My vision in research focuses on creating new-to-the-world artefacts that help organisations in adopting IT initiatives to tackle problems. My research outcome can be in the form of methodological approaches, frameworks, conceptual models, and software tools. From a research methodology perspective, I embrace the value of methodological pluralism and I utilize both quantitative approaches (e.g., surveys, and social network analysis) and qualitative approaches (e.g., interpretive case study, interview, and domain expert review). To continue on with my philosophy of methodological pluralism, I have subscribed to apply multi-method studies in design science research. My research outcomes which lie at the intersection of cloud computing, data analytics, IoT, and blockchain technologies have been published in leading Information Systems and Software Engineering disciplines some of them in ERA A*/A ranked venues. I have developed my experience in academic research and teaching with demonstrated ability to work both independently and as an integral part of a team.
In terms of teaching experience, I have been serving as a lecturer and tutor at University of Technology Sydney (UTS), University of New South Wales (UNSW), Western Sydney University, and Sydney-based international colleges since March 2016. I believe that teaching in IT is crucial for young students as it will build a foundation for their future career and research, i.e. the next generation of IT professionals and academia.
My short-term goals are to (i) aid aiding master/PhD students as well as junior academic staff to publish their research in leading quality conferences/journals (ii) get grants from the industry to support innovative research, and (iii) enable interdisciplinary research foundations. My long-term goal is to build an internationally recognised research and teaching profile and to be an academic role model.
Design science research, cloud computing, big data analytics, IoT/Smart city architecture, model-driven software development, situational method engineering
- Design and Innovation Fundamentals (48240)
- Database Fundamentals (31271)
- Introduction to Information Systems (31266)
- Information System Development Methodologies (31257)
© 2019 Elsevier Ltd IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens' quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed.
© 2019 Elsevier Inc. Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can also mitigate risks against system quality goals. Towards this, we propose an interactive goal reasoning approach which is supported by a probabilistic layer for the precise analysis of cloud migration risks to improve the reliability of risk control. The approach is illustrated using a commercial scenario of integrating a digital document processing system to Microsoft Azure cloud platform.
Fahmidehgholami, M & Beydoun, G 2019, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering, vol. 128, pp. 948-963.View/Download from: Publisher's site
© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected ...
Fahmideh, M & Zowghi, D 2018, 'IoT Smart City Architectures: an Analytical Evaluation', 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Annual Information Technology, Electronics and Mobile Communication Conference, IEEE, Vancuover, Canada.View/Download from: Publisher's site
while several IoT architectures have been proposed for enabling smart city visions, not much work has been done to assess and compare these architectures. By applying our proposed evaluation framework that incorporates a variety of 33 criteria, this paper presents a comparative analysis of nine existing well-known IoT architectures. The results of the analysis highlight the strengths and weaknesses of these architectures and give insight to city leaders, architects, and developers aiming at selecting the most appropriate architecture or their combination that may fit their own specific smart city development scenario.
Fahmideh, M & Lammers, T 2018, 'A study of influential factors in designing self-reconfigurable robots for green manufacturing', ACIS Website proceedings, Australasian Conference on Information Systems, ACIS, Sydney, pp. 1-7.
There is incremental growth in adopting self-reconfigurable robots in automating manufacturing conventional product lines. Using this class of robots adapting themselves with ever-changing environmental conditions has been acclaimed as a promising way of reducing energy consumption and environmental impact and thus enabling green manufacturing. Whilst the majority of existing research focuses on highlighting the efficacy of self-reconfigurable robots in energy reduction with technical driven solutions, the research on exploring the salient factors in design and development self-reconfigurable robots that directly enable or hinder green manufacturing is non-extant. This interdisciplinary research contributes to the nascent body of the knowledge by empirical investigation of design-time, run-time, and hardware aspects which should be contingently balanced when developing green-aware self-reconfigurable robots.