Tracing and Fixing Inconsistencies in Clone-and-Own Tabular Data Models

Many data-intensive applications handle tabular data with more advanced structuring and processes than spreadsheets, enabling end-users to copy and adapt tabular data and processes to create new templates or datasets anytime. Recent research advances demonstrated that, in such clone-and-own scenarios, actions performed on the data structure, together with cloning and adaptation actions, can be captured within an operation-based model to prevent the drift of the internal tabular data model. However, this approach is limited by the assumption that each operation must maintain consistency regarding dependencies generated by the domain-specific languages that connect the observed and computed data.