Interactive data exploration using semantic windows

We present a new interactive data exploration approach, called Semantic Windows (SW), in which users query for multidimensional "windows" of interest via standard DBMS-style queries enhanced with exploration constructs. Users can specify SWs using (i) shape-based properties, e.g., "identify all 3-by-3 windows", as well as (ii) content-based properties, e.g., "identify all windows in which the average brightness of stars exceeds 0.8". This SW approach enables the interactive processing of a host of useful exploratory queries that are difficult to express and optimize using standard DBMS techniques. SW uses a sampling-guided, data-driven search strategy to explore the underlying data set and quickly identify windows of interest. To facilitate human-in-the-loop style interactive processing, SW is optimized to produce online results during query execution. To control the tension between online performance and query completion time, it uses a tunable, adaptive prefetching technique. To enable exploration of big data, the framework supports distributed computation.