A strategy for determining the optimal domain for knowledge based decision support systems

Inductive consistency in knowledge-based decision support systems

The automation and intelligence highlighted in Industry 4.0 put forward higher requirements for reasonable trade-offs between humans and machines for decision-making governance. However, in the context of Industry 4.0, the vision of decision support for design engineering is still unclear. Additionally, the corresponding methods and system architectures are lacking to support the realization of value-chain-centric complex engineered systems design lifecycles. Hence, we identify decision support demands for complex engineered systems designs in the Industry 4.0 era, representing the integrated design problems at various stages of the product value chain. As a response, in this paper, the architecture of a Knowledge-Based Design Guidance System (KBDGS) for cloud-based decision support (CBDS) is presented that highlights the integrated management of complexity, uncertainty, and knowledge in designing decision workflows, as well as systematic design guidance to find satisfying solutions with the iterative process “formulation-refinement-exploration-improvement” (FREI). The KBDGS facilitates diverse multi-stakeholder collaborative decisions in end-to-end cloud services. Finally, two design case studies are conducted to illustrate the proposed work and the efficacy of the developed KBDGS. The contribution of this paper is to provide design guidance to facilitate knowledge discovery, capturing, and reuse in the context of decision-centric digital design, thus improving the efficiency and effectiveness of decision-making, as well as the evolution of decision support in the field of design engineering for the age of Industry 4.0 innovation paradigm.

A tool that will allow spread sheet users and knowledge-based decision support systems (KB-DSS) professional designers to implement more advanced applications of the KB-DSS type than are possible with spreadsheet technology is described. This tool is meant to aid designers in increasing their productivity when they are required to develop the application. The KB-DSS design task is supported by an integrated development environment. Interface design and modeling are subtasks of KB-DSS design. >

The National Repository of Digital Forensics Information (NRDFI) is a knowledge repository for law enforcement digital forensics investigators (LEDFI). Over six years, the NRDFI has undertaken significant design revisions in order to more closely align the architecture of the system with theory addressing motivation to share knowledge and communication within ego-centric groups and communities of practice. These revisions have been met with minimal change in usage patterns by LEDFI community members, calling into question the applicability of relevant theory when the domain for knowledge sharing activities expands beyond the confines of an individual organization to a community of practice. When considered alongside an empirical study that demonstrated a lack of generalizability for existing theory on motivators to share knowledge, a call for deeper investigation is clear. In the current study, researchers apply grounded theory methodology through interviews with members of the LEDFI community to discover aspects of community context that appear to position communities of practice along a continuum between process focus and knowledge focus. Findings suggest that these contextual categories impact a community’s willingness to participate in various classes of knowledge support initiatives, and community positioning along these categories dictates prescription for design of knowledge based decision support systems beyond that which can be found in the current literature.

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.

This paper combines with the characteristic of knowledge-based engineering decision problem for circular economy, analyses knowledge representation, rule representation, reasoning mechanism and knowledge base structure of multi-objective attribute decision problems. It proposes a software architecture of knowledge-based engineering decision support system, which lays the foundation for the realization of the dynamic monitoring of the basic conditions of regional sustainable development and accurately reflecting on the relationship of the various elements and the evolution of the process.

Healthcare and supply chain management have recently been the two most active areas for RFID applications. The healthcare environment is a natural fit for generating and utilizing instance-level data for decision support. We consider a scenario from the French healthcare environment involving tracking and tracing of surgical equipment within and among hospitals and develop a knowledge-based system for decision support that helps improve the overall performance of the surgical instrument management process while reducing errors. We illustrate the process through the developed healthcare knowledge-based system and evaluate its performance.

Parents' and nonparents' perceptions of cries

A prototype of knowledge-based decision support system for construction cost management

Using OPTRANS object as a KB–DSS development environment for designing DSS for production management

This paper presents the strengths and weaknesses of the current bid price setting process of large telecommunication carrier operators and proposes a domain knowledge focused enhanced process supported by a knowledge based decision support system.

Decision making tools for surface mine equipment selection

GAFY: A Novel Approach for Increased Confidence in Knowledge Based Decision Support System in Online Samples

Integrated knowledge management model and system for construction projects

A hybrid inference framework is presented in which the aim is to select decision models through natural language in knowledge-based decision support system. In situations in which the rules that determine a system are uncertain and incomplete, the knowledge elicitation, maintenance and update of the rule-based system can be the problematic tasks. In such a situation, it has been found that a hybrid case-based and rule-based reasoning framework can provide a more effective and accurate means of performing such selection than other statistical methods and models. The hybrid framework results in capturing knowledge in context by storage of the case that prompted a new rule to be added, which aims at the unwanted side effects associated with typical rule reasoning system. This framework has been used to determine the inventory models in the decision support system. The results obtained from the experiment are presented with the both efficiency-improving and accuracy-improving.