Per Närman

Enterprise architecture analysis with extended influence diagrams

The discipline of enterprise architecture advocates the use of models to support decision-making on enterprise-wide information system issues. In order to provide such support, enterprise architecture models should be amenable to analyses of various properties, as e.g. the level of enterprise information security. This paper proposes the use of a formal language to support such analysis. Such a language needs to be able to represent causal relations between, and definitions of, various concepts as well as uncertainty with respect to both concepts and relations. To support decision making properly, the language must also allow the representation of goals and decision alternatives. This paper evaluates a number of languages with respect to these requirements, and selects influence diagrams for further consideration. The influence diagrams are then extended to fully satisfy the requirements. The syntax and semantics of the extended influence diagrams are detailed in the paper, and their use is demonstrated in an example.

Enterprise Architecture: A Framework Supporting System Quality Analysis

Enterprise Architecture is a model-based approach to business-oriented IT management. To promote good IT decision making, an enterprise architecture framework needs to explicate what kind of analyses it supports. Since creating enterprise architecture models is expensive and without intrinsic value, it is desirable to only create enterprise architecture models based on metamodels that support well-defined analyses. This paper suggests a metamodel derived specifically with a set of theory-based system quality analyses in mind. The ISO 9126-based theory behind the system quality analysis is introduced in the shape of an extended influence diagram. Finally, an example illustrates that our theory-based metamodel does support system quality analysis.

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.

Enterprise architecture availability analysis using fault trees and stakeholder interviews

The availability of enterprise information systems is a key concern for many organisations. This article describes a method for availability analysis based on Fault Tree Analysis and constructs from the ArchiMate enterprise architecture (EA) language. To test the quality of the method, several case-studies within the banking and electrical utility industries were performed. Input data were collected through stakeholder interviews. The results from the case studies were compared with availability of log data to determine the accuracy of the methods predictions. In the five cases where accurate log data were available, the yearly downtime estimates were within eight hours from the actual downtimes. The cost of performing the analysis was low; no case study required more than 20 man-hours of work, making the method ideal for practitioners with an interest in obtaining rapid availability estimates of their enterprise information systems.

Data accuracy assessment using enterprise architecture

Errors in business processes result in poor data accuracy. This article proposes an architecture analysis method which utilises ArchiMate and the Probabilistic Relational Model formalism to model and analyse data accuracy. Since the resources available for architecture analysis are usually quite scarce, the method advocates interviews as the primary data collection technique. A case study demonstrates that the method yields correct data accuracy estimates and is more resource-efficient than a competing sampling-based data accuracy estimation method.

Extended Influence Diagram Generation

One step towards achieving high interoperability is to get an understanding of the current degree of interoperability, which calls for interoperability analyses. Extended influence diagrams have been proposed as an approach for conducting such interoperability analyses [1]. These extended influence diagrams need to have a high degree of construct validity in the sense that they actually measure the variable under analysis. A convenient way of ascertaining a sufficient degree of construct validity is to base the analyses on scientific theory. Such theory is readily available in the plethora of scientific papers and articles stored in online databases. Consequently, a method for generating extended influence diagrams should be able to elicit knowledge from text-based sources. This paper proposes a method for knowledge elicitation from scientific texts, generating extended influence diagrams. This paper unfolds as follows: The extended influence diagram notation is presented in section 2. Next, the method for deriving extended influence diagrams with its process steps and rules is presented in section 3. Finally, section 4 presents the conclusions and discussion.

An enterprise architecture framework for multi-attribute information systems analysis

Enterprise architecture is a model-based IT and business management discipline. Enterprise architecture analysis concerns using enterprise architecture models for analysis of selected properties to provide decision support. This paper presents a framework based on the ArchiMate metamodel for the assessment of four properties, viz., application usage, system availability, service response time and data accuracy. The framework integrates four existing metamodels into one and implements these in a tool for enterprise architecture analysis. The paper presents the overall metamodel and four viewpoints, one for each property. The underlying theory and formalization of the four viewpoints is presented. In addition to the tool implementation, a running example as well as guidelines for usage makes the viewpoints easily applicable.

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.

Hybrid Probabilistic Relational Models for System Quality Analysis

The formalism Probabilistic Relational Models (PRM) couples discrete Bayesian Networks with a modeling formalism similar to UML class diagrams and has been used for architecture analysis. PRMs are well-suited to perform architecture analysis with respect to system qualities since they support both modeling and analysis within the same formalism. A particular strength of PRMs is the ability to perform meaningful analysis of domains where there is a high level of uncertainty, as is often the case when performing system quality analysis. However, the use of discrete Bayesian networks in PRMs complicates the analysis of continuous phenomena. The main contribution of this paper is the Hybrid Probabilistic Relational Models (HPRM) formalism which extends PRMs to enable continuous analysis thus extending the applicability for architecture analysis and especially for trade-off analysis of system qualities. HPRMs use hybrid Bayesian networks which allow combinations of discrete and continuous variables. In addition to presenting the HPRM formalism, the paper contains an example which details the use of HPRMs for architecture trade-off analysis.

Enterprise Architecture Analysis for Data Accuracy Assessments

Poor data in information systems impede the quality of decision-making in many modern organizations. Manual business process activities and application services are never executed flawlessly which results in steadily deteriorating data accuracy, the further away from the source the data gets, the poorer its accuracy becomes. This paper proposes an architecture analysis method based on Bayesian Networks to assess data accuracy deterioration in a quantitative manner. The method is model-based and uses the ArchiMate language to model business processes and the way in which data objects are transformed by various operations. A case study at a Swedish utility demonstrates the approach.