We apply mathematical concept analysis to the problem of reengineering configurations. Concept analysis will reconstruct a taxonomy of concepts from a relation between objects and attributes. We use concept analysis to infer configuration structures from existing source code. Our tool NORA/RECS will accept source code, where configuration-specific code pieces are controlled by the preprocessor. The algorithm will compute a so-called concept lattice, which —when visually displayed — offers remarkable insight into the structure and properties of possible configurations. The lattice not only displays tine-grained dependencies between configurations, but also visualizes the overall quality of configuration structures according to software engineering principles. In a second step, interferences between configurations can be analyzed in order to restructure or simplify configurations. Interferences showing up in the lattice indicate high coupling and low cohesion between configuration concepts. Source files can then be simplified according to the lattice structure. Finally, we show how governing expressions can be simplified by utilizing an isomorphism theorem of mathematical concept analysis.

this paper we present the technique of local scaling, where only concepts of the outer scale, which are differentiated further by the inner scale, are refined. This is also called dynamic scaling, since only at run-time the result of the request to the underlying database determines where to scale and where not. 2 Conceptual Data Systems

Concept exploration is a knowledge acquisition tool for interactively exploring the hierarchical structure of finitely generated lattices. Applications comprise the support of knowledge engineers by constructing a type lattice for conceptual graphs, and the exploration of large formal contexts in formal concept analysis.

. Concept lattices are used in formal concept analysis to represent data conceptually so that the original data are still recognizable. Their line diagrams should reflect the semantical relationships within the data. Up to now, no satisfactory automatic drawing programs for this task exist. The geometrical heuristic is the most successful tool for drawing concept lattices manually. It uses a geometric representation as intermediate step between the list of upper covers and the line diagram of the lattice. 1 Introduction Concept lattices have become a useful tool in data analysis and knowledge processing (cf. [7], [10], [6]). They allow to represent data conceptually so that the original data are still recognizable. This supports reliable data interpretations and opens possibilities of exploring data and retrieving information. Concept lattices can be graphically represented by labelled line diagrams which have been proved as useful communication tools in many applications (cf. [9]). A...

Conceptual Information Systems unfold the conceptual structure of data stored in relational databases. In the design phase of the system, conceptual hierarchies have to be created which describe different aspects of the data. In this paper, we describe two principal ways of designing such conceptual hierarchies, data driven design and theory driven design, and discuss advantages and drawbacks. The central part of the paper shows how Attribute Exploration, a knowledge acquisition tool developed by B. Ganter can be applied for narrowing the gap between both approaches.

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The free distributive completion of a partial complete lattice is the complete lattice that it is freely generated by the partial complete lattice `in the most distributive way'. This can be described as being a universal solution in the sense of universal algebra. Free distributive completions generalize the constructions of tensor products and of free completely distributive complete lattices over partially ordered sets.