On selecting the largest element in spite of erroneous information
4th Annual Symposium on Theoretical Aspects of Computer Sciences on STACS 87
Computing with unreliable information
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Computing with Noisy Information
SIAM Journal on Computing
Finding the maximum and minimum
Discrete Applied Mathematics
On fault-tolerant networks for sorting
On fault-tolerant networks for sorting
Sorting and searching in the presence of memory faults (without redundancy)
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Computer
Fault tolerant sorting network
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Sorting and Selection with Imprecise Comparisons
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Proceedings of the VLDB Endowment
Designing reliable algorithms in unreliable memories
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Optimal resilient sorting and searching in the presence of memory faults
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
Resilient algorithms and data structures
CIAC'10 Proceedings of the 7th international conference on Algorithms and Complexity
Quality control for comparison microtasks
Proceedings of the First International Workshop on Crowdsourcing and Data Mining
Using the crowd for top-k and group-by queries
Proceedings of the 16th International Conference on Database Theory
Leveraging transitive relations for crowdsourced joins
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
WiseMarket: a new paradigm for managing wisdom of online social users
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 22nd international conference on World Wide Web
Answering planning queries with the crowd
Proceedings of the VLDB Endowment
Community-based bayesian aggregation models for crowdsourcing
Proceedings of the 23rd international conference on World wide web
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Our work investigates the problem of retrieving the maximum item from a set in crowdsourcing environments. We first develop parameterized families of max algorithms, that take as input a set of items and output an item from the set that is believed to be the maximum. Such max algorithms could, for instance, select the best Facebook profile that matches a given person or the best photo that describes a given restaurant. Then, we propose strategies that select appropriate max algorithm parameters. Our framework supports various human error and cost models and we consider many of them for our experiments. We evaluate under many metrics, both analytically and via simulations, the tradeoff between three quantities: (1) quality, (2) monetary cost, and (3) execution time. Also, we provide insights on the effectiveness of the strategies in selecting appropriate max algorithm parameters and guidelines for choosing max algorithms and strategies for each application.