Methodik zur Vermessung von Unsicherheit in Projekten

Die Beschäftigung mit „Unsicherheit“ ist beides – gegen unsere menschliche Natur und unerlässlich für den Erfolg jedes Projekts. Die Methode „Uncertainty Mapping“ hilft, diesen scheinbar unlösbaren Widerspruch zu überwinden. Durch einen strukturierten Dialog werden die Projektbeteiligten in die Lage versetzt, zum wahren Kern von Unsicherheiten vorzudringen. Das Überraschende daran: es dauert nicht länger als das übliche Vorgehen und es gibt sogar Energie! In nur zwei Stunden können Gruppen mit bis zu 20 Personen zu wirklich tiefen Einsichten gelangen – und einander besser verstehen. Der Artikel beschreibt den konzeptionellen Hintergrund und gibt eine Schritt-für-Schritt-Anleitung zur Anwendung der Methode.

Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge

SUMMARY Thickness of cover over crystalline basement is an important consideration for mineral exploration in covered regions. It can be estimated from a variety of geophysical data types using a variety of inference methods. A robust method for combining such estimates to map the cover–basement interface over a region of interest is needed. Due to the large uncertainties involved, these need to be probabilistic maps. Predominantly, interpolation methods are used for this purpose, but these are built on simplifying assumptions about the inputs which are often inappropriate. The Bayesian estimate fusion is an alternative capable of addressing that issue by enabling more extensive use of domain knowledge about all inputs. This study is intended as a first step towards making the Bayesian estimate fusion a practical tool for cover thickness uncertainty mapping. The main contribution is to identify the types of data assumptions that are important for this problem, to demonstrate their importance using synthetic tests and to design a method that enables their use without introducing excessive tedium. We argue that interpolation methods like kriging often cannot achieve this goal and demonstrate that Markov chain Monte Carlo sampling can. This paper focuses on the development of statistical methodology and presents synthetic data tests designed to reflect realistic exploration scenarios on an abstract level. Intended application is for the early stages of exploration where some geophysical data are available while drill hole coverage is poor.

The uncertainty mapping of ontologies based on three-dimensional combination weight vector space model

Purpose This paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies. Design/methodology/approach The authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty. Findings It provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology. Research limitations/implications The future work will focus on exploring the similarity measure and combinational methods in every dimension. Originality/value This paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.