Learning-Based Relaxation of Completeness Requirements for Data Entry Forms

TL;DR

This paper introduces LACQUER, a learning-based framework for relaxing completeness requirements in data entry forms to avoid meaningless values while preserving essential data quality. It learns field dependencies with Bayesian Networks, uses SMOTE to address class imbalance, and employs an endorser mechanism to ensure reliable relaxations during form filling. Across two real-world datasets, LACQUER achieves precision and recall on key targets, reduces meaningless entries by 20–64%, and operates with prediction times suitable for interactive use, outperforming rule-based baselines. The work demonstrates practical applicability for enterprise forms and provides a path to integrate automatically learned relaxation into adaptive form design, with potential extensions to combine with value-suggestion systems like LAFF.

Abstract

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.

Figures (9)

• Figure 1: The Automated Form Filling Relaxation Problem
• Figure 2: An Example of BN and the Probability Functions of its Nodes
• Figure 3: An Example of SMOTE Interpolation
• Figure 4: Main Steps of the LACQUER Approach
• Figure 5: Example of Pre-processed Historical Input Instances