deviate from the standard, indicating potentially suspicious transactions. In the context of employment, profiles can be of use for tracking employees by monitoring their online behavior, for the detection of fraud by them, and for the deployment of human resources by pooling and ranking their skills. Profiling can also be used to support people at work, and also for learning, by intervening in the design of adaptive hypermedia systems personalizing the interaction. For instance, this can be useful for supporting the management of attention . In forensic science, the possibility exists of linking different databases of cases and suspects and mining these for common patterns. This could be used for solving existing cases or for the purpose of establishing risk profiles of potential suspects . Consumer profiling Consumer profiling is a form of customer analytics, where customer data is used to make decisions on product promotion, the pricing of products, as well as personalized advertising. When the aim is to find the most profitable customer segment, consumer analytics draws on demographic data, data on consumer behavior, data on the products purchased, payment method, and surveys to establish consumer profiles. To establish predictive models on the basis of existing databases, the Knowledge Discovery in Databases (KDD) statistical method is used. KDD groups similar customer data to predict future consumer behavior. Other methods of predicting consumer behaviour are correlation and pattern recognition. Consumer profiles describe customers based on a set of attributes and typically consumers are grouped according to income, living standard, age and location. Consumer profiles may also include behavioural attributes that assess a customer's motivation in the buyer decision process. Well known examples of consumer profiles are Experian's Mosaic geodemographic classification of households, CACI's Acorn, and Acxiom's Personicx. Ambient intelligence In a built environment with ambient intelligence everyday objects have built-in sensors and embedded systems that allow objects to recognise and respond to the presence and needs of individuals. Ambient intelligence relies on automated profiling and human–computer interaction designs. Sensors monitor an individual's action and behaviours, therefore generating, collecting, analysing, processing and storing personal data. Early examples of consumer electronics with ambient intelligence include mobile apps, augmented reality and location-based service. Risks and issues Profiling technologies have raised a host of ethical, legal and other issues including privacy, equality, due process, security and liability. Numerous authors have warned against the affordances of a new technological infrastructure that could emerge on