Backup technologists are joining together with academic funding to improve data searching security and functionality.
Backup vendor, Thinking SAFE is to undertake a collaborative research initiative with Royal Holloway University of London (RHUL) and The Smith Institute.
The project is supported by an Engineering and Physical Sciences Research Council (EPSRC) industrial CASE Award made available through the Knowledge Transfer Network for Industrial Mathematics and will take over three years to complete.
The group said that information retrieval faces a classic problem of best match search or proximity search. Either a closest matching document is returned in response to a query, or a ranked set of closest matching documents. The aim of the project is to develop novel technology for secure searching of huge quantities of compressed and encrypted storage that is distributed across multiple locations.
The initial project will focus on searching corporate data stores in excess of 100 terabytes containing in excess of 10 million documents, using both free-text and controlled vocabulary searches.
Professor Fionn Murtagh, RHUL Computer Science Department head and Julian Dean, Thinking SAFE technical director will supervise the project.
Dean said the aim was to push the boundaries of data storage and retrieval to provide companies with greater backup functionality and performance.
RHUL’s Murtagh said: “Mining of massive data sets from business, engineering and science is always exciting. The information search and recovery work of Thinking SAFE is additionally challenging because it is linked to information security on a range of levels. The innovative research in this collaborative project will have immediate application to significant parts of the "Data Grid" that underpins much of corporate activity.”
The Knowledge Transfer Network for Industrial Mathematics is managed by The Smith Institute, which helps companies to gain a competitive advantage in their products, processes and operations through the application of mathematical modelling and analysis.