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. Just as various fragments of a dynamic webpage are served by one or more nodes of a content distribution network, our technique involves decomposing a client query into subqueries and executing subqueries on judiciously chosen data aggregators with their individual subquery incoherency bounds. We provide a technique for getting the optimal set of subqueries with their incoherency bounds which satisfies client query´s coherency requirement with least number of refresh messages sent from aggregators to the client. For estimating the number of refresh messages, we build a query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. 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.