Multi-query outlier detection over data streams: poster.

Real-time analytics of anomalous phenomena on streaming data typically relies on processing a large variety of continuous outlier detection requests, each configured with different parameter settings. The processing of such complex outlier analytics workloads is resource consuming due to the algorithmic complexity of the outlier mining process. In this work we propose a sharing-aware multi-query execution strategy for outlier detection on data streams called SOP. The key insight of SOP is to transform the problem of handling a multi-query outlier analytics workload into a single-query skyline computation problem. SOP achieves minimal utilization of both computational and memory resources for the processing of these complex outlier analytics workload.