In information retrieval a classical problem is that of best match search, or proximity search: either a closest matching document is to be returned in response to a query, or a ranked set of closest matching documents.

This project will identify new mathematical approaches to best match and proximity searching with particular application to very large, compressed and encrypted textual data stores. Encryption presents particular challenges in that with a good approach there should be no relationship between an encrypted document and an encrypted very similar document. Cluster based multiresolution methods will be developed to exploit the anticipated exact match, at some level of summary, between encrypted summary data for similar documents.

Project staff and support

Pedro Contreras (Postgraduate Associate, RHUL)
Fionn Murtagh (Academic supervisor, RHUL)
Julian Dean (Industrial supervisor, Thinking SAFE)
Vera Hazelwood (Technology Translator, Industrial Mathematics KTN)

This project is being carried out at RHUL, in conjunction with Thinking SAFE. It is supported by an EPSRC industrial CASE award, made available through the Knowledge Transfer Network for Industrial Mathematics. Start date: October 2006; duration: 3.5 years.