Ontology-based semantic web services framework for knowledge management system

The latest Semantic Web developments and insights in knowledge management challenge the new era of semantic-based knowledge-management systems, where lies the new possibilities that Semantic Web affords for improved knowledge management. Connectivity and interoperability of knowledge management systems is the key to the vision of the future. We need a comprehensive framework that addresses main issues related to distributed Knowledge Management. Semantic Web Services (SWS) is the next major generation of the Web in which e-services and business communication become more knowledge-based. It proposes to extend the traditional Web Services technologies with its key enabling technologies of ontologies and semantics; to solve the problem of heterogeneity and interoperability of data across applications. This makes it possible to select, integrate and invocate services dynamically, which enable services to adapt themselves to changes without human intervention. The main purpose of this paper is to present the relevance of SWS technologies to KMS. Further, we discuss about the two major initiatives in SWS research. Later, we will propose ontology based semantic web services framework for KMS. Our focus is in the web service provider layer where we will introduce the three main components; knowledge manager, web service manager and ontology mapping manager

Modelling a Semantic Knowledge Management System for Collaborative Learning Environment Using a Structural Equation Modelling

Effective knowledge management system (KMS) should be able to deliver relevant knowledge to the right knowledge user at the right time. However, current KMS still largely relies on human efforts to access, extract and filter information pertinent to their knowledge need, resulted in inefficient process especially in collaborative learning environment. Effective KMS requires the identification of proper technology designed with the right system features to support the knowledge management (KM) activities to ensure that the goals of KM will be achieved. This study analyzed the proposed Semantic KMS Model for Collaborative Learning Environment using structural equation modelling (SEM) to test the effects of the model constructs in achieving the KM goals of KMS used in organizations. The model build upon comprehensive reviews of existing models in literature, and a prototype called Semantic Knowledge Management System for Collaborative Learning (SKMSCL) is developed to translate the constructs into KMS features. A post-implementation survey was conducted to assess the semantic KMS prototype in terms of the system quality, knowledge quality and the semantic KMS features identified, and how well the SKMSCL support the KM goals in comparison with the current KMS used in higher learning institutions (HLIs). Data was collected via questionnaire from a private university who participated in this study. Since there were no references can be found on the relationship between KMS knowledge quality, system quality and semantic KMS features and KM Goals, eleven research questions are derived from the model rather than hypotheses. In summary, findings indicated that seven out of eleven research questions tested are significant and supported by the findings

Knowledge management systems success model for higher education institutions: a partial least square approach

The implementation of Knowledge Management (KM) in various organizations including higher education institutions (HEIs) have provided significant benefits in making the best use of knowledge in meeting organizational strategic objectives.This study reports the findings of the factors that influence the success of knowledge management systems (KMS) in higher education institutions (HEIs).The KMS success model for HEIs was proposed and tested to 204 academicians in Malaysian public universities using partial least square approach. Out of seventeen hypotheses, fifteen hypotheses were supported. It was found that perceptions of usefulness of KMS and satisfaction levels of academicians play important roles in determining KMS success in higher education.These perceptions require the support of organizational factors such as leadership, incentive, culture of sharing, subjective norm, and training.The KMS success model developed in this study can help stakeholders in implementing successful KMS in higher education

Learning Analytics and Teaching Analytics: The Similarities and Differences

Analytics in education which constitutes of Learning Analytics and Teaching Analytics arouses great attention among researchers and practitioners in the current climate. The use of analytics in education enables educational data to be collected and analysed to serve the needs of all stakeholders to improve the educational process. The present paper gives an overview of Learning Analytics and Teaching Analytics and explores its similarities and differences, as well as the confusion that has been raised between the two defined terms. Alongside, the analytics selection flowchart presented in this paper provides a breakdown on the analytics research direction for Learning Analytics and Teaching Analytics. A deeper and varied understanding of Learning Analytics and Teaching Analytics is imperative for establishing effective and accurate analytical tools alongside with recommendations for improvement in the future

System requirement specifications for a semantic knowledge management system for collaborative learning environment

In this study, a Semantic KMS Model is formulated to support collaborative learning environment based on ontology.A comprehensive review was conducted to identify the important components of existing models in Knowledge Management (KM), KMS and semantic areas, and a survey was conducted to finalize the important components of the proposed model.As a result, the proposed semantic KMS model consisted of six important components to support collaborative works; KM Processes, Ontology-based Knowledge Model, Semantic KM Features, Knowledge Quality, System Quality and KM Goals.A prototype, called Semantic KMS for Collaborative Learning was developed to illustrate how the model components are supporting KM processes in collaborative works based on the system requirement specifications described in this paper.The required modules of the semantic KMS are described in details and the ontology-based knowledge models are also presented

Cybersecurity Vulnerabilities in Smart Grids with Solar Photovoltaic: A Threat Modelling and Risk Assessment Approach

Cybersecurity is a growing concern for smart grids, especially with the integration of solar photovoltaics (PVs). With the installation of more solar and the advancement of inverters, utilities are provided with real-time solar power generation and other information through various tools. However, these tools must be properly secured to prevent the grid from becoming more vulnerable to cyber-attacks. This study proposes a threat modeling and risk assessment approach tailored to smart grids incorporating solar PV systems. The approach involves identifying, assessing, and mitigating risks through threat modeling and risk assessment. A threat model is designed by adapting and applying general threat modeling steps to the context of smart grids with solar PV. The process involves the identification of device assets and access points within the smart grid infrastructure. Subsequently, the threats to these devices were classified utilizing the STRIDE model. To further prioritize the identified threat, the DREAD threat-risk ranking model is employed. The threat modeling stage reveals several high-risk threats to the smart grid infrastructure, including Information Disclosure, Elevation of Privilege, and Tampering. Targeted recommendations in the form of mitigation controls are formulated to secure the smart grid’s posture against these identified threats. The risk ratings provided in this study offer valuable insights into the cybersecurity risks associated with smart grids incorporating solar PV systems, while also providing practical guidance for risk mitigation. Tailored mitigation strategies are proposed to address these vulnerabilities. By taking proactive measures, energy sector stakeholders may strengthen the security of their smart grid infrastructure and protect critical operations from potential cyber threats

Preliminary study on semantic knowledge management model collaborative learning

Knowledge management (KM) is about collecting, organizing, and storing the knowledge assets of an organization to make it accessible for future knowledge reuse and application. Effective knowledge management system (KMS) should be able to deliver relevant knowledge to the right knowledge user at the right time. Yet, existing KMS is limited in several ways, and still largely relies on human efforts to access, extract and filter information pertinent to their knowledge need. Successful KMS requires the identification of proper technology designed with the right system features to support the KM activities, hence achieve the goals of KM. Due to this motivation, this paper aims to discuss the application of semantic technology to enhance the KMS and propose a semantic KM model to support collaborative learning environment. This preliminary model has been pro posed based on the review of the literatures on KM, KMS, semantic technology and collaborative learning environment and the verification of the model components will be done using a questionnaire survey. A pilot survey was conducted to several academicians in Higher Learning Institutions (HLIs)in Malaysia to validate the survey instruments before the actual survey is carried out. Rasch Unified Measurement Method (RUMM) is used to analyze the pilot data. As a result, Person reliability is found to be quit high, but Item reliability suggested fair data. A few respondents and items were identified as misfits with distorted measurements. Some problematic questions are revised and the negative questions are considered to be reworded into positive questions

Risk analysis of water grid systems using threat modeling

Critical infrastructure systems consist of physical and cyber assets that are essential to the operation of the economy and the government. As one of the most important critical infrastructures worldwide, the water sector has become vulnerable to new risks in the form of cyber threats that can severely impact public health, and are difficult to detect. A water grid system (WGS) plays an important role in guarding the business processes of the water sector against possible threats and risks. Threat modeling can be used to analyze threats to the WGS. It is applied to identify points of access to the assets and devices of the system, classify threats to them, assess the risks posed by them, and suggest mitigation measures. Each threat is classified based on its type according to the STRIDE methodology, and the results of the threat classification can be used to assess the level of risk by using the DREAD methodology. This yields a risk rating for each threat that can be used to devise mitigation measures to minimize the risk posed by it. Through the threat modeling stage, it is known that the high-risk threats on WGSs are tampering with a risk score of 14, denial of service threats with a risk score of 13, and repudiation threats with a risk score of 12. The results of the ranking are used to formulate recommendations in the form of mitigation controls against these threats

A Systematic Review of Internet of Things Adoption in Organizations: Taxonomy, Benefits, Challenges and Critical Factors

Despite the evident growth of the Internet of Things (IoT) applications, IoT deployments in organizations remain in their early stages. This paper aims to systematically review and analyze the existing literature on IoT adoption in organizations. The extant literature was identified using five electronic databases from 2015 to July 2021. Seventy-seven articles have met the eligibility criteria and were analyzed to answer the research questions. This study produced a coherent taxonomy that can serve as a framework for future research on IoT adoption in organizations. This paper presents an overview of the essential features of this emerging technology in terms of IoT adoption benefits and challenges in organizations. Existing theoretical models have been analyzed to identify the factors that influence IoT adoption and to understand the future requirements for widespread IoT adoption in organizations. Six critical factors affecting and playing a key role in IoT adoption in organizations were identified based on the critical review findings: technological, organizational, environmental, human, benefit, and value. Decision-makers and developers can prioritize these critical factors and progressively improve their development to enhance IoT adoption efficiency. This review also includes an in-depth analysis to bridge gaps and provide a comprehensive overview to further understand this research field

Investigating the User Interface Design Frameworks of Current Mobile Learning Applications: A Systematic Review

Online learning has replaced traditional face-to-face classroom instruction in the educational system. Learning via mobile, or mobile learning, is one of the solutions that most learners use since it is less expensive and easier to adopt on the go. However, in addition to hindering information transfer, issues such as small screen size and bad interface design can also make learning more cognitively demanding. This paper presents a systematic literature review on the user interface design of mobile learning applications based on the preferred reporting items for systematic reviews and meta-analyses approach. Articles selected for this study were published after the COVID-19 outbreak, between 2020 and 2022. The goal of this research was to outline the current user interface design criteria and guidelines applied when designing a mobile learning application and explore how these factors affect the learner’s cognitive load. It also aimed to identify potential research gaps and future opportunities in the creation of a UID guideline/framework for mobile learning. The findings of this study may be used as a guideline for designers, developers, educators, instructors, and others who are interested in creating a mobile learning application that provides learners with an effective knowledge and mobile learning experience