Optimization in Knowledge-Intensive Crowdsourcing

We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers. Human factors designate workers' expertise in different skills, their expected minimum wage, and their availability. In SmartCrowd, we formulate task assignment as an optimization problem, and rely on pre-indexing workers and maintaining the indexes adaptively, in such a way that the task assignment process gets optimized both qualitatively, and computation time-wise. We present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms. We finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in SmartCrowd is necessary to achieve efficient high quality task assignment.Comment: 12 page

On the Complexity of Mining Itemsets from the Crowd Using Taxonomies

We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the crowd as an oracle to find out whether an itemset is frequent or not, and relies on a known taxonomy of the item domain to guide the search for frequent itemsets. In the spirit of data mining with oracles, we analyze the complexity of this problem in terms of (i) crowd complexity, that measures the number of crowd questions required to identify the frequent itemsets; and (ii) computational complexity, that measures the computational effort required to choose the questions. We provide lower and upper complexity bounds in terms of the size and structure of the input taxonomy, as well as the size of a concise description of the output itemsets. We also provide constructive algorithms that achieve the upper bounds, and consider more efficient variants for practical situations.Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing acknowledgemen

Crowd Search: Generic Crowd Sourcing Systems Using Query Optimization

We think about the query optimization issue in Generic crowdsourcing system. Generic crowdsourcing is intended to conceal the complexities and calm the client the weight of managing the group. The client is just needed to present a SQL-like question and the framework assumes the liability of arranging the inquiry, creating the execution plan and assessing in the crowdsourcing commercial center. A given query can have numerous options execution arranges and the distinction in crowdsourcing expense between the best and the most exceedingly worst arranges may be a few requests of extent. In this manner, as in social database frameworks, query optimization is imperative to crowdsourcing frameworks that give revelatory question interfaces. In this paper, we propose CROWDOP, an expense based query advancement approach for explanatory crowdsourcing frameworks. CROWDOP considers both cost and latency in query advancement destinations and produces question arranges that give a decent harmony between the cost and latency. We create proficient calculations in the CROWDOP for upgrading three sorts of inquiries: selection queries join queries, and complex selection-join queries. Deco is a far reaching framework for noting decisive questions postured over put away social information together with information got on demand from the group. In this paper we assume Deco's cost based query streamlining agent, expanding on Deco's information model, query dialect, and query execution motor exhibited befor