Query optimization over crowdsourced data

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco’s cost-based query optimizer, building on Deco’s data model, query lan-guage, and query execution engine presented earlier. Deco’s objec-tive in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco’s query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco’s query optimizer include a cost model distinguishing between “free ” existing data versus paid new data, a cardinality es-timation algorithm coping with changes to the database state dur-ing query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challeng-ing. We experimentally evaluate Deco’s query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enu-meration. 1