摘要
Missing value imputation with crowdsourcing is a novel method in data cleaning to capture missing values that could hardly be filled with automatic approaches. However, the time cost and overhead in crowdsourcing are high. Therefore, we have to reduce cost and guarantee the accuracy of crowdsourced imputation. To achieve the optimization goal, we present COSSET+, a crowdsourced framework optimized by knowledge base. We combine the advantages of both knowledge-based filter and crowdsourcing platform to capture missing values. Since the amount of crowd values will affect the cost of COSSET+, we aim to select partial missing values to be crowdsourced. We prove that the crowd value selection problem is an NP-hard problem and develop an approximation algorithm for this problem. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed approaches.
Missing value imputation with crowdsourcing is a novel method in data cleaning to capture missing values that could hardly be filled with automatic approaches. However, the time cost and overhead in crowdsourcing are high. Therefore, we have to reduce cost and guarantee the accuracy of crowdsourced imputation. To achieve the optimization goal, we present COSSET+, a crowdsourced framework optimized by knowledge base. We combine the advantages of both knowledge-based filter and crowdsourcing platform to capture missing values. Since the amount of crowd values will affect the cost of COSSET+, we aim to select partial missing values to be crowdsourced. We prove that the crowd value selection problem is an NP-hard problem and develop an approximation algorithm for this problem. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed approaches.
