Predictive Software — EmTech 2009

At 5 in the morning, the smart phone is beeping and vibrating its lungs out.

A mail from the agency is usually not anticipated at this time unless you're

working on something important, something classified and of extremely high

value. Being a forensics and security expert, a call from the anti-terrorism

squad being run by the government isn't surprising anymore for john. After

reaching their HQ, he is briefed by a senior officer. This time it's an email

message containing what seems like codes compiled in English language and

numerals from a source that is bothering the squad. With more than a decade of

experience in data mining and analysis, John is now supposed to make sense out

of the mail containing this amalgam of words as the mail poses a potential

threat to the state.

This might sound like the storyline of the next version of Die Hard or any

other out of the world insanely big budgeted Hollywood flick. But at EmTech 2009

held in New Delhi, Srikanth S. Sampara, chief executive of Tuple Technologies

gave something similar as an example where the concept of predictive software

can come in handy. When using an appropriate model and technique, predictive

software can also help in pointing out possible fraudulent cases, such as in the

case of insurance claims, said Sampara when asked for a day to day world

implementation. But in a case like this, reliability and success rate play a

very important role as a fraudulent application being passed as genuine one for

sure would bring many insurance companies on their knees begging for bailouts.

Computer scientists are taking advantage of vast databases and sophisticated

modeling techniques to create algorithms that help in making correct predictions

of changes in financial markets, business opportunities, traffic patterns and

even international conflicts. Dr. Lipika Dey who is a senior scientist at the

Innovation Labs of Tata Consultancy Services divided data processing into two

categories, structured and unstructured both requiring different modeling

techniques and levels of complexity. Different models are being tried and tested

in order to extract actionable intelligence from enterprise data.

Dr. Lokendra Shastri who is the General Manager at SET Labs, Infosys said

that Languages can be ambiguous. Specially in the web age, where short forms and

new words are being formed each day, deriving inferences has become more

complex. He gave a few examples of simple sentences which could have more than

one meaning in different situations. Dr. Shastri has worked on areas of scalable

, parallel models of knowledge representation and inference and showed how these

play a significant role in data mining and analysis techniques.

Could a predictive software solution warn us about the ongoing financial

crisis? The answer to that was probably 'no.' Adding on to his answer, Suresh

Satyamurthy, country director of Autonomy Corporation said predictions and

enough warnings couldn't be made by thousands of Wallstreet professionals, plus

completely relying on such solutions is not the best thing to do when you have

so much at stake. Maybe in future we would have solutions so intelligent that

will help us in avoiding such situations.