ORCID Profile
0000-0001-7435-2445
Current Organisation
Western Sydney University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Information Systems | Pattern Recognition and Data Mining | Database Management | Interorganisational Information Systems and Web Services
Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) |
Publisher: Emerald
Date: 30-06-2023
DOI: 10.1108/ECAM-03-2022-0209
Abstract: Despite a large amount of BIM data at the handover stage, it is still difficult to identify and effectively isolate valuable construction supply chain (CSC) data that need to be reliably handed over for operation. Moreover, the role of reconciling disparate data is usually played by one party. The integration of blockchain and BIM is a plausible framework for building a reliable digital asset lifecycle. This paper proposes a BIM single source of truth (BIMSSoT) data model using blockchain for ensuring a reliable CSC data delivery. This paper utilises a blended methodology, the foundation of which is ingrained in business and management research with elements of information and communication technology (ICT) research wherever required. Knowledge elicitation case studies utilising novel interventions such as a data flow diagram (DFD), taxonomy and entity-relationship diagram (ERD) were used in this paper to develop the BIMSSoT data model. The model was validated using an expert forum, and its technological feasibility was established by developing a proof of concept. The practical contribution of this research leads to the progression of BIM towards digital engineering to go beyond object-based 3D modelling by building structured and reliable datasets, transitioning from project-centric records to a digital ecosystem of linked databases by utilizing blockchain's potential for ensuring trusted data. To the best of the author's knowledge, prior to this paper, no research had investigated a detailed data model development leveraging blockchain and BIM to integrate an immutable and complete record of CSC data as another dimension of BIM for operations.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 04-2015
Publisher: Springer Singapore
Date: 2017
Publisher: Elsevier BV
Date: 11-2023
Publisher: Association for Computing Machinery (ACM)
Date: 04-01-2018
DOI: 10.1145/3150224
Abstract: High performance computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show that hybrid environments are the natural path to get the best of the on-premise and cloud resources—steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This article brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.
Publisher: Elsevier BV
Date: 10-2011
Publisher: Elsevier BV
Date: 02-2015
Publisher: IEEE
Date: 09-2014
Publisher: Association for Computing Machinery (ACM)
Date: 13-07-2018
DOI: 10.1145/3148149
Abstract: Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.
Publisher: IEEE
Date: 06-2018
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: MDPI AG
Date: 02-03-2022
DOI: 10.3390/SU14052916
Abstract: Smallholder farmers produce over 70% of the world’s food needs. Yet, the socioeconomic conditions of the smallholder farmers are substandard. One of the primary reasons for this unpropitious situation is that they generate modest income by selling their harvest due to the lack of trusted buyers and organized markets. This research explores how technology can enable the trust to reduce transaction-related risks, empowering unknown parties to transact. Blockchain technology has the potential of mitigating transaction-related risks and promoting trust with a t er-proof history of transactions and automatic execution of smart contracts. Based on blockchain technology to promote trust, this research has discovered a novel approach for smallholder farmers to conduct exchanges by generating social capital as an in idual and using that social capital as collateral for financial exchanges when establishing contracts. This approach empowers farmers to trade smart futures contracts on behalf of the expected harvest at a better rate to receive some cash in advance to be used in the cultivation process to produce a high-quality harvest that attracts better rates. It also enables them to perform aggregated marketing with enhanced market linkages that, in turn, assist in increasing margins made by the farmer.
Publisher: Wiley
Date: 2010
DOI: 10.1002/SPE.964
Publisher: Elsevier
Date: 2016
Publisher: Elsevier BV
Date: 05-2008
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier BV
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 07-2013
Publisher: Association for Computing Machinery (ACM)
Date: 05-2014
DOI: 10.1145/2593512
Abstract: A brief review of the Internet history reveals the fact that the Internet evolved after the formation of primarily independent networks. Similarly, interconnected clouds, also called Inter-cloud , can be viewed as a natural evolution of cloud computing. Recent studies show the benefits in utilizing multiple clouds and present attempts for the realization of an Inter-cloud or federated cloud environment. However, cloud vendors have not taken into account cloud interoperability issues, and each cloud comes with its own solution and interfaces for services. This survey initially discusses all the relevant aspects motivating cloud interoperability. Furthermore, it categorizes and identifies possible cloud interoperability scenarios and architectures. The spectrum of challenges and obstacles that the Inter-cloud realization is faced with are covered, a taxonomy of them is provided, and fitting enablers that tackle each challenge are identified. All these aspects require a comprehensive review of the state of the art, including ongoing projects and studies in the area. We conclude by discussing future directions and trends toward the holistic approach in this regard.
Publisher: ACM
Date: 04-04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: IEEE
Date: 2017
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 09-2015
DOI: 10.1109/ICPP.2015.60
Publisher: IEEE
Date: 06-2015
Publisher: Wiley
Date: 18-04-2013
DOI: 10.1002/SPE.2124
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Association for Computing Machinery (ACM)
Date: 20-01-2018
DOI: 10.1145/3122981
Abstract: Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.
Publisher: Association for Computing Machinery (ACM)
Date: 14-11-2017
DOI: 10.1145/3132618
Abstract: Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 03-2013
DOI: 10.1109/AINA.2013.51
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 12-2011
Publisher: Elsevier BV
Date: 06-2012
Publisher: Elsevier
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Wiley
Date: 07-03-2017
DOI: 10.1002/CPE.4126
Publisher: Elsevier BV
Date: 07-2012
Publisher: IEEE
Date: 05-2016
Publisher: Wiley
Date: 15-05-2017
DOI: 10.1002/CPE.4169
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1631-8.CH014
Abstract: One of the key factors driving Cloud computing is flexible and on-demand resource provisioning in a pay-as-you-go manner. This resource provisioning is based on Service Level Agreements (SLAs) negotiated and signed between customers and providers. Efficient management of SLAs and Cloud resources to reduce cost, achieve high utilization, and generate profit is challenging due to the large-scale nature of Cloud environments and complex resource provisioning processes. In order to advance the adoption of this technology, it is necessary to identify and address the issues preventing proper resource and SLA management. The authors purport that monitoring is the first step towards successful management strategies. Thus, this chapter identifies the SLA management and monitoring challenges in Clouds and federated Cloud environments, and proposes a novel resource monitoring architecture as a basis for resource management in Clouds. It presents the design and implementation of this architecture and presents the evaluation of the architecture using heterogeneous application workloads.
Publisher: IEEE
Date: 2010
DOI: 10.1109/AINA.2010.32
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: CRC Press
Date: 19-12-2017
DOI: 10.1201/B18021
Publisher: IGI Global
Date: 2016
DOI: 10.4018/978-1-5225-0759-8.CH017
Abstract: The numerous advantages of cloud computing environments, including scalability, high availability, and cost effectiveness have encouraged service providers to adopt the available cloud models to offer solutions. This rise in cloud adoption, in return encourages platform providers to increase the underlying capacity of their data centers so that they can accommodate the increasing demand of new customers. Increasing the capacity and building large-scale data centers has caused a drastic growth in energy consumption of cloud environments. The energy consumption not only affects the Total Cost of Ownership but also increases the environmental footprint of data centers as CO2 emissions increases. Hence, energy and power efficiency of the data centers has become an important research area in distributed systems. In order to identify the challenges in this domain, this chapter surveys and classifies the energy efficient resource management techniques specifically focused on the PaaS cloud service models.
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/ICPP.2009.7
Publisher: IEEE
Date: 11-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2009
DOI: 10.1142/S012962640900002X
Abstract: This paper presents a new approach to resource management and scheduling in computational grids, in order to simplify the vision users have from grid resources and their management. Scheduling decisions are moved to the site where resources are hosted, allowing quick response to changes in load and resource availability. Users do not need to be aware of the resources they use and are instead supplied with "virtual resources" representing the amount of computational power available to them in the site. This approach results in new challenges, like the management of two-level scheduling schema and the need to define a site capacity measure, but simplifies and optimizes scheduling in grids. In this paper we present details of this approach – called Site Resource Scheduler (SRS) – as well as some issues regarding its simulated performance and its deployment in a site. We show that the main advantage of this approach is an overall reduction in the execution time of tasks in most scenarios.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 10-2008
Publisher: Elsevier BV
Date: 2012
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Elsevier BV
Date: 2017
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 06-2009
Publisher: Elsevier BV
Date: 05-2015
Publisher: IEEE
Date: 09-2011
DOI: 10.1109/ICPP.2011.17
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2021
Publisher: Wiley
Date: 28-01-2016
DOI: 10.1002/CPE.3767
Publisher: IEEE
Date: 12-2015
Publisher: Wiley
Date: 27-06-2017
DOI: 10.1002/SPE.2422
Publisher: Association for Computing Machinery (ACM)
Date: 12-06-2018
DOI: 10.1145/3199523
Abstract: The world is becoming a more conjunct place and the number of data sources such as social networks, online transactions, web search engines, and mobile devices is increasing even more than had been predicted. A large percentage of this growing dataset exists in the form of linked data, more generally, graphs, and of unprecedented sizes. While today's data from social networks contain hundreds of millions of nodes connected by billions of edges, inter-connected data from globally distributed sensors that forms the Internet of Things can cause this to grow exponentially larger. Although analyzing these large graphs is critical for the companies and governments that own them, big data tools designed for text and tuple analysis such as MapReduce cannot process them efficiently. So, graph distributed processing abstractions and systems are developed to design iterative graph algorithms and process large graphs with better performance and scalability. These graph frameworks propose novel methods or extend previous methods for processing graph data. In this article, we propose a taxonomy of graph processing systems and map existing systems to this classification. This captures the ersity in programming and computation models, runtime aspects of partitioning and communication, both for in-memory and distributed frameworks. Our effort helps to highlight key distinctions in architectural approaches, and identifies gaps for future research in scalable graph systems.
Publisher: IEEE
Date: 09-2011
DOI: 10.1109/HPCC.2011.44
Publisher: IEEE
Date: 12-2016
Publisher: Wiley
Date: 12-05-2017
DOI: 10.1002/CPE.3839
Publisher: IEEE
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Elsevier BV
Date: 10-2015
Publisher: Oxford University Press (OUP)
Date: 30-11-2016
Publisher: Elsevier BV
Date: 04-2016
Start Date: 04-2022
End Date: 02-2024
Amount: $538,350.00
Funder: Australian Research Council
View Funded Activity