We can help you apply Complex Event Processing and a variety of Statistical Analysis techniques to your data to help you improve your business.

The classification and detection of abnormalities is a branch of data-mining often referred to as rare-event or anomaly detection. We have many years experience in this field and have developed effective techniques to handle this type of hard search Finding a needle in a haystack
The colloquial example of a hard search problem is 'finding a needle in a haystack', however it is even harder to find a particular grass type in a haystack. The needle is very different and can be easily identified. Suppose, however, that the hay contains hundreds of different species of grass, and that the task is to identify a particular rare species of grass that is subtly different from all the others. To make things even more difficult you have previously only ever seen a few thousand examples of the special grass and this is all that classifications can be based on.

This contrived example illustrates a type of problem that is common. A typical real-life example is the card fraud detection problem; here the data consists of millions of transactions which have a multiplicity of features such that no two are exactly the same. Most are perfectly legal. Only a tiny percentage are known to be fraudulent and there is almost as much variation amongst these as there is in the legal transactions.

Data like this is so sparse and lacking in consistency, it seems an impossible task to spot future fraud using a system based on the sample data as the basis of each decision. However, while there simply is not enough information to go on to make black and white decisions, we can perform powerful screening, in real time, to flag which transactions should be investigated further by human experts, who would otherwise be overwhelmed by the sheer quantity of data. The name of the game is to flag likely anomalies/fraud without ‘crying wolf’ too often.

problem.