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Detecting changes by comparing data snapshots is an important requirement for di erence queries, active databases, and version and con guration 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 at-le 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 a n e n tire sub-tree of nodes, and that copy a n e n tire sub-tree. This allows us to describe changes in a semantically more meaningful way. Since this change detection problem is NP-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.

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 problem is much more challenging 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. These operations allows us to describe changes in a semantically more meaningful way. Since this change detection problem is AfP-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 stud y the quality of the solution produced by our algorithm, as well as the running time, both analytically and experimentally.

Semistructured data may be irregular and incomplete and does not necessarily conform to a xed schema. As with structured data, it is often desirable to maintain a history of changes to data, and to query over both the data and the changes. Representing and querying changes in semistructured data is more di cult than in structured data due to the irregularity and lack of schema. We p resent a model for representing changes in semistructured data and a language for querying over these changes. An important feature of our approach is that we represent and query changes directly as annotations on the a ected data, instead of indirectly as the di erence between database states. We describe the implementation of our model and query language. We p resent extensions that permit convenient snapshot-based access in our change-based data model. We also describe our design and implementation of a query subscription service that permits users to subscribe to changes in semistructured information sources.