ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Operator scheduling in data stream systems
The VLDB Journal — The International Journal on Very Large Data Bases
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Integrated resource management for data stream systems
Proceedings of the 2005 ACM symposium on Applied computing
Loadstar: load shedding in data stream mining
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Multicriteria Optimization
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A holistic approach for resource-aware adaptive data stream mining
New Generation Computing
Resource adaptive periodicity estimation of streaming data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Data stream mining and resource adaptive computation
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Self-configuring data mining for ubiquitous computing
Information Sciences: an International Journal
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Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work under virtually any dynamic resource constraints. Few approaches exist for stream mining algorithms that are capable to adapt to given constraints, and none of them reflects from the resource adaptation to the resulting output quality. In this paper, we propose a general model to achieve resource and quality awareness for stream mining algorithms in dynamic setups. The general applicability is granted by classifying influencing parameters and quality measures as components of a multiobjective optimization problem. By the use of CluStream as an example algorithm, we demonstrate the practicability of the proposed model.