Georgios Plataniotis

EA Anamnesis: An Approach for Decision Making Analysis in Enterprise Architecture

Enterprise Architecture (EA) modeling languages can express the business-to-IT-stack for an organization, showing how changes in the IT landscape impact business aspects and vice versa. Yet EA languages provide only the final architectural design, not the rationale behind this design. In earlier work, the authors presented the EA Anamnesis approach for EA rationalization. The authors discussed how EA Anamnesis forms a complement to current EA modeling languages, showing for example design alternatives, EA artifact selection criteria and the decision making strategy that was used. In this paper, the authors extend EA Anamnesis with a capability for organizational learning. In particular, the authors present an integration of two viewpoints presented in earlier work: (1) an ex-ante decision making viewpoint for rationalizing EA during decision making, which for example captures a decision and its anticipated consequences, and (2) an ex-post decision making viewpoint, which for example captures the unanticipated decision consequences, and possible adjustments in criteria. The authors use a fictitious, yet realistic, case study to illustrate our approach.

Relating Decisions in Enterprise Architecture Using Decision Design Graphs

Enterprise Architecture (EA) modeling languages, such as ArchiMate, describe an enterprise holistically. In doing so, they show an enterprises business products and services, and how these are realized by IT infrastructure and applications. However, EA modeling languages lack the capability to capture design rationales for decisions that lead to specific architectural designs. In our previous work we presented the EA Anamnesis approach for capturing decision details behind EA models. In doing so, we focused on capturing individual architectural decisions (in terms of alternatives, decision criteria, et cetera). In this paper we present an approach for relating architectural decisions. Using decision design graphs, we make explicit how decisions from different enterprise domains (Business, Application, Technology) relate to each other. For example, how decisions taken on a business level affect IT decisions and vice versa. Our approach is inspired by well-known mechanisms for capturing architectural rationales in software architecture. Specifically we contribute: (1) a decision relationship metamodel for enterprise architecture, with a focus on recording the impact of decisions (2) the notion of a Decision Design Graph for enterprise architecture, a visual representation of this metamodel, and (3) an illustrative example illustrating the potential usefulness of capturing decision relationships.

Formalizing Enterprise Architecture Decision Models Using Integrity Constraints

The aim of this paper is to introduce and validate a logic-based framework that serves as the underlying model for a recently introduced formalism for capturing enterprise architecture design decisions by Plataniotis et al. Our working hypothesis is that capturing of design knowledge in terms of a logic-based framework will enable consistency checks of the underlying rationales and advanced impact/what-if analysis when confronted with changes. We formalize a set of integrity constraints, which allow guidance of decision capturing during model creation and provide means to perform consistency checks. We apply our formal framework to a practical case study from the insurance sector.

An Empirical Evaluation of Design Decision Concepts in Enterprise Architecture

Enterprise Architecture (EA) languages describe the design of an enterprise holistically, typically linking products and services to supporting business processes and, in turn, business processes to their supporting IT systems. In earlier work, we introduced EA Anamnesis, which provides an approach and corresponding meta-model for rationalizing architectural designs. EA Anamnesis captures the motivations of design decisions in enterprise architecture, alternative designs, design criteria, observed impacts of a design decision, and more. We argued that EA Anamnesis nicely complements current architectural languages by providing the capability to learn from past decision making.In this paper, we provide a first empirical grounding for the practical usefulness of EA Anamnesis. Using a survey amongst 35 enterprise architecture practitioners, we test the perceived usefulness of EA Anamnesis concepts, and compare this to their current uptake in practice. Results indicate that while many EA Anamnesis concepts are perceived as useful, the current uptake in practice is limited to a few concepts - prominently ‘rationale’ and ‘layer’. Our results go on and show that architects currently rationalize architectural decisions in an ad hoc manner, forgoing structured templates such as provided by EA Anamnesis. Finally, we interpret the survey results discussing for example possible reasons for the gap between perceived usefulness and uptake of architectural rationalization.

A Conceptual Model for Compliance Checking Support of Enterprise Architecture Decisions

Enterprise architecture modeling languages capture holistically the structure of an enterprise. They therefore represent how the services and business processes of an organization are supported by IT infrastructure and applications. However, the reasoning behind the selection of specific design decisions in the architecture remains usually implicit. In our earlier work we proposed the EA Anamnesis approach which captures design rationalization information in the solution space of the enterprise architecture. Its major contribution is a formal metamodel that captures the reasoning behind design decisions and the relationships between them. In this paper, we extend our approach with concepts from the problem space domain of the enterprise architecture, such as goals, principles, requirements. Furthermore, we provide a bridging with the existing concepts of EA Anamnesis which are part of the solution space. In doing so, we can represent the extent to which EA design decisions, which define the EA design, comply with given goals, principles and requirements. The extension is evaluated with a real world case study within a Research and Technology Organization.

Capturing Design Rationales in Enterprise Architecture: A Case Study

We aim for rationalizing Enterprise Architecture, supplementing models that express EA designs with models that express the decision making behind the designs. In our previous work we introduced the EA Anamnesis approach for architectural rationalization, and illustrated it with a fictitious case study.

Traceability and modeling of requirements in enterprise architecture from a design rationale perspective

Our work aims to rationalize Enterprise Architectures (EA) by providing the reasoning behind the designs, in terms of selection criteria, design alternatives and more. Its major contribution is a formal metamodel that captures the reasoning and the inter-relationships of design decisions. This paper extends our approach in order to provide an explicit bridging between the Problem space that is defined by the different requirements and the Solution space that is described by specific design decisions. In doing so, EA Anamnesis also supports traceability from specific design decisions to the given requirements.

A computational approach for design rationalization in Enterprise Architecture

We aim for rationalizing Enterprise Architecture (EA) languages, showing not only final EA designs, but also the reasoning behind these designs (in terms of selection criteria, design alternatives, and more). Our earlier work proposes the EA Anamnesis approach for architectural rationalization. Its major contribution is a formal metamodel and a corresponding concrete syntax to interrelate business and IT decisions. Yet, up to now, EA Anamnesis lacks software tool support. As a response, this paper introduces a software tool for EA Anamnesis. In doing so, we contribute (1) a computational assessment for the EA Anamnesis metamodel and a corresponding visual syntax, showing its implementability, (2) a reflection of our aim to develop a tool by rapid prototyping, whereby practitioner feedback enables concurrent maturation of the software tool and metamodel, and (3) the idea of presenting a tool to foster acceptance and practical uptake of EA Anamnesis.

Capturing Decision Making Strategies in Enterprise Architecture – A Viewpoint

Enterprise Architecture modeling languages describe an enterprise holistically, showing its business products and services and how these are realized by IT infrastructure and applications. However, these modeling languages lack the capability to capture the design rationale for decisions that lead to specific architectural designs. In our previous work we presented the EA Anamnesis approach for capturing architectural decision details. In this paper, we extend the EA Anamnesis approach with a viewpoint that captures and rationalizes decision making strategies in enterprise architecture. Such a viewpoint is useful because it helps enterprise architects reconstruct the decision making process leading up to a decision and understand how and under which circumstances this decision was made. For example, under time pressure an architect may rely on heuristics instead of examining the decision problem in depth. More specifically, we contribute: (1) a metamodel for capturing decision making strategies, which is grounded in established decision making literature, (2) an illustrative example showcasing the potential usefulness of capturing the decision making process.

EA anamnesis: towards an approach for enterprise architecture rationalization

ArchiMate is a Domain Specific Language (DSL) to model an enterprise from a holistic perspective, showing not only the IT infrastructure of an organization, but also how this IT infrastructure supports business processes and contributes to the realization of products and (commercial) services. Yet, ArchiMate lacks the capability to capture the design decisions behind the models. Capturing such decisions is important to improve the design teaching and communication after the design process. This is what we refer to as EA Anamnesis. People who can benefit from EA Anamnesis are e.g. persons that are foreign to a given architecture, such as external Enterprise architects. In this paper, we introduce an approach to capture design decisions. For the moment we target our approach primarily on capturing design decisions in the context of ArchiMate models. Specifically, we (1) introduce a metamodel for capturing architectural design decisions. This metamodel is grounded in DSLs for capturing rationales in software engineering. (2) Finally, we provide a fictitious use case scenario for the insurance industry to illustrate the use of our approach.