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

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt

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

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research

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. © 2019 Insight Society

Measuring transaction performance based on storage approaches of Native XML database

Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using three main storage technologies – text-based, model-based, and schema-based techniques; and Hybrid Database systems which are comprised of both XML-Enabled and Native XML database systems. NXDs are faster than other database technologies because there is no need to convert the format of the data prior to storage. No performance evaluation has been carried out to compare all three storage strategies, hence, this paper reports on the first attempt to evaluate all three storage strategies by using open source products to measure the response time taken for each of the database basic tasks such as database creation, dataset insertion, and data manipulation. The results of the evaluation show that the schema-based storage strategy: performs 3.5 times faster than the other two storage techniques in data insertion; shows very good performance in query processing on small and large datasets; performs 10.33 times faster than text-based, and 7.5 times faster than model-based storage techniques in query processing of large datasets