We observe that in diverse applications ranging from stock trading to traffic monitoring, data streams are continuously monitored by multiple analysts for extracting patterns of interest in real-time. Such complex pattern mining requests cover a broad range of popular mining query types, including detection of clusters, outliers, nearest neighbors, and top-k requests. These analysts often submit similar pattern mining requests yet customized with different parameter settings. In this work, we exploit classical principles for core database technology, namely, multi-query optimization, now in the context of data mining.

ElkeRundensteiner.pdf