Topology selection for stream mining systems

Authors:
Raphael Ducasse;Deepak S. Turaga;Mihaela van der Schaar
Affiliations:
University of California, Los Angeles, Los Angeles, CA, USA;IBM research, New York, CA, USA;UCLA, Los Angeles, CA, USA
Venue:
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Year:
2009

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Abstract

Multi-concept identification in high volume multimedia streams is critical for a number of applications, including large-scale multimedia analysis, processing, and retrieval. Content of interest is filtered using a collection of binary classifiers that are deployed on distributed resource-constrained infrastructure. In this paper, we design distributed algorithms for determining the optimal topology of single concept detectors (classifiers) to identify the multiple concepts of interest. These algorithms dynamically order individual classifiers into chain topologies to tradeoff accuracy against processing delay, based on underlying data characteristics, system resource constraints as well as the performance and complexity characteristics of each classifier