Mining Massive Data Sets

Abstract

The ever growing flood of data arises from many different sources: huge databases store increasing amounts of data on customers, sales, expenses, traveling, credit card usage, tax payments and the like; the Web gives us access to basically unlimited information in the form of text, images, videos and sound. Everything we do leaves an information trail which can potentially be stored and used. Most of the times, the uses are beneficial (providing us with a list of our expenses, information on our next trip or the delivery of some book we bought on the Web …), but sometimes this information can also be exploited by rogues to put us at risk (identity theft, credit card fraud, intrusion on a sensitive computer network, terrorism …). Information security breach is thus becoming an every-day threat for the citizens of modern societies and – when linked to terrorism – it is becoming more and more dangerous. However, discriminating good uses from bad ones is not easy if we want to maintain our standards of democracy and individual freedom. We therefore need to develop different – both efficient and noninvasive – security applications that can be deployed across a wide range of activities in order to deter and to detect the bad uses. The main research challenges of such security applications are: to gather and share large amounts of data or even just sample them when the volume is too big (data streams), to fuse data from different origins (such as numerical, text), to extract the relevant information in the correct context, to develop effective user interfaces from which people can obtain quick interpretations and security-alerts, and to preserve people’s privacy.