When you have a huge amount of data, it can be very hard to use in decisions making. How do you separate out the signal from the noise? You need data that is ‘representative, characteristic and compact’. Hong and his colleague help to provide parameters to produce detailed and representative data. For example, in disease diagnosis, a clinician needs to have the most relevant information. By analysing the most representative data, conclusions are derived more quickly.
Many real-life problems can be formulated using a mathematical method called ‘boolean satisfiability’ named after the mathematician George Boole. Choosing a good solution for users is hard and time consuming. Hong and his colleagues have proposed a method to use local preferences. After acquiring all the local preferences, they can provide the optimal solutions for the users. For example; a hotel that user will like the most.