Structuring business rules interactions

Kumar, Veerendra; Anantaram, C.

doi:10.1016/j.elerap.2003.09.007

Cited by 15 publications

(3 citation statements)

References 8 publications

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“…Business rules capture requirements [5] including the decisions, guidelines and controls which make up the functionality [6]. As long as inconsistency and ambiguity are identified, either in rules or by rules, they are proved to be useful for capturing and resolving conflicts, both at the requirements level [7] and at the design level [8].…”

Section: Background and Introductionmentioning

confidence: 99%

Adaptation and Maintenance of Multi-agent Systems using Reaction Rules

Xiao¹

2012

Proceedings of the 2nd International Conference on Computer Application and System Modeling

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Software systems must be adaptable and maintainable in a business environment to remain useful. Business rules are central to business requirements and their adaptability and maintainability is the key to keep systems up to the changing environment over time. In this paper, we propose reaction rules for modelling interactive agent behaviour in Multi-Agent Systems. A rule-based knowledge model is sourced from the business requirements specification and becomes a knowledge base of driving agent behaviour dynamically, at runtime. Because the knowledge model is externalised from the running system itself, system behaviour can be adapted and maintained in an easier manner. The work is illustrated using the case of a rail track management system.

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Section: Background and Introductionmentioning

confidence: 99%

Adaptation and Maintenance of Multi-agent Systems using Reaction Rules

Xiao¹

2012

Proceedings of the 2nd International Conference on Computer Application and System Modeling

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“…Storage of business rule executions includes information about when they were triggered and how they affect the data stored (Bajec and Krisper 2005). Storage of business rules and associated executions with data will provide capabilities for the use of OLAP systems to study interactions among business rules (Rai and Anantaram 2004), and also the interactions between business rules and associated data. Such study can be used in business intelligence, which aims to provide information to support evidence based decision making.…”

Section: Modeling Of Facts With Business Operational Contextmentioning

confidence: 99%

Design of a Unified Data with Business Rules Storage Model for OLTP and OLAP Systems

Opoku-Anokye

Tang

2014

CIT

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This paper reviews the literature concerning the practice of using Online Analytical Processing (OLAP) systems to recall information stored by Online Transactional Processing (OLTP) systems. Such a review provides a basis for discussion on the need for the information that are recalled through OLAP systems to maintain the contexts of transactions with the data captured by the respective OLTP system. The paper observes an industry trend involving the use of OLTP systems to process information into data, which are then stored in databases without the business rules that were used to process information and data stored in OLTP databases without associated business rules. This includes the necessitation of a practice, whereby sets of business rules are used to extract, cleanse, transform and load data from disparate OLTP systems into OLAP databases to support the requirements for complex reporting and analytics. These sets of business rules are usually not the same as business rules used to capture data in particular OLTP systems. The paper argues that differences between the business rules used to interpret these same data sets risk gaps in semantics between information captured by OLTP systems and information recalled through OLAP systems. The literature concerning the modeling of business transaction information as facts with context as part of the modelling of information systems was reviewed to identify design trends that are contributing to the design quality of OLTP and OLAP systems. The paper then argues that the quality of OLTP and OLAP systems design has a critical dependency on the capture of facts with associated context, encoding facts with contexts into data with business rules, storage and sourcing of data with business rules, decoding data with business rules into the facts with the context and recall of facts with associated contexts. The paper proposes UBIRQ, a design model to aid the co-design of data with business rules storage for OLTP and OLAP purposes. The proposed design model provides an opportunity for the implementation and use of multi-purpose databases and business rules stores for OLTP and OLAP systems. Such implementations would enable the use of OLTP systems to record and store data with executions of business rules, which will allow for the use of OLTP and OLAP systems to query data with business rules used to capture the data, thereby ensuring that information recalled via OLAP systems preserves the contexts of transactions as per the data captured by the respective OLTP system.

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“…The justification for using business rules is that they provide a means for implementing business policy [28]. Business policies, naturally, change over time and thus -<concept> <name> fault </name> -<properties> <property> type </property> <property> location </property> <property> immediate_impact </property> <property> priority </property> <property> description </property> <property> cleared </property>  <properties> </concept> …”

Section: Policy Rulementioning

confidence: 99%

Adaptive Agent Model: Software Adaptivity using an Agent-oriented Model-Driven Architecture

Xiao

Greer

2009

Information and Software Technology

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Model-Driven Architecture (MDA) promotes the development of software systems through successive building and generation of models, improving the reusability of models. Applying the same principles to the area of Agent-Oriented Software Engineering (AOSE) advances the ideas behind MDA even more significantly, due to the inherent adaptivity of software agents We describe an appropriate set of models originating from requirements specification and transformable to models understandable and executable by agents thus demonstrating an Agent-oriented Model-Driven Architecture (AMDA) approach. In AMDA, agents use hierarchical business knowledge models with business process rules at the top, business rules to control policy and logic in the middle and a base layer defining business concepts. Being externalised, knowledge is easily configurable by human beings and applied by software agents. A real case study is used to illustrate the process. The main advances over the object-oriented MDA are (i) the addition of component dynamics (ii) the use of agent-executable rule-based business models and (iii) a proposed higher level of abstraction with the direct representation of business requirements.

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Structuring business rules interactions

Cited by 15 publications

References 8 publications

Adaptation and Maintenance of Multi-agent Systems using Reaction Rules

Adaptation and Maintenance of Multi-agent Systems using Reaction Rules

Design of a Unified Data with Business Rules Storage Model for OLTP and OLAP Systems

Adaptive Agent Model: Software Adaptivity using an Agent-oriented Model-Driven Architecture

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