Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conjugate Directions for Stochastic Gradient Descent
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Understanding The Linux Kernel
Understanding The Linux Kernel
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Dynamic instrumentation of production systems
ATEC '04 Proceedings of the annual conference on USENIX Annual Technical Conference
Conditional Random Fields for Intrusion Detection
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Finding hierarchical heavy hitters in streaming data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning SQL for Database Intrusion Detection Using Context-Sensitive Modelling (Extended Abstract)
DIMVA '09 Proceedings of the 6th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Operating System Concepts
Dynamic AspectC++: Generic Advice at Any Time
Proceedings of the 2009 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the Eighth SoMeT_09
CiAO: an aspect-oriented operating-system family for resource-constrained embedded systems
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Introduction to data mining for sustainability
Data Mining and Knowledge Discovery
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Mobile devices are a special class of resource-constrained embedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained resources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Timeconsuming operations have to be started early on. For instance, loading files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system's operation. Our goal is to adjust such strategies on the basis of logged system data. Optimization is then achieved by predicting an application's behavior based on facts learned from earlier runs on the same system. In this paper, we analyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices.