Daniela Rosca

A decision making methodology in support of the business rules lifecycle

The business rules that underlie an enterprise emerge as a new category of system requirements that represent decisions about how to run the business, and which are characterized by their business-orientation and their propensity for change. We introduce a decision making methodology which addresses several aspects of the business rules lifecycle: acquisition, deployment and evolution. We describe a meta-model for representing business rules in terms of an enterprise model, and also a decision support sub-model for reasoning about and deriving the rules. A technique for automatically extracting business rules from the decision structure is described and illustrated using business rules examples inspired by the London Ambulance Service case study. A system based on the metamodel has been implemented, including the extraction algorithm.

Application of a decision support mechanism to the business rules lifecycle

Decision structures have been proposed in a number of contexts an knowledge-based software engineering as an important mechanism for recording and reasoning about the information needed to make decisions during the software lifecycle. We apply decision structures to a new domain, namely that of the business rules of an enterprise. Given the dynamic nature of business rules, decision structures are seen to be an appropriate framework to record and evolve business rules. The paper describes how to combine decision structures and business rules within a conceptual modeling framework. An architecture is presented that addresses business rules throughout the operational lifetime of the systems they govern.

Software engineering life cycle support-decision based systems development

The software engineering life cycle encompasses a broad range of activities, from the initial elicitation of the system requirements to the continuing evolution of the operational system. These activities can be best supported if there is a unifying paradigm which can integrate functional and non-functional problem-solving, process management, and knowledge acquisition and reuse. The decision based software development (DBSD) paradigm structures the software development and evolution process as a continuous problem-solving and decision making activity. In the DBSD paradigm, the software engineering team identifies and articulates software development problems, proposes alternative solutions, and develops supporting justifications from which a decision is made. This paper describes our experiences an using DBSD on five diverse projects.<<ETX>>

A product focused, layered software development framework

This paper started from the observation that there is a need among the existing software process models to adapt to the size and scope of the product being developed. Therefore, we propose here the Layered Software Development Framework, which recasts classical software development models in light of the architectural levels described in Helms Scalability Model. It describes the development of software as a set of specific development activities limited to individual architectural levels and the interactions between them, rather than as a set of general development activities distributed across all architectural levels. The framework can be tailored to the specific product being developed, and practices followed by a development organization.

Informed and Timely Business Decisions - A Data-driven Approach.

One of the main characteristics of business rules is their propensity for frequent change, due to internal or external factors to an enterprise. As these rules change, their immediate dissemination across people and systems in an enterprise becomes vital. The delay in dissemination can adversely impact the reputation of the enterprise, and cause significant loss of revenue. The current BRMS are often maintained by the IT group within a company, therefore the modifications of the BRs intended by executive management would not be instantaneous, since they have to be coded, and tested before being deployed. Moreover, the executives might not have the possibility to take the best decisions, without having the benefit of analyzing historical data, and quickly simulating what-if scenarios to visualize the effects of a set of rules on the business. Some of the systems that provide this functionality are prohibitively expensive. This paper addresses these challenges by using the power of Big Data analysis to source, clean and analyze historical data that is used for mining business rules, which can be visualized, tested on what-if scenarios, and immediately deployed without the intervention of the IT group. The proposed approach is instantiated in this paper by using open source components to mine stop loss rules for financial systems.