Leveraging personal metadata for Desktop search: The Beagle++ system

Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this approach—the extensible Beagle++ toolbox. To prove the validity of our approach, we conducted a series of experiments. By comparing our results against the ones of a regular Desktop search solution – Beagle – we show an improved quality in search and overall performance.