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：数据录入表单通过完整性约束来指定不同类型用户需填写的必填或可选字段。然而，部分必填字段可能不再适用于特定用户群体，却仍被错误地标记为必填项，此类字段被称为“过时必填字段”。由于过时必填字段在表单提交前通常存在非空校验，用户被迫在这些字段中输入无意义值才能完成表单提交。这些无意义值严重威胁着填写数据的质量。现有技术通常依赖人工编写规则来识别过时必填字段并松弛其完整性约束，但此类方法效率低下且成本高昂。本文提出基于学习的自动化方法LACQUER，用于松弛数据录入表单的完整性约束。LACQUER通过构建贝叶斯网络模型自动学习用户必须填写无意义值的条件。为增强学习能力，系统识别仅适用于少数用户的必填字段，并采用过采样技术SMOTE生成更多此类字段的实例，以有效挖掘字段间的依赖关系。实验结果表明，LACQUER能精准松弛数据录入表单中必填字段的完整性约束，在不同数据集上的精确率介于0.76至0.90之间。该方法可阻止20%至64%的无意义值输入，阴性预测值保持在0.72至0.91范围。此外，LACQUER具有高效性，预测单个实例的完整性约束仅需最多839毫秒。