Execution and optimization of continuous windowed aggregation queries

The desire of companies to analyze web-site activity data quickly in order to show personalized content and advertisements to users has led to renewed interest in continuous query processing. One important query class here is windowed aggregation which does time-based windowing followed by grouping and aggregation over a data stream. An example query may aggregate each user's activity over a recent one hour window, and update the result every five minutes. In this paper, we characterize the rich execution plan space for windowed aggregation queries. No such attempt has been made previously to the best of our knowledge. Our second contribution is in developing a cost-based optimizer to pick a good plan from this space for a given query. Finally, we show the effectiveness of the cost-based optimizer.