Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators

Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know value of portfolio for a client; or the AVG of temperatures sensed by a set of sensors. In these queries a client specifies a coherency requirement as part of the query. We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Our technique involves decomposing a client query into sub-queries and executing sub-queries on judiciously chosen data aggregators. We provide a technique for getting the optimal set of sub-queries with their incoherency bounds which satisfies client query’s coherency requirement with least number of refresh messages sent from aggregators to the client. Performance results using real-world traces show that our cost based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.