Business environmental scanning and collective intelligence (VASIC) as proposed by Lesca is a method to help companies tune in to their environment to anticipate opportunities or risks. This method requires collecting information, yet with the development of information technology, employees face a glut of information. To help sustain VASIC, it is necessary to develop tools to manage information overload. In this thesis, we propose a nearness measurement to estimate if two pieces of information are similar and we have created a prototype, called Alhena, based on this measurement. We demonstrate the properties of our measurement and its relevance in the context of VASIC. We also show that the prototype can be used in other fields such as literature, computer science and psychology. This work is multidisciplinary as it covers aspects of business environmental scanning (management science), research information, computer linguistics and mathematics. We focus on a concrete problem in management science to provide a tool that operationalizes computational and mathematical techniques with a goal of providing decision making support (time saving, reading assistance, ...).
