I’m not going to comment on the various financial aspects of the recent news about HP’s write-down of the value of its Autonomy acquisition – others are able to do this far better than me – but I would urge anyone interested to re-read the documents Oracle released earlier this year. However, I am going to write about the IDOL technology itself (I’d also recommend Tony Byrne’s excellent post).
Autonomy’s ability to market its technology has never been in doubt: aggressive and fearless, it painted IDOL as unique and magical, able to understand the meaning of data in multiple forms. However, this has never been true; computers simply don’t understand ‘meaning’ like we do. IDOL’s foundation was just a search engine using Bayesian probabilistic ranking; although most other search technologies use the vector space model there are a few other examples of this approach: Muscat, a company founded a few years before and literally across the hall from Autonomy in a Cambridge incubator, grew to a £30m business with customers including Fujitsu and the Daily Telegraph newspaper. Sadly Muscat was a casualty of the dot-com years but it is where the founders of Flax first met and worked together on a project to build a half-billion-page web search engine.
Another even less well-known example is OmniQ, eventually acquired and subsequently shelved by Sybase. Digging in the archives reveals some familiar-sounding phrases such as “automatically capture and retrieve information based on concepts”.
Originally developed at Muscat, the open source library Xapian also uses Bayesian ranking and we’ve used this successfully to build systems for the Financial Times, Newspaper Licensing Agency and Tait Electronics. Recently, Apache Lucene/Solr version 4.0 has introduced the idea of ‘pluggable’ ranking models, with one option being the Bayesian BM25. It’s important to remember though that Bayesian ranking is only one way to approach a search problem and in many cases, simply unnecessary.
It certainly isn’t magic.