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- Aier, Stephan, et al.
(författare)
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A Survival Analysis of Application Life Spans based on Enterprise Architecture Models
- 2009
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Ingår i: Proc. 3rd International Workshop on Enterprise Modelling and Information Systems Architectures, EMISA 2009. ; , s. 141-154
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Konferensbidrag (refereegranskat)abstract
- Modern enterprises face the challenge to survive in an ever changing environment. One commonly accepted means to address this challenge and further enhance survivability is enterprise architecture (EA) management, which provides a holistic model-based approach to business/IT alignment. Thereby, the decisions taken in the context of EA management are based on accurate documentation of IT systems and business processes. The maintenance of such documentation causes high investments for enter-prises, especially in the absence of information on the change rates of different systems and processes. In this paper we propose a method for gathering and analyzing such in-formation. The method is used to analyze the life spans of the application portfolio of three companies from different industry sectors. Based on the results of the three case studies implications and limitations of the method are discussed.
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- Buckl, S., et al.
(författare)
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A pattern-based approach to quantitative enterprise architecture analysis
- 2009
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Ingår i: 15th Americas Conference on Information Systems 2009, AMCIS 2009. - 9781615675814 ; , s. 2314-2324
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Konferensbidrag (refereegranskat)abstract
- Enterprise Architecture (EA) management involves tasks that substantially contribute to the operations of an enterprise, and to its sustainable market presence. One important aspect of this is the availability of services to customers. However, the increasing interconnectedness of systems with other systems and with business processes makes it difficult to get a clear view on change impacts and dependency structures. While management level decision makers need this information to make sound decisions, EA models often do not include quality attributes (such as availability), and very rarely provide quantitative means to assess them. We address these shortcomings by augmenting an information model for EA modeling with concepts from Probabilistic Relational Models, thus enabling quantitative analysis. A sample business case is evaluated as an example of the technique, showing how decision makers can benefit from information on availability impacts on enterprise business services.
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