A Distributed Approach for Optimizing Cascaded Classifier Topologies in Real-Time Stream Mining Systems

Abstract

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Big-Data Streaming Applications Scheduling Based on Staged Multi-armed Bandits

“…In [7], a load shedding scheme ensures that dropped load has minimal impact on the benefits of mining and dynamically learns a Markov model to predict feature values of unseen data. Instead of deciding on what fraction of the data to process, as in load shedding, the second set of approaches [1], [2], [3], [4], [5] determine how the available data should be processed given the underlying resource allocation. In these works, individual tasks operate at a different performance level given the resources allocated to them.…”

“…Numerous other works such as [36] considered stronger notions of regret, but the algorithms that achieve sublinear strong regret are computationally intractable. Other approaches such as [31] considered weaker notions of regret, 3. The algorithms we propose in this paper will select the best actions in A r i ðB r i Þ according to an optimality criterion that will be defined later.…”

Error Tolerant Multimedia Stream Processing: There's Plenty of Room at the Top (of the System Stack)

“…Thus, the scalability and robustness options provided by such systems are inherently suitable for error-tolerant designs that adapt system resources according to the desired precision. For example, game-theoretic optimization of distributed classifier chains under precision-complexity constraints was shown to achieve significant resource-precision scalability for speaker recognition from voice recordings [63]. Asanovic et al [64] and Anastasia and Andreopoulos [6] demonstrated that back-propagation learning algorithms with matrix operations are robust to significant noise levels in the performed computations.…”

Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining

“…real-time stream mining applications such as traffic analysis, financial fraud prevention, disaster information management, video surveillance, and online patient monitoring [5], [6]. Stream mining systems rely on sophisticated machine learning techniques and can be conceptually viewed as processing pipelines that identify data of interest by progressively testing it on classifiers.…”