Informatica (slovenia) 

Abstract: DEX is an expert system shell for qualitative multi-attribute decision modeling and support. During the last decade, it has been applied over fifty times in complex real-world decision problems. In this article we advocate for the applicability and great potential of this approach for industrial decision-making. The approach is illustrated by a typical industrial application in land use planning, and supplemented by an overview of some other completed industrial applications. The learned lessons indicate the suitability of the qualitative DEX methodology particularly for "soft", i.e., less structured and less formalized, decision problems. Practical experience also indicates the importance of methods that facilitate the analysis, simulation, and explanation of decisions.

Abstract: This paper is a contribution to the ongoing quest for modular descriptions of language processors, with the specific aim of rapidly prototyping domain-specific languages [20]. Some might argue that this problem was solved in the eighties, with the development of a proliferation of language processors based on attribute grammars [9, 13, 21]. Others might argue that functional programming languages such as ML are adequate for the purpose, without any further extensions. We believe that functional programming languages do not offer enough specialised support for implementing compilers. However, attribute grammars are not in widespread use, despite their many advantages. This may be due to restrictions imposed by attribute definition languages, which are often less flexible than general purpose functional programming languages. Such general languages tend to yield descriptions that are compact, but they lack the dedicated structuring mechanisms of attribute grammars. In this paper we initiate a systematic study of such structuring mechanisms, by giving them a compositional semantics. The semantics is expressed in the vocabulary of functional programming. Our semantics thus opens the way towards combining the powerful structuring mechanisms for attribute grammars with the flexibility of a general purpose programming language. In particular, it is easy to define new structuring operators in our semantics. Furthermore, because the semantics is a functional program, one immediately obtains a prototype for experimenting with newly defined features. Naturally the results of this paper do not stand on their own, and many of the ideas have been gleaned from the attribute grammar literature, in particular [14, 17, 18, 19, 22, 25]. Especially the thesis by Stephen Adams [1] has been an inspiration for this work.

Abstract: Bertin’s reorderable matrix (Bertin 1981, 1983, 2001) is a simple visualization method for exploring tabular data. The basic idea is to transform a multidimensional data set into a 2D interactive graphic. The graphical presentation of a data set contains rows and columns which can be permuted, allowing different views of the data set. The actual data values are replaced with symbols, say circles or rectangles, that have a size relative to the actual data value. Processing the reorderable matrix involves bringing together similar rows and columns. The reorderable matrix suits well in application areas which call for human expertise to guide the automatic process, as in architecture (Adams & Daru, 1994; Veenendaal, 1994) and system analysis and project management (Ulrich & Eppinger, 1999) where the knowledge is difficult to transform into a form “understandable” for computers. When using the reorderable matrix, the manual and automatic phases for reordering the matrix take turns and support each others. Hence, the human knowledge guides the systems but the dull parts are left to computer. There are some known implementations for automatic reordering of the reorderable matrix, most notably those published by people from the Eindhoven University of Technology (see Daru & Adams, 1989; Snijder, 1994; Veenendaal, 1994), and those used in the connection with engineering data management. Experimental implementations of the reorderable matrix are reported by Lohringer (1995), Rao & Card (1994) and Schmid & Hinterberger (1994). All these are far too inefficient to be useful in an interactive tool. The purpose of this paper is to discuss the role of the barycenter heuristic in ordering the rows and columns of the matrix. So far, the barycenter heuristic has been mainly used in graph drawing algorithms. In order to gain full advantage of the barycenter heuristic in ordering rows and columns of the reorderable matrix, we survey its use in various contexts and recall the theoretical results obtained.