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Using Building Data Models to Represent Workflows and a Contextual Dimension

The context-workflow relationship is often poorly defined or forgotten entirely. In workflow
systems and applications context is either omitted, defined by the workflow or defined
based on a single aspect of a contextual dimension. In complex environments this can
be problematic as the definition of context is useful in determining the set of possible
workflows. Context provides the envelope that surrounds the workflow and determines
what is or is not possible.
The relationship between workflow and context is also poorly defined. That context can
exist independently of workflow is often ignored, and workflow does not exist independently
of context. Workflow representations void of context violate this stipulation. In order for
a workflow representation to exist in a contextual dimension it must possess the same
dimensions as the context.
In this thesis we selected one contextual dimension to study, in this case the spatial
dimension, and developed a comprehensive definition using building data models. Building
data models are an advanced form of representation that build geometric data models into
an ob ject-oriented representation consisting of common building elements. The building
data model used was the Industry Foundation Classes (IFC) as it is the leading standard
in this emerging field.
IFC was created for the construction of facilities and not the use of facilities at a
later time. In order to incorporate workflows into IFC models, a zoning technique was
developed in order to represent the workflow in IFC. The zoning concept was derived from
multi-criteria layout for facilities layout and was adapted for IFC and workflow.
Based on the above work a zoning extension was created to explore the combination of
IFC, workflow and simulation. The extension is a proof of concept and is not intended to
represent a robust formalized system. The results indicate that the use of a comprehensive
definition of a contextual dimension may prove valuable to future expert systems.

Identiferoai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4649
Date January 2009
CreatorsHenriques, David
Source SetsUniversity of Waterloo Electronic Theses Repository
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation

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