The advent of the Internet has made the illegal dissemination of copyrighted material easy. An important problem is how to automatically detect when a "new" digital document is "suspiciously close" to existing ones. The SCAM project at Stanford University has addressed this problem when there is a single registered-document database. However, in practice, test documents may appear in many autonomous databases, and one would like to discover copies without having to exhaustively search in all databases. The authors' approach, dSCAM, is a distributed version of SCAM that keeps succinct metainformation about the contents of the available document databases. Given a suspicious document S, dSCAM uses its information to prune all databases that cannot contain any document that is close enough to S, and hence the search can focus on the remaining sites. They also study how to query the remaining databases so as to minimize different querying costs. They empirically study the pruning and searching schemes, using a collection of 50 databases and two sets of test documents.
['Hector Garcia-Molina', 'Luis Gravano', 'Narayanan Shivakumar']
dSCAM: finding document copies across multiple databases