Incremental Mining for Facility Management

Modern buildings are equipped with high-tech systems that take care of several fundamental aspects, e.g., air-conditioning, heating and water supply. The requirements posed on facility management by such buildings are challenging. Modern techniques implement adaptive control systems to achieve this, in which decisions are preferably based on the results of (multiple correlated) mining tasks on recently gathered sensor data. In this work, we discuss the general relationship between such control systems and the underlying mining tasks. We exemplary choose change detection in the context of pattern analysis as a representative, because this mining task involves general requirements known from stream processing like the need for incremental algorithms, but also poses specific challenges like in-time detection. We present three concrete approaches for this and an according evaluation.