Johan König

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.

Availability of enterprise IT systems: an expert-based Bayesian framework

Ensuring the availability of enterprise IT systems is a challenging task. The factors that can bring systems down are numerous, and their impact on various system architectures is difficult to predict. At the same time, maintaining high availability is crucial in many applications, ranging from control systems in the electric power grid, over electronic trading systems on the stock market to specialized command and control systems for military and civilian purposes. This paper describes a Bayesian decision support model, designed to help enterprise IT system decision-makers evaluate the consequences of their decisions by analyzing various scenarios. The model is based on expert elicitation from 50 experts on IT systems availability, obtained through an electronic survey. The Bayesian model uses a leaky Noisy-OR method to weigh together the expert opinions on 16 factors affecting systems availability. Using this model, the effect of changes to a system can be estimated beforehand, providing decision support for improvement of enterprise IT systems availability. The Bayesian model thus obtained is then integrated within a standard, reliability block diagram-style, mathematical model for assessing availability on the architecture level. In this model, the IT systems play the role of building blocks. The overall assessment framework thus addresses measures to ensure high availability both on the level of individual systems and on the level of the entire enterprise architecture. Examples are presented to illustrate how the framework can be used by practitioners aiming to ensure high availability.

An architecture framework for enterprise IT service availability analysis

This paper presents an integrated enterprise architecture framework for qualitative and quantitative modeling and assessment of enterprise IT service availability. While most previous work has either focused on formal availability methods such as fault trees or qualitative methods such as maturity models, this framework offers a combination. First, a modeling and assessment framework is described. In addition to metamodel classes, relationships and attributes suitable for availability modeling, the framework also features a formal computational model written in a probabilistic version of the object constraint language. The model is based on 14 systemic factors impacting service availability and also accounts for the structural features of the service architecture. Second, the framework is empirically tested in nine enterprise information system case studies. Based on an initial availability baseline and the annual evolution of the 14 factors of the model, annual availability predictions are made and compared with the actual outcomes as reported in SLA reports and system logs. The practical usefulness of the method is discussed based on the outcomes of a workshop conducted with the participating enterprises, and some directions for future research are offered.

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.

The Distribution of Time to Recovery of Enterprise IT Services

The context of this article is the availability of enterprise IT services, a key concern for many enterprises. While there is a plethora of literature concerned with service availability, there is no previous systematic empirical study on IT service time to recovery following outages. The existing literature typically assumes a distribution, or builds on analogies to related areas such as software engineering. Therefore, our objective is to find the statistical distribution of IT service time to recovery. Method-wise, this investigation is based on logs of more than 1800 incidents in a large Nordic bank, corresponding to more than 11000 hours of recorded downtime. Five possible distributions of time to recovery from the literature were investigated using the Akaike Information Criterion to find the distribution offering the best fit. The results show that the log-normal distribution outperformed the others for all tested service channels (collections of IT services). It is concluded that the log-normal distribution offers the best fit of IT service time to recovery. Using this distribution in simulation and decision-support tools offers the prospect of better predictions of downtime and downtime costs to the practitioner community.

Probabilistic availability analysis of control and automation systems for active distribution networks

The future smart electricity grids will exhibit tight integration between control and automation systems and primary power system equipment. Optimal and safe operation of the power system will be completely dependent on well functioning information and communication (ICT) systems. Considering this, it is essential that the control and automation systems do not constitute the weak link in ensuring reliable power supply to society. At the same time, studies of reliability when considering complex interdependencies between integrated ICT systems becomes increasingly difficult to perform due to the large amount of integrated entities with varying characteristics involved. To manage this challenge there is a need for structured modeling and analysis methods that accommodate this characteristics and interdependencies. In other fields, the analysis of large interconnected systems is done using models that capture the systems and its context as well as its components and interactions. This paper addresses this issue by combining enterprise architecture methods that utilize these modeling concepts, with fault tree analysis and probabilistic relational models. This novel approach enables a holistic overview thanks to the use of formalized models. It also allows use of rigorous analysis thanks to the adaptation of the models to enable Fault Tree Analysis. The paper is concluded with an example of application of the analysis method on a proposed smart grid function in a distribution network.

Reliability Analysis of Substation Automation System Functions Using PRMs

This paper presents the application of a framework for reliability analysis of substation automation (SA) system functions. The framework is based on probabilistic relational models which combines probabilistic reasoning offered by Bayesian networks together with architecture models in form of entity relationship diagrams. In the analysis, both the physical infrastructure, and the logical structure of the system, is regarded in terms of qualitative modeling and quantitative analysis. Moreover, the framework treats the aspect of failures caused by software. An example is detailed with the framework applied to an IEC 61850-based SA system. The logical structure, including functions and their relations, is modeled in accordance with Pieces of Information for COMmunication (PICOM) defined in the IEC 61850 standard. By applying PICOMs as frame of reference when modeling functions the model instantiation becomes more standardized compared to subjectively defining functions. A quantitative reliability analysis is performed on a function for tipping a circuit breaker in case of mismatch between currents. The result is presented both in terms of a qualitative architecture model and a quantitative result showing the probability of successful operation during a period of one year.