Ryan K. L. Ko

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Purpose – In the last two decades, a proliferation of Business Process Management (BPM) modeling languages, standards and software systems have given rise to much confusion and obstacles to adoption. Because new BPM languages and notations terminologies were not well defined, duplicate features are common. This paper makes sense of the myriad BPM standards,(More)
The key barrier to widespread uptake of cloud computing is the lack of trust in clouds by potential customers. While preventive controls for security and privacy are actively researched, there is still little focus on detective controls related to cloud accountability and auditability. The complexity resulting from large-scale virtualization and data(More)
Computers play an integral part in designing, modelling, optimising and managing business processes within and across companies. While Business Process Management (BPM), Workflow Management (WfM) and Business Process Reengineering (BPR) have been IT-related disciplines with a history of about three decades, there is still a lack of publications clarifying(More)
Trust is one of the main obstacles to widespread Cloud adoption. In order to increase trust in Cloud computing, we need to increase transparency and accountability of data in the Cloud for both enterprises and end-users. However, current system tools are unable to log file accesses and transfers effectively within a Cloud environment. In this paper, we(More)
Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data.(More)
The inability to effectively track data in cloud computing environments is becoming one of the top concerns for cloud stakeholders. This inability is due to two main reasons. Firstly, the lack of data tracking tools built for clouds. Secondly, current logging mechanisms are only designed from a system-centric perspective. There is a need for data-centric(More)
Provenance, a meta-data describing the derivation history of data, is crucial for the uptake of cloud computing to enhance reliability, credibility, accountability, transparency, and confidentiality of digital objects in a cloud. In this paper, we survey current mechanisms that support provenance for cloud computing, we classify provenance according to its(More)
Data leakages out of cloud computing environments are fundamental cloud security concerns for both the end-users and the cloud service providers. A literature survey of the existing technologies revealed the inadequacies of current technologies and the need for a new methodology. This position paper discusses the requirements and proposes a novel auditing(More)
While provenance research is common in distributed systems, many proposed solutions do not address the security of systems and accountability of data stored in those systems. In this paper, we survey provenance solutions which were proposed to address the problems of system security and data accountability in distributed systems. From our survey, we derive(More)
Semantic Web efforts aim to bring the WWW to a state in which all its content can be interpreted by machines; the ultimate goal being a machine-processable Web of Knowledge. We strongly believe that adding a mechanism to extract and compute concepts from the Semantic Web will help to achieve this vision. However, there are a number of open questions that(More)