Adaptive, Automatic Stream Mining

Sensor devices and embedded processors are becoming widespread, especially in measurement/monitoring applications. Their limited resources (CPU, memory and/or communication bandwidth and power) pose some interesting challenges. We need concise, expressive models to represent the important features of the data, and lend themselves to efficient estimation. In particular, under these severe constraints, we want models and estimation methods which (a) require little memory and a single pass over the data, (b) can adapt and handle arbitrary periodic components, and (c) can deal with various types of noise. We propose AWSOM (Arbitrary Window Stream mOdeling Method), which allows sensors in remote or hostile environments to efficiently and effectively discover interesting patterns and trends. This can be done automatically, i.e., with no prior inspection of the data or any user intervention and expert tuning before or during data gathering. Our algorithms require limited resources and can thus be incorporated in sensors—possibly alongside a distributed query processing engine. Updates are performed in constant time with respect to stream size, using logarithmic space. Existing forecasting methods (SARIMA, GARCH, etc.) or “traditional” Fourier and wavelet analysis fall short on one or more of these requirements. To the best of our knowledge, AWSOM is the first framework that combines all of the above characteristics.