A Distributed Approach to Finding Complex Dependencies in Data

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, and then adding in dynamic measures { on canonical machine learning datasets.