Degree-based ideas and technique can facilitate inter-disciplinary collaboration and education

Abstract

Abstract-In many application areas, there is a need for inter-disciplinary collaboration and education. However, such collaboration and education are not easy. On the example of our participation in a cyberinfrastructure project, we show that many obstacles on the path to successful collaboration and education can be overcome if we take into account that each person's knowledge of a statement is often a matter of degree -and that we can therefore use appropriate degree-based ideas and techniques. I. CYBERINFRASTRUCTURE: A BRIEF OVERVIEWBefore we start explaining the problems of inter-disciplinary communication and our proposed solution to this problem, let us first briefly describe the context of cyberinfrastructure in which our inter-disciplinary communication took place.Practical problem: need to combine geographically separate computational resources. In different knowledge domains in science and engineering, there is a large amount of data stored in different locations, and there are many software tools for processing this data, also implemented at different locations. Users may be interested in different information about this domain.Sometimes, the information required by the user is already stored in one of the databases. For example, if we want to know the geological structure of a certain region in Texas, we can get this information from the geological map stored in Austin. In this case, all we need to do to get an appropriate response from the query is to get this data from the corresponding database.In other cases, different pieces of the information requested by the user are stored at different locations. For example, if we are interested in the geological structure of the Rio Grande Region, then we need to combine data from the geological maps of Texas, New Mexico, and the Mexican state of Chihuahua. In such situations, a correct response to the user's query requires that we access these pieces of information from different databases located at different geographic locations.In many other situations, the appropriate answer to the user's request requires that we not only collect the relevant data x 1 , . . . , x n , but that we also use some data processing algorithms f (x 1 , . . . , x n ) to process this data. For example, if we are interested in the large-scale geological structure of a geographical region, we may also use the gravity measurements from the gravity databases. For that, we need special algorithms to transform the values of gravity at different locations into a map that describes how the density changes with location. The corresponding data processing programs often require a lot of computational resources; as a result, many such programs reside on computers located at supercomputer centers, i.e., on computers which are physically separated from the places where the data is stored.The need to combine computational resources (data and programs) located at different geographic locations seriously complicates research.Centralization of computational resources -traditional approach to com...