Departmental Papers (CIS)
Document Type
Conference Paper
Date of this Version
June 2002
Abstract
We present an archiving technique for hierarchical data with key structure. Our approach is based on the notion of timestamps whereby an element appearing in multiple versions of the database is stored only once along with a compact description of versions in which it appears. The basic idea of timestamping was discovered by Driscoll et al. in the context of persistent data structures where one wishes to track the sequences of changes made to a data structure. We extend this idea to develop an archiving tool for XML data that is capable of providing meaningful change descriptions and can also efficiently support a variety of basic functions concerning the evolution of data such as retrieval of any specific version from the archive and querying the temporal history of any element. This is in contrast to diff-based approaches where such operations may require undoing a large number of changes or significant reasoning with the deltas. Surprisingly, our archiving technique does not incur any significant space overhead when contrasted with other approaches. Our experimental results support this and also show that the compacted archive file interacts well with other compression techniques. Finally, another useful property of our approach is that the resulting archive is also in XML and hence can directly leverage existing XML tools.
Keywords
algorithms, documentation, theory
Date Posted: 07 May 2005
Comments
Copyright ACM, 2002. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pages 1-12.
Publisher URL: http://doi.acm.org/10.1145/564691.564693
NOTE: At the time of publication, the author Peter Buneman was affiliated with the University of Edinburgh. Currently June 2007 he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.