Query optimization over crowdsourced data

Hyunjung Park, Jennifer Widom

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)781-792
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number10
StatePublished - Aug 26 2013
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the NSF (IIS-0904497), the BoeingCorporation, and a KAUST research grant.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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