Feature Selection over Distributed Data Streams through Convex Optimization

Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recent contributions based on geometric ideas we present an alternative approach. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through constraints applied separately on each stream. We report numerical experiments on a real–world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature. Keyword list: data streams, distributed system, convex optimization