This big data project develops tools and algorithms to support users in the task of choosing one (or a few) object(s) from a very large set, particularly when there is a great deal of complex data on which to base this choice.
Consider a traveler looking at hotel options on a travel site, a scientist trying to identify proteins to investigate further based upon the results of a high throughput experiment, or an intelligence analyst trying to identify suspected terrorists. In all of these cases we have a big data challenge in that there are likely to be hundreds, perhaps thousands or even millions, of options to choose from. While there are some criteria that can be expressed as simple functions of attribute values, e.g. price for a hotel room, these criteria capture only a part of the objective function. Other considerations, such as stylishness of a hotel, can be much harder to determine as a function of known attributes. The user may be compelled to examine candidate options individually. The computer's task is to help minimize the number of candidates examined, and to optimize the order of examination. This project examines how best to accomplish this task.
Techniques explored include supporting human specification of information need against a variety of big data sources and machine presentation of relevant results with the volume of big data. The broader impact of this project is in effectively harnessing the power of big data in a variety of applications, including business, science, and national defense.
