Paper Info
Title
Query optimization over crowdsourced data
Abstract
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 language, and query execution engine presented earlier. Deco's objective 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 estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.
Year
DOI
Venue
2013
10.14778/2536206.2536207
PVLDB
Keywords
DocType
Volume
cost-based query optimizer,query optimizer,crowdsourced data,query optimization,query execution,query semantics,data model,traditional query optimization,query language,query execution engine,query plan
Journal
6
Issue
ISSN
Citations 
10
2150-8097
20
PageRank 
References 
Authors
0.70
20
2
Name
Order
Citations
PageRank
Hyunjung Park132013.71
Jennifer Widom2161502524.75