Exploring the Issues of Open Government Data Implementation in Malaysian Public Sectors

The paper presents a preliminary study of current progress and the issues of OGD implementation in Malaysia. With this objective, the authors attempt to identify initial factors that influence OGD implementation in the public sectors and discern how far the OGD initiative in Malaysia has grown since its inception. The authors make the highlight of the OGD implementation phase rather than adoption phase due to the research aim is to look at the OGD activities beyond adoption. Adoption phase is where the organization is in the state of deciding whether to adopt an innovation or not, while the implementation phase is the extent where the innovation is taking into actual use. Taking from the perspective of the central agency who is leading the OGD initiative, by using interview, observation, and desk research as the research approaches, the issues pertaining to OGD implementation is consolidated into the technology-organization-environment framework. The findings have indicated that data granularity, culture, policy, resources, skills, incentives, use and participation, and external pressure are the current issues transpired in the OGD implementation. These findings are contributing to the conceptual framework of authors’ future works in determining the factors influencing OGD post-adoption in the public sectors

Application of knowledge management in Malaysian banks – A preliminary study

Knowledge management system acquires high attention recently in all sectors.In this research,I will focus on the systems implemented in Malaysian banking industry. Different countries(developed, developing and third world countries)have different approaches towards knowledge management in banking industry. And the system’s contribution may vary in different areas. It is my intention to study about the difference of knowledge management system between Malaysia and overseas countries in this research

Deep learning and big data technologies for IoT security

Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects