Efficient Execution of Continuous Aggregate Queries over Multi-Source Streaming Data

On-line decision making often involves query processing over time-varying data which arrives in the form of data streams from distributed locations. Examples of time-varying data include financial information such as stock prices and currency exchange rates, real-time traffic, weather information and data from process control applications. In such environments, typically a decision is made whenever some function of the current value of a set of data items satisfies a threshold criterion. For example, when the traffic entering a highway exceeds a prespecified limit, some flow control measure is initiated; when the value of a stock portfolio goes below a comfort level, an investor might decide to rethink his portfolio management strategy. In this paper we present data dissemination and query processing techniques where such queries access data from multiple sources. Key challenges in supporting such Continuous Aggregate Queries with Thresholds lie in minimizing network and source overheads, without the loss of fidelity in the responses provided to users. Using real world data we demonstrate the superior performance of our techniques when compared to alternatives based on periodic independent polling of the sources