Intuition
Meanwhile, Ijen from Ncorp attempts to use a computer to perform the sort of intuitive matching that we might expect from humans.
One of the big problems that people have when working with computers is that computers are very specific and exact. If a customer searches a traditional database of an online travel agent they might look for a two-week break in Spain for between £200 and £300. If no such holiday has been entered into the database they have to try again. Had they been talking to a human travel agent, the travel agent might have said: 'I've nothing exactly like that, but I've got a fortnight in Portugal for £195; what about that?'
The difference is crucial: a human being has an intuitive feel for how close different pieces of data happen to be. For example, in human terms, a £300 holiday is an ocean away from a £700 one, whereas a £250,000 house is very close to a £249,600 house  despite the fact that the financial difference in both cases is the same, £400. People have acquired a sense of proportion which computers lack.
It is, of course, possible to program into an application a complex set of rules that define what, in this instance, is a sense of proportion. In our example, we might define 'close' as plus or minus five percent. The problem here is two-fold. First, it is difficult and time-consuming to define the rules and then program them  it is often the process of defining the rules that is the most time-consuming. Second, experience suggests that, in most business interactions, the rules are constantly altering.
This process would be much easier if the root problem was addressed. If it was possible to encapsulate such concepts of 'closeness', 'matching', and 'similarity' and develop software that could recognise when blocks of data are close then this solution could be applied in a number of different ways. This is exactly what Ijen has done, and the range of applications is staggering. For a start, it can be used to solve the holiday problem outlined above. But it can also be used, for example, to 'watch' the activity of a user and figure out what sort of holiday the user likes, which factors predominate  the price, the destination, or the dates, perhaps  and suggest alternatives that are likely to be acceptable within the ranges deduced.
It is also able to identify separate holiday events. If the same user has been looking for a week in Portugal and a fortnight in Florida, the system will not suggest 10 days in Iceland. And the same technology can be used in reverse  holiday packages can be put together and tried against the profiles of, for example, the top 1000 users, so the travel agent can predict the take-up.
But it is important to realise that this is a generic solution that can be applied in a variety of areas. The financial services industry has been an early adopter, using the software to identify patterns in the mass of structured information which it collects but which is often stored and never used because the processing has been too complex to automate. Human resources departments can also use this technology  matching people to jobs and getting the right people to do the right tasks.
