[Editor’s Note: This week, we welcome Yefim “Jeff” Zhuk of Sallie Mae as he presents a series on Knowledge-Driven Architecture. This series follows up the author’s presentation at the recent international 2011 Semantic Technology Conference San Francisco and further expands on the subject of integrated software and knowledge engineering, originally described by Mr. Zhuk in the book “Integration-ready Architecture and Design.” Part I | Part II | Part IV]
Part III – Transitioning From “What” to “How” and explaining Conversational Semantic Decision Support (CSDS) with Use Cases
a) Formalization of Business Rules
One of the current development trends is a shift to rule-based applications. As more flexible and quickly adaptive to business changes, rule-based applications live a longer life and provide higher return on investment.
Conversational semantic decision support can be very helpful in the process of collecting and formalizing the rules . CSDS will make sure that the rules are expressed in the known terms and the rules criteria are directly tied to existing data.
b) Service integration and data mapping
The next use case is related to data mapping while integrating services.
Interaction with CSDS allows a Business Analyst to capture the results of analysis in a format readable not only by people but also understandable by a computer, ready for auto-validation. In this case, the results of analysis are automatically transformed into a decision table to drive service mediation at run-time and also become part of the “localized ontology” that provides the mapping between the conceptual ontological model to proprietary models and values of legacy systems.
c) Best practices in development
Another case is about enabling best practices. Not just teaching best practices, but in a very practical way helping their implementation.
Let’s start with requirements. Business Analysts traditionally write requirements in MS Word documents with the mix of “what” and “how”, with informational gaps and unnecessary details. Re-discovery and translation layers are expected. With Conversational Semantic Decision Support, we can go beyond drawing diagrams, like the one below, but actually enable best practices and decrease information gaps.
The simplest example of the Conversational Support is a form, like the one below.
The form will retrieve necessary data and check them against a growing ontology. The system’s feedback will include hints to help a user to provide valid entries. For example, it will not accept unknown names in the parent field (BF supports other business functions or goals), and will re-enforce a hierarchy with no gaps. At the same time, internal items can include new names and the user will be invited to define them later.
2. Realizing Efficiency & Interoperability: SOA & Semantic Technology in the Business Mission Area (BMA), U.S. DoD, Dennis E. Wisnosky, CTO, DoD (See Mr. Wisnosky’s 2011 Semantic Technology Keynote here)
3. Integration-Ready Architecture and Design, Jeff (Yefim) Zhuk, Cambridge University Press, A book on Software and Knowledge Engineering
4. Measuring IT Tactical Spending to Provide Transparency, Gartner Research
5. Rules Collector, Yefim Zhuk/Boeing, From “tribal knowledge” to rules and rule-based applications
Director of Enterprise Architecture, Yefim leads Information Architecture at Sallie Mae. In the past he consulted government agencies and corporations in SOA and knowledge engineering, shared his expertise at Java One, Wireless One and Boeing Conferences. Cambridge University Press published his book “Integration-Ready Architecture and Design”. In the book and several patents he described a new field of Integrated Software and Knowledge Engineering and Knowledge-Driven Architecture. Hobby: mountaineering and guitar.