Exploiting Correlated Attributes in Acquisitional Query Processing

Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate the selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heu...

• Type: Conference
• Year: 2005
• Where: ICDE
• Authors: Amol Deshpande, Carlos Guestrin, Wei Hong, Samuel Madden
• Keywords: Candidate Low-cost Attributes, Conditional Plans, Database, ICDE 2005, Optimal Conditional Plan