Meaningful Change Detection in Structured Data

Abstract

Detecting changes by comparing data snapshots is an important requirement for difference queries, active databases, and version and configuration management. In this paper we focus on detecting meaningful changes in hierarchically structured data, such as nested-object data. This is a much more challenging problem than the corresponding one for relational or flat-file data. In order to describe changes better, we base our work not just on the traditional "atomic" insert, delete, update operations, but also on operations that move an entire sub-tree of nodes, and that copy an entire sub-tree. This allows us to describe changes in a semantically more meaningful way. Since this change detection problem is N P-hard, in this paper we present a heuristic change detection algorithm that yields close to "minimal" descriptions of the changes, and that has fewer restrictions than previous algorithms. Our algorithm is based on transforming the change detection problem to a problem of computing a minimum-cost edge cover of a bipartite graph. We study the quality of the solution produced by our algorithm, as well as the running time, both analytically and experimentally.