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Change detection in hierarchically structured information

Chawathe, Sudarshan S., Rajaraman, Anand, Garcia-Molina, Hector, Widom, Jennifer·1996·Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96

Abstract

Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a "minimum-cost edit script" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script. Our algorithms make use of some key domain characteristics to achieve substantially better performance than previous, generalpurpose algorithms. We study the performance of our algorithms both analytically and empirically, and we describe the application of our techniques to hierarchically structured documents. 1 Introduction We study the problem of detecting and representing changes to hierarchically stru...

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Metadata

Authors
Chawathe, S. S., Rajaraman, A., Garcia-Molina, H., & Widom, J.
Venue
Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96
Publisher
ACM Press
Published
1996
Pages
493-504
ISSN
0730-8078 (ISSN-L)
OpenAlex
W2024797550
Cited by
484
References
0
FWCI
7.25
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Change detection in hierarchically structured information. https://app.cochat.ai/papers/10.1145/233269.233366
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Chawathe, S. S., Rajaraman, A., Garcia-Molina, H., & Widom, J. (1996). Change detection in hierarchically structured information. Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96. https://doi.org/10.1145/233269.233366

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.