Efficiently updating materialized views
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Predicate migration: optimizing queries with expensive predicates
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query optimization in the presence of limited access patterns
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
WSQ/DSQ: a practical approach for combined querying of databases and the Web
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Query Optimization in the Presence of Foreign Functions
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Querying Heterogeneous Information Sources Using Source Descriptions
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Matching Schemas in Online Communities: A Web 2.0 Approach
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficiently incorporating user feedback into information extraction and integration programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
TurKit: tools for iterative tasks on mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
Human-assisted graph search: it's okay to ask questions
Proceedings of the VLDB Endowment
Turkalytics: analytics for human computation
Proceedings of the 20th international conference on World wide web
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Proceedings of the VLDB Endowment
CrowdScreen: algorithms for filtering data with humans
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
So who won?: dynamic max discovery with the crowd
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Deco: a system for declarative crowdsourcing
Proceedings of the VLDB Endowment
Query optimization over crowdsourced data
Proceedings of the VLDB Endowment
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
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Crowdsourcing enables programmers to incorporate "human computation" as a building block in algorithms that cannot be fully automated, such as text analysis and image recognition. Similarly, humans can be used as a building block in data-intensive applications--providing, comparing, and verifying data used by applications. Building upon the decades-long success of declarative approaches to conventional data management, we use a similar approach for data-intensive applications that incorporate humans. Specifically, declarative queries are posed over stored relational data as well as data computed on-demand from the crowd, and the underlying system orchestrates the computation of query answers. We present Deco, a database system for declarative crowdsourcing. We describe Deco's data model, query language, and our prototype. Deco's data model was designed to be general (it can be instantiated to other proposed models), flexible (it allows methods for data cleansing and external access to be plugged in), and principled (it has a precisely-defined semantics). Syntactically, Deco's query language is a simple extension to SQL. Based on Deco's data model, we define a precise semantics for arbitrary queries involving both stored data and data obtained from the crowd. We then describe the Deco query processor which uses a novel push-pull hybrid execution model to respect the Deco semantics while coping with the unique combination of latency, monetary cost, and uncertainty introduced in the crowdsourcing environment. Finally, we experimentally explore the query processing alternatives provided by Deco using our current prototype.