Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have not-null validation checks before submitting the form, users have to enter meaningless values in such fields in order to complete the form submission. These meaningless values threaten the quality of the filled data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this paper, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users, and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance.

翻译：数据输入表单利用完整性要求来指定某些字段为必填或选填，以便从不同类型用户处收集必要信息。然而，部分必填字段可能不再适用于某些用户类型，但仍被错误标记为必填，我们将这类字段称为"过时必填字段"。由于过时必填字段在提交表单前通常设有非空校验，用户不得不输入无意义值才能完成提交，这些无意义值严重威胁数据质量。现有技术通常依赖人工编写规则识别过时必填字段并松弛其完整性要求，但存在效率低、成本高的问题。本文提出LACQUER——一种基于学习的自动化表单完整性要求松弛方法。该方法通过构建贝叶斯网络模型，自动学习用户被迫填写无意义值的条件。为提升学习能力，LACQUER识别仅适用于小规模用户群的必填字段，采用SMOTE过采样技术为该类字段生成更多实例，从而有效挖掘其依赖关系。实验结果表明：在不同数据集上，LACQUER能以0.76至0.90的精确度精准松弛表单必填字段的完整性要求；可阻止用户填写20%至64%的无意义值，负预测值介于0.72至0.91之间；且具有高效性，预测单条实例的完整性要求耗时不超过839毫秒。