David Höök

Architecture analysis of enterprise systems modifiability - Models, analysis, and validation

Enterprise architecture (EA) models can be used in order to increase the general understanding of enterprise systems and to perform various kinds of analysis. This paper presents instantiated architectural models based on a metamodel for enterprise systems modifiability analysis, i.e. for assessing the cost of making changes to enterprise-wide systems. The instantiated architectural models detailed are based on 21 software change projects conducted at four large Nordic companies. Probabilistic relational models (PRMs) are used for formalizing the EA analysis approach. PRMs enable the combination of regular entity-relationship modeling aspects with means to perform enterprise architecture analysis under uncertainty. The modifiability metamodel employed in the analysis is validated with survey and workshop data (in total 110 experts were surveyed) and with the data collected in the 21 software change projects. Validation indicates that the modifiability metamodel contains the appropriate set of elements. It also indicates that the metamodel produces estimates within a 75% accuracy in 87% of the time and has a mean accuracy of 88% (when considering projects of 2000 man-hours or more).

EAF2- A Framework for Categorizing Enterprise Architecture Frameworks

What constitutes an enterprise architecture framework is a contested subject. The contents of present enterprise architecture frameworks thus differ substantially. This paper aims to alleviate the confusion regarding which framework contains what by proposing a meta framework for enterprise architecture frameworks. By using this meta framework, decision makers are able to express their requirements on what their enterprise architecture framework must contain and also to evaluate whether the existing frameworks meets these requirements. An example classification of common EA frameworks illustrates the approach.

Quantifying IT Impacts on Organizational Structure and Business Value with Extended Influence Diagrams

This paper presents a framework for analysis of how IT systems add business value by causally affecting the structure of organizations. The well established theory of organizational behavior developed by Mintzberg combined with more recent research on business value of IT is used to develop a quantitative theoretical framework showing which business values are affected by IT in relation to the organizational structure. This framework, which is based upon a qualitative equivalent developed in an earlier paper, describes relationships in an Extended Influence Diagram for quantified conditional probability tables and open up for an empirical appliance. Hence obtained data can be mathematically expressed for more sound assessments. The intention is to create a fully functioning tool for analyses of what kind of IT system should be used by an organization with a given structure to maximize its business value.

Using enterprise architecture and technology adoption models to predict application usage

Application usage is an important parameter to consider in application portfolio management. This paper presents an enterprise architecture analysis framework which can be used to assess application usage. The framework, in the form of an architecture metamodel, incorporates variables from the previously published Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) model. The paper describes how the metamodel has been tailored for a specific domain, viz. industry maintenance management. The metamodel was tested in the maintenance management domain through a survey with 55 respondents at five companies. Data collected in the survey showed that the domain-specific metamodel is able to explain variations in maintenance management application usage. Integrating the TAM and TTF variables with an architecture metamodel allows architects to reuse research results smoothly, thereby aiding them in producing good application portfolio decision-support.

Modeling the IT Impact on Organizational Structure

The impact IT systems have on organizations is widely debated, both in academia and industry. This paper describes a quantitative framework for analyzing organizational impact from IT systems. The framework consists of an abstract model that is a metamodel suitable for expressing organizational structure incorporated with an extended influence diagram for analysis. The purpose is to create enterprise architecture (EA) models that can be used for analysis of the enterprise. The framework has been validated through a case study where the framework has been used to analyze the changes in organizational structure after the introduction of an IT system.

A formal method for cost and accuracy trade-off analysis in software assessment measures

Creating accurate models of information systems is an important but challenging task. It is generally well understood that such modeling encompasses general scientific issues, but the monetary aspects of the modeling of software systems are not equally well acknowledged. The present paper describes a method using Bayesian networks for optimizing modeling strategies, perceived as a trade-off between these two aspects. Using GeNIe, a graphical tool with the proper Bayesian algorithms implemented, decision support can thus be provided to the modeling process. Specifically, an informed trade-off can be made, based on the modelers prior knowledge of the predictive power of certain models, combined with his projection of their costs. It is argued that this method might enhance modeling of large and complex software systems in two principal ways: Firstly, by enforcing rigor and making hidden assumptions explicit. Secondly, by enforcing cost awareness even in the early phases of modeling. The method should be used primarily when the choice of modeling can have great economic repercussions.

A Method for Choosing Software Assessment Measures Using Bayesian Networks and Diagnosis

Creating accurate models of information systems is an important but challenging task. While the scientific aspects of such modeling are generally acknowledged, the monetary aspects of the modeling of software systems are not. The present paper describes a Bayesian method for optimizing modeling strategies, perceived as a trade-off between these two aspects. Specifically, an informed trade-off can be made, based on the modelers prior knowledge of the predictive power of certain models, combined with her projection of the costs. It is argued that this method enhances modeling of large and complex software systems in two principal ways: Firstly, by enforcing rigor and making hidden assumptions explicit. Secondly, by enforcing cost awareness even in the early phases of modeling. The method should be used primarily when the choice of modeling can have great economic repercussions.