Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
MRST: an efficient monitoring technology of summarization on stream data
Journal of Computer Science and Technology
Quality of service in stateful information filters
DMSN '06 Proceedings of the 3rd workshop on Data management for sensor networks: in conjunction with VLDB 2006
Frequency-based load shedding over a data stream of tuples
Information Sciences: an International Journal
Quality-driven resource-adaptive data stream mining?
ACM SIGKDD Explorations Newsletter
Buffer-preposed qos adaptation framework and load shedding techniques over streams
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Processing flows of information: From data stream to complex event processing
ACM Computing Surveys (CSUR)
Complex event processing with T-REX
Journal of Systems and Software
Incremental linear model trees on massive datasets: keep it simple, keep it fast
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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In this demo, we show that intelligent load shedding is essential in achieving optimum results in mining data streams under various resource constraints. The Loadstar system introduces load shedding techniques to classifying multiple data streams of large volume and high speed. Loadstar uses a novel metric known as the quality of decision (QoD) to measure the level of uncertainty in classification. Resources are then allocated to sources where uncertainty is high. To make optimum classification decisions and accurate QoD measurement, Loadstar relies on feature prediction to model the data dropped by the load shedding mechanism. Furthermore, Loadstar is able to adapt to the changing data characteristics in data streams. The system thus offers a nice solution to data mining with resource constraints.