Enterprise ontology was a topic of academic research in the 90s, e.g. in Canada, Holland, and Scotland. This early work was informed by industrial practice in enterprise modeling, and emphasized industrial issues like product design, requirements, organization, manufacturing, transportation, quality, inventory etc. The added value of an ontological approach compared to conventional enterprise modeling was however never clear to industrial practitioners, and ontology remained an academic exercise, which was not picked up by leading tool vendors.

Recently, the fad of the “semantic web” has brought forward an even more theoretical approach to enterprise ontology. Its proponents seem unaware of the earlier work. As before, interoperability is the core concern that ontologies are addressing, e.g. in the IDEAS framework. However, it seems that the focus has moved from interoperability of enterprises to exchange of enterprise models. This post questions if such an approach is viable. In the absence of any evidence that demonstrates that ontologies work in practice, I apologize for the theoretical nature of this post. Read the rest of this entry »

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Active knowledge modeling (AKM) is a business-centric approach to dynamically reconfigurable service oriented architectures (SOA). Services are made available to users in the situation they find themselves, as captured by enterprise models, in a business level language. The role of a knowledge architecture is to bring purpose and context to the services, and to dynamically compose and configure business solutions from basic services in a manner far more flexible than a conventional BPMS.   

We here explore the relationships between AKM and SOA, through SOA reference models, reference architectures, maturity models, and standards. Several frameworks have been developed in order to capture and explain just exactly what service oriented architectures are. This post gives an overview of different frameworks, their purpose and perspectives:   

  • Reference models developed to explain and create agreement about the meaning of key terms, and the dependencies between them,
  • Reference architectures, template solutions for a domain, outlining typical components and subsystems, aspects and layers of services,
  • Development and maturity models that describe different generations of SOA, or the path from a conventional application architecture towards a fully service oriented realization.
  • Modeling architectures, presenting overviews of modeling methods, which models should be developed and how they fit together, and how the modeling languages are structured.

Web services (WS) standards are also plentiful. People have mapped them before, but the dependencies between different standards are seldom visualized. We present a WS standards map that captures major dependencies.

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A previous post outlined some reasons why we think data modeling and semantic approaches are poorly suited for developing common data models across applications, disciplines, functions, and organizations. In particular, we argue that formal, precise representations makes it difficult to discuss terms before we have agreed upon a common language. Another problem is class hierarchies, which typically are local to a community. Enforcing a single classification structure in a common model can alienate stakeholders who have a different way of seeing things. Finally, visual models are preferred over textual representations because they more easily work as a neutral common ground, avoiding terminology wars.

This post introduces a modeling methodology that utilizes knowledge architectures to arrive at integrated information and data architectures. By following this approach, you create a conceptual knowledge model, which is suitable for interdisciplinary, cross-functional and cross-organizational communication. The methodology outlines the steps involved in creating common understanding, and some modeling principles that should be followed.

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An integrated data model can connect the applications, functions, and disciplines of a company. It can be a foundation for service oriented architectures, business process automation, and master data management. The process towards establishing an integrated model can be long and winding. While data modeling methodologies are suitable for documenting the end result of this process, they may hinder more than facilitate its progress. In particular, prematurely introducing a precise common representation, may alientate groups of stakeholders, leading to a “common” model with a bias towards a few perspectives. An approach more adjusted to group dynamics and social learning is needed. Physical data models and logical information models should be complemented by conceptual knowledge models. This post presents some of the challenges involved, while a later post outlines a knowledge architecture approach to integrating data models. Read the rest of this entry »