T-Crowd: Effective Crowdsourcing for Tabular Data

Caihua Shan, Nikos Mamoulis, Guoliang Li, Reynold Cheng, Zhipeng Huang, Yudian Zheng

Abstract: We study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., what is the age of Jet Li?). Existing work often treats related attributes independently, leading to suboptimal performance. We present T-Crowd: a crowdsourcing system that considers attribute relationships. T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. Our solution seamlessly supports categorical and continuous attributes. Our experiments on real datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference.