement
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Active learning using on-line algorithms
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Contributions to the study of SMS spam filtering: new collection and results
Proceedings of the 11th ACM symposium on Document engineering
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Nowadays, enterprise software systems store a large amount of operational information in logs. Manually analysing these data can be time-consuming and error-prone. Although a static knowledge database eases the task to capture recurring problems, maintaining such a knowledge repository requires periodic knowledge updates by domain experts. Moreover, as the repository grows, the problem of memory efficiency will also arise. Our goal is to enable administrators to efficiently capture interesting data in a high volume stream of events in real-time. We are proposing a statistical approach for software applications to be automatically trained with a smaller dataset to efficiently predict interesting data under such conditions. The proposed solution maintains a stable memory usage by migrating keywords from a dynamic data structure to fixed sized data structures (Bloom Filter). In particular, the solution has achieved better prediction results by enhancing the Bayesian theory with belief modifiers.