Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
Data networks
Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
QoS-Driven Load Shedding on Data Streams
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Supporting timeliness and accuracy in distributed real-time content-based video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Distributed Stream Management using Utility-Driven Self-Adaptive Middleware
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Improving classifier utility by altering the misclassification cost ratio
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Dealing with Overload in Distributed Stream Processing Systems
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Multi-site cooperative data stream analysis
ACM SIGOPS Operating Systems Review
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Resource Management for Networked Classifiers in Distributed Stream Mining Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Proceedings of the international conference on Multimedia
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Networks of classifiers can offer improved accuracy and scalability over single classifiers by utilizing distributed processing resources and analytics. However, they also pose a unique combination of challenges. First, classifiers may be located across different sites that are willing to cooperate to provide services, but are unwilling to reveal proprietary information about their analytics, or are unable to exchange their analytics due to the high transmission overheads involved. Furthermore, processing of voluminous stream data across sites often requires load shedding approaches, which can lead to suboptimal classification performance. Finally, real stream mining systems often exhibit dynamic behavior and thus necessitate frequent reconfiguration of classifier elements to ensure acceptable end-to-end performance and delay under resource constraints. Under such informational constraints, resource constraints, and unpredictable dynamics, utilizing a single, fixed algorithm for reconfiguring classifiers can often lead to poor performance. In this paper, we propose a new optimization framework aimed at developing rules for choosing algorithms to reconfigure the classifier system under such conditions. We provide an adaptive, Markov model-based solution for learning the optimal rule when stream dynamics are initially unknown. Furthermore, we discuss how rules can be decomposed across multiple sites and propose a method for evolving new rules from a set of existing rules. Simulation results are presented for a speech classification system to highlight the advantages of using the rules-based framework to cope with stream dynamics.