Energy big data: A survey <s> 2) COST CONTROL <s> Data deduplication stands as a building block for data integration and data cleaning. The state-of-the-art techniques focus on how to exploit crowdsourcing to improve the accuracy of deduplication. However, they either incur significant overheads on the crowd or offer inferior accuracy. This paper presents ACD, a new crowd-based algorithm for data deduplication. The basic idea of ACD is to adopt correlation clustering (which is a classic machine-based algorithm for data deduplication) under a crowd-based setting. We propose non-trivial techniques to reduce the time required in performing correlation clustering with the crowd, and devise methods to postprocess the results of correlation clustering for better accuracy of deduplication. With extensive experiments on the Amazon Mechanical Turk, we demonstrate that ACD outperforms the states of the art by offering a high precision of deduplication while incurring moderate crowdsourcing overheads. <s> BIB001 </s> Energy big data: A survey <s> 2) COST CONTROL <s> A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform for a large number of questions to be answered by Internet workers. Such systems have been shown to be useful to solve problems that are difficult for computers, including entity resolution, sentiment analysis, and image recognition. In this paper, we investigate the online task assignment problem: Given a pool of n questions, which of the k questions should be assigned to a worker? A poor assignment may not only waste time and money, but may also hurt the quality of a crowdsourcing application that depends on the workers' answers. We propose to consider quality measures (also known as evaluation metrics) that are relevant to an application during the task assignment process. Particularly, we explore how Accuracy and F-score, two widely-used evaluation metrics for crowdsourcing applications, can facilitate task assignment. Since these two metrics assume that the ground truth of a question is known, we study their variants that make use of the probability distributions derived from workers' answers. We further investigate online assignment strategies, which enables optimal task assignments. Since these algorithms are expensive, we propose solutions that attain high quality in linear time. We develop a system called the Quality-Aware Task Assignment System for Crowdsourcing Applications (QASCA) on top of AMT. We evaluate our approaches on five real crowdsourcing applications. We find that QASCA is efficient, and attains better result quality (of more than 8% improvement) compared with existing methods. <s> BIB002
The crowd is not free. When there are a lager number of tasks to do, the crowd-sourcing can be quite expensive. Therefore, cost control is challenging for crowd-sourced data control. In recent researches, several effective techniques have been proposed for cost control. One is pruning, which first applies computer algorithms to preprocess all the tasks and remove unnecessary tasks, guiding workers only to answer the essential tasks BIB001 . Another technique is task selection, which can improve quality and reduce cost by prioritizing all the tasks and selecting the most beneficial task to do first BIB002 .