Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Information-Theoretic Measures for Anomaly Detection
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Convex Optimization
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Entropy-based Concept Shift Detection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Adaptive concept drift detection
Statistical Analysis and Data Mining - Best of SDM'09
Change detection in learning histograms from data streams
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
A distribution-based approach to anomaly detection and application to 3G mobile traffic
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Virtual Resources Allocation for Workflow-Based Applications Distribution on a Cloud Infrastructure
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
TRANSLATIONS OF SERVICE LEVEL AGREEMENT IN SYSTEMS BASED ON SERVICE-ORIENTED ARCHITECTURES
Cybernetics and Systems - SMART MODELING SUPPORT FOR MANAGING COMPLEXITIES AND DYNAMICS OF KNOWLEDGE-BASED SYSTEMS—PART 1
On-line bayesian context change detection in web service systems
Proceedings of the 2013 international workshop on Hot topics in cloud services
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In this paper, the problem of detecting the major changes in the stream of service requests is formulated. The change of stream component varies over time and depends on, e.g., a time of a day. The underlying cause of the change is called a context. Hence, at each moment there exists a probability distribution determining the probability of requesting the system service conditioned by the context. The aim is to find such a moment in which the distributions change. To solve that problem dissimilarity measures between two probability distributions are given. Nevertheless, detecting every change is not interesting but only long-lasting changes because of the costs of the service system resources reallocation. Therefore, in the proposed algorithm an issue of sensitivity to temporary changes detection is considered.