Frank, A. (2011) Formal Description of Userinterface - Demonstrated in a Comparison of iPhone and Android Smartphones.
Frank, A. (2011) Universe of Discourse - Rational GUI Design with Visible Context.
Frank, A. (2011) User Manuel for Hamster.
Frank, A. (2010) Why Context Matters in Giving Driving Instructions ―The Necessity for Representing Decision Points
Frank, A. (2009) Comment to Taxonomy of Wayfinding Tasks by Wiener, Büchner, and Hölscher
Frank, A. (2009) Scale Is Introduced by Observation Processes
Frank, A. (2007) Levels of Reality
I argue that level of detail is determined by agents constructing a plan to achieve a specific goal. Influences relevant for the plan are included, others not. This is related to the observation that human decision making is done with imperfect information and the influence of processes are included as they significantly change the outcome. Processes can be classified by the space-time frequency regions they occupy. A goal determines a space-time frequency region and only processes in this regions are relevant. This determines the level of reality related to this goal.
Frank, A. (2007) Ontologies for Imperfect Data in GIS
The importance for ontological clarification to design GIS, to structure data in a GIS or to construct usable user interface is well established; ontologies are crucial to extend interoperability from a syntactic to a semantic dimension. The discussion of ontology for GIS always pretends that the data represent reality perfectly, but real data in a GIS can give only an imperfect image of reality. An ontology for imperfect data is necessary, which is an ontology of imperfections in the representation. The analysis starts with a brief review of the ontology typically assumed for a GIS, followed by the description of the ontology of the unavoidable imperfections in the data collected. This covers aspects like partial knowledge, measurement errors, object formation, etc. (restricted to information about physical objects, e.g., data in a GIS with environmental purposes). An ontology of imperfections sheds new light on the quality of information discussion and leads to an operational definition for data quality not based on perfection. Sufficient quality of data is achieved if further improvements would not improve a decision noticeable. This leads to a differentiation of how insufficient data quality can influence a decision.
Frank, A. (2007) Data Quality Ontology: An Ontology for Imperfect Knowledge .
Abstract. Data quality and ontology are two of the dominating research topics in GIS, influencing many others. Research so far investigated them in isolation. Ontology is concerned with perfect knowledge of the world and ignores so far imperfections in our knowledge. An ontology for imperfect knowledge leads to a consistent classification of imperfections of data (i.e., data quality), and a formalizable description of the influence of data quality on decisions. If we want to deal with data quality with ontological methods, then reality and the information model stored in the GIS must be represented in the same model. This allows to use closed loops semantics to define "fitness for use" as leading to correct, executable decisions. The approach covers knowledge of physical reality as well as personal (subjective) and social constructions. It lists systematically influences leading to imperfections in data in logical succession.
Frank, A. (2007) The Importance of the Cadastre for Capital Formation: Is It the Same Everywhere? .
Frank, A. (2006) Numbers in Prelude for Haskell
Abstract: A more differentiated structure for the classes Num and related in the Haskell-98 prelude is proposed. It structures operations along the lines of algebraic structures but remains close to and compatible with the current prelude. It allows overloading of the regular arithmetic operations for instances where the corresponding axioms are valid.
An early draft for "Ontology for GIS" (September 2005)
"GIS Theory" draft (February 2006)