Business rules=meta-data

Perkins, Andrea

doi:10.1109/tools.2000.868979

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…Subsequently, building prototypes will help users to confirm the elicited requirements and align these with their needs (O'Donnell, Sipsma & Watt, 2012). • Elicit business rules: business rules define or constraint the aspects of a business, while they intend to control and influence its behavior (Perkins, 2000). According to the literature, business rules must be elicited to regulate the DW and the Data Mart functionalities.…”

Section: Elicitationmentioning

confidence: 99%

DSS from an RE Perspective: A systematic mapping

García

Romero

Raventós

2016

Journal of Systems and Software

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Decision support systems (DSS) provide a unified analytical view of business data to better support decision-making processes. Such systems have shown a high level of user satisfaction and return on investment. However, several surveys stress the high failure rate of DSS projects. This problem results from setting the wrong requirements by approaching DSS in the same way as operational systems, whereas a specific approach is needed. Although this is well-known, there is still a surprising gap on how to address requirements engineering (RE) for DSS. To overcome this problem, we conducted a systematic mapping study to identify and classify the literature on DSS from an RE perspective. Twenty-seven primary studies that addressed the main stages of RE were selected, mapped, and classified into 39 models, 27 techniques, and 54 items of guidance. We have also identified a gap in the literature on how to design the DSS main constructs (typically, the data warehouse and data flows) in a methodological manner from the business needs. We believe this study will help practitioners better address the RE stages of DSS projects.

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Section: Elicitationmentioning

confidence: 99%

DSS from an RE Perspective: A systematic mapping

García

Romero

Raventós

2016

Journal of Systems and Software

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“…A saying from software engineering is [29] "If you can model it, you can implement it." We have designed the class diagram, the activity diagram, the use case diagram, and the sequence diagram for modeling the static view of the medical data quality and the dynamic behavior of the medical data extraction process using UML.…”

Section: B Modeling the Medical Data Quality Using Umlmentioning

confidence: 99%

Optimizing Medical Data Quality Based on Multiagent Web Service Framework

Khoury

Shah

2012

IEEE Trans. Inform. Technol. Biomed.

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One of the most important issues in e-healthcare information systems is to optimize the medical data quality extracted from distributed and heterogeneous environments, which can extremely improve diagnostic and treatment decision making. This paper proposes a multiagent web service framework based on service-oriented architecture for the optimization of medical data quality in the e-healthcare information system. Based on the design of the multiagent web service framework, an evolutionary algorithm (EA) for the dynamic optimization of the medical data quality is proposed. The framework consists of two main components; first, an EA will be used to dynamically optimize the composition of medical processes into optimal task sequence according to specific quality attributes. Second, a multiagent framework will be proposed to discover, monitor, and report any inconstancy between the optimized task sequence and the actual medical records. To demonstrate the proposed framework, experimental results for a breast cancer case study are provided. Furthermore, to show the unique performance of our algorithm, a comparison with other works in the literature review will be presented.

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“…Quite often the differences in the business rotes can be parameterized [14]. By storing these differences in a database, the running system can either interpret these changes from a database table or the appropriate function can be called with the differing values from the database.…”

Section: Figure 13 -Refugee Tracking Systemmentioning

confidence: 99%

Architecture and design of adaptive object-models

2001

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Many object-oriented information systems share an architectural style that emphasizes flexibility and run-time adaptability. Business rules are stored externally to the program such as in a database or XML files instead of in code. The object model that the user cares about is part of the database, and the object model of the code is just an interpreter of the users' object model. We call these systems "Adaptive Object-Models", because the users' object model is interpreted at runtime and canbe changed with immediate (but controlled) effects on the system interpreting it. The real power in Adaptive Object-Models is that they have a definition of a domain model and rules for its integrity and can be configured by domain exports external to the execution of the program.This paper describes the Adaptive Object-Model architecture along with its strengths and weaknesses. It illustrates the Adaptive Object-Model architectural style by describing a framework for Medical Observations (following Fowler's Analysis Patterns) that we built.

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Business rules=meta-data

Cited by 16 publications

References 2 publications

DSS from an RE Perspective: A systematic mapping

DSS from an RE Perspective: A systematic mapping

Optimizing Medical Data Quality Based on Multiagent Web Service Framework

Architecture and design of adaptive object-models

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