The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
Robust Classification for Imprecise Environments
Machine Learning
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
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
Convex Optimization
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Platform Overlays: enabling in-network stream processing in large-scale distributed applications
NOSSDAV '05 Proceedings of the international workshop on Network and operating systems support for digital audio and video
Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
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
On Composing Stream Applications in Peer-to-Peer Environments
IEEE Transactions on Parallel and Distributed Systems
Staying FIT: efficient load shedding techniques for distributed stream processing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
MM '08 Proceedings of the 16th ACM international conference on Multimedia
QoS-Aware Shared Component Composition for Distributed Stream Processing Systems
IEEE Transactions on Parallel and Distributed Systems
Adaptive Multimedia Mining on Distributed Stream Processing Systems
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
| Hi-index | 0.00 |
We consider the problem of optimizing stream mining applications that are constructed as tree topologies of classifiers and deployed on a set of resource constrained and distributed processing nodes (or sensors). The optimization involves selecting appropriate false-alarm detection tradeoffs (operating points) for each classifier to minimize an end-to-end misclassification penalty, while satisfying resource constraints. We design distributed solutions, by defining tree configuration games, where individual classifiers configure themselves to maximize an appropriate local utility. We define the local utility functions and determine the information that needs to be exchanged across classifiers in order to design the distributed solutions. We analytically show that there is a unique pure strategy Nash equilibrium in operating points, which guarantees convergence of the proposed approach. We develop both myopic strategy, where the utility is purely local to the current classifier, and foresighted strategy, where the utility includes impact of classifier's actions on successive classifiers. We analytically show that actions determined based on foresighted strategies improve the end-to-end performance of the classifier tree, by deriving an associated probability bound. We also investigate the impact of resource constraints on the classifier action selections for each strategy, and the corresponding application performance. We propose a learning-based approach, which enables each classifier to effectively adapt to the dynamic changes of resource constraints. We evaluate the performance of our solutions on an application for sports scene classification. We show that foresighted strategies result in better performance than myopic strategies in both resource unconstrained and resource constrained scenarios, and asymptotically approach the centralized optimal solution. We also show that the proposed distributed solutions outperform the centralized solution based on the Sequential Quadratic Programming on average in resource unconstrained scenarios.