Research on key technologies of knowledge-based engineering decision support system for circular economy

Decision support systems under multiple objectives (MODSS) has been classified as a specific type of system within the broad family of DSS and has been one of the most active areas of research. However, despite its theoretical development large scale real-world applications of multiobjective decision-making (MODM) methods had been seriously lacking due to the technical knowledge and expertise needed to select and apply the most appropriate method. This paper explores the possibility of embedding intelligent guidance within MODSS, and outlines a specific guidance framework for the design of knowledge-based guidance for the selection of a suitable MODM method. This framework has been implemented as an intelligent and graphical user interface based multiple objective decision support system prototype. The prototype, with its database, methodology base of MODM and knowledge based system, will help any decision-maker in selecting the most appropriate method in solving various MODM problems.

Spatial data mining is the discovery of interesting hidden patterns and characteristics that may be implicitly in spatial databases. This paper aims to produce a descriptive model for examining the suitability in settlements by applying various machine learning techniques to figure out the knowledge discovery in spatial databases (KDSD). The study illustrates the unique hallmark that characterizes the spatial data mining by conducting the data mining algorithms. Moreover, the study presents the importance of spatial data mining and discussed multiple data sets preprocessing, classification functions, clustering and outlier detection in directions supervised learning for extracting classification rules and assessing the local amenity based on rules reliability. The classification accuracy among the three methods of the classifier algorithms (Decision Tree, Rule-Based, and Bayesian) is also compared, thereby determining the most suitable classifier by experiments performance evaluation of the training and test set.

Aims Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors.Colorectal (CRC) screening programs represent a large volume of procedures that need a follow-up endoscopy. A knowledge-based CDDS (K-CDSS) is a technology which contains clinical rules and associations of compiled data that assist with clinical decision-making tasks. Existing CDSSs are limited. To overcome this limitation we develop a K-CDSS based on variables and clinical rules introduced by medical specialist for management of patients included in CRC screening and surveillance of colorectal polyps.

Inductive consistency in knowledge-based decision support systems

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.

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. >

Construction sites are dynamic and complex; they are the most vulnerable environments to natural disasters such as hurricanes. Wind-caused damage to the construction sites could cause millions of dollar losses, considerable schedule delays, and threats to both worker safety and public safety. Therefore, there is sorely a need to enhance the overall resilience of construction projects and implement resilient construction operations. However, there is a lack of research that focuses on enhancing the resilience of construction operations during the construction phase as most existing research focuses on enhancing the structural resilience of the completed buildings or infrastructures. There is a lack of holistic and systematic knowledge that ensures whole life-cycle disaster management of construction projects. To address the gap, this paper aims to develop an ontology-based semantic model of disaster resilient construction operations to formally represent and reason about the knowledge of resilient construction operations during wind disasters. This paper starts by presenting the research method, and it follows by presenting our initial modeling and validation efforts towards a formal semantic model on disaster resilient construction operations. Finally, the paper discusses our proposed framework for a knowledge-based decision support system that allows for knowledge access, classification, and transfer on disaster resilient construction operations. The model, together with its potential application of the knowledge-based system would allow construction professionals to share and transfer domain-specific knowledge on construction disaster resilience, which will eventually contribute to enhance the whole life-cycle disaster resilience of construction projects.

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

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

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 knowledge-based decision support system to improve sow farm productivity

Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry.

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.

The emergence of information and communications technology (ICT) and rise in living standards necessitate knowledge-based decision support systems that provide services anytime and anywhere with low cost. These services assist individuals for making right decisions regarding lifestyle choices (e.g., dietary choices, stretching after workout, transportation choices), which may have a significant impact on their future health implications that may lead to medical complications and end up with a chronic disease. In other words, the knowledge-based services help individuals to make a personal and conscious decision to perform behaviour that may increase or decrease the risk of injury or disease. The main aim of this chapter is to provide personalized ubiquitous lifecare (u-lifecare) services based on users’ generated big data. We propose a platform to acquire knowledge from diverse data sources and briefly explain the potential underlying technology tools. We also present a case study to show the interaction among the platform components and personalized services to individuals.

In this paper logical techniques developed to formalize the analysis of multi-interpretable information, in particular belief set operators and selection operators, are applied to an ecological domain. A knowledge-based decision support system is described that determines the abiotic (chemical and physical) characteristics of a site on the basis of samples of plant species that are observed. The logical foundation of this system is described in terms of a belief set operator and a selection operator.