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A Distributed Approach to Finding Complex Dependencies in Data

By Matthew D. Schmill

Abstract

Learning complex dependencies from time series data is an important task � dependencies can be used to make predictions and characterize a source of data. We have developed Multi-Stream Dependency Detection (msdd), a machine learning algorithm that detects complex dependencies in categorical time-series data. dmsdd attempts to balance the search for strong dependencies across a heterogeneous network of workstations. We develop a load balancing policy for dmsdd { rst using only static techniques

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.418.6107
Provided by: CiteSeerX
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