Systems integration—connecting software systems for cross-functional work—is a significant concern in many large organizations, which continue to maintain hundreds, if not thousands, of independently evolving software systems Current approaches in this space remain ad hoc, and closely tied to technology platforms Following a design science approach, and via multiple design-evaluate cycles, we develop Systems Integration Requirements Engineering Modeling Language (SIRE-ML) to address this problem SIRE-ML builds on the foundation of coordination theory, and incorporates important semantic information about the systems integration domain The article develops constructs in SIRE-ML, and a merge algorithm that allows both functional managers and integration professionals to contribute to building a systems integration solution Integration models built with SIRE-ML provide benefits such as ensuring coverage and minimizing ambiguity, and can be used to drive implementation with different platforms such as middleware, services, and distributed objects We evaluate SIRE-ML for ontological expressiveness and report findings about applicability check with an expert panel The article discusses implications for future research such as tool building and empirical evaluation, as well as implications for practice

A Modeling Language for Conceptual Design of Systems Integration Solutions

During the early phase of systems development, systems analysts often conceptualize the domain under study and represent it in one or more conceptual models. One of the most important, yet elusive ...

Conceptualizing Systems for Understanding

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Conceptualizing Systems for Understanding