ANALYZING THE SYSTEM FEATURES, USABILITY, AND PERFORMANCE OF A CONTAINERIZED APPLICATION ON CLOUD COMPUTING SYSTEMS

This study analyzed the system features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted on a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory & CPU capacity, along with performance metrics such as container latency, Distance Matrix API response time, and CPU utilization for each service. Easy-to-use usability was also evaluated by assessing the quality of documentation, a learning curve for be- ginner users, and a scale-to-zero factor. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability when com- pared to AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud ser- vice for similar containerized web applications

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Adaptive Big Data Pipeline

Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms

Cloud-computing strategies for sustainable ICT utilization : a decision-making framework for non-expert Smart Building managers

Virtualization of processing power, storage, and networking applications via cloud-computing allows Smart Buildings to operate heavy demand computing resources off-premises. While this approach reduces in-house costs and energy use, recent case-studies have highlighted complexities in decision-making processes associated with implementing the concept of cloud-computing. This complexity is due to the rapid evolution of these technologies without standardization of approach by those organizations offering cloud-computing provision as a commercial concern. This study defines the term Smart Building as an ICT environment where a degree of system integration is accomplished. Non-expert managers are highlighted as key users of the outcomes from this project given the diverse nature of Smart Buildings’ operational objectives. This research evaluates different ICT management methods to effectively support decisions made by non-expert clients to deploy different models of cloud-computing services in their Smart Buildings ICT environments. The objective of this study is to reduce the need for costly 3rd party ICT consultancy providers, so non-experts can focus more on their Smart Buildings’ core competencies rather than the complex, expensive, and energy consuming processes of ICT management. The gap identified by this research represents vulnerability for non-expert managers to make effective decisions regarding cloud-computing cost estimation, deployment assessment, associated power consumption, and management flexibility in their Smart Buildings ICT environments. The project analyses cloud-computing decision-making concepts with reference to different Smart Building ICT attributes. In particular, it focuses on a structured programme of data collection which is achieved through semi-structured interviews, cost simulations and risk-analysis surveys. The main output is a theoretical management framework for non-expert decision-makers across variously-operated Smart Buildings. Furthermore, a decision-support tool is designed to enable non-expert managers to identify the extent of virtualization potential by evaluating different implementation options. This is presented to correlate with contract limitations, security challenges, system integration levels, sustainability, and long-term costs. These requirements are explored in contrast to cloud demand changes observed across specified periods. Dependencies were identified to greatly vary depending on numerous organizational aspects such as performance, size, and workload. The study argues that constructing long-term, sustainable, and cost-efficient strategies for any cloud deployment, depends on the thorough identification of required services off and on-premises. It points out that most of today’s heavy-burdened Smart Buildings are outsourcing these services to costly independent suppliers, which causes unnecessary management complexities, additional cost, and system incompatibility. The main conclusions argue that cloud-computing cost can differ depending on the Smart Building attributes and ICT requirements, and although in most cases cloud services are more convenient and cost effective at the early stages of the deployment and migration process, it can become costly in the future if not planned carefully using cost estimation service patterns. The results of the study can be exploited to enhance core competencies within Smart Buildings in order to maximize growth and attract new business opportunities

An agile information flow consolidator for delivery of quality software projects: technological perspective from a South African start-up

In today’s knowledge-based economy, modern organisations understand the importance of technology in their quest to be considered global leaders. South African markets like others worldwide are regularly flooded with the latest technology trends which can complicate the acquisition, use, management and maintenance of software. To achieve a competitive edge, companies tend to leverage agile methods with the best possible combination of innovative supporting tools as a key differentiator. Software technology firms are in this light faced with determining how to leverage technology and efficient development processes for them to consistently deliver quality software projects and solutions to their customer base. Previous studies have discussed the importance of software development processes from a project management perspective. African academia has immensely contributed in terms of software development and project management research which has focused on modern frameworks, methodologies as well as project management techniques. While the current research continues with this tradition by presenting the pertinence of modern agile methodologies, it additionally further describes modern agile development processes tailored in a sub-Saharan context. The study also aims novelty by showing how innovative sometimes disruptive technology tools can contribute to producing African software solutions to African problems. To this end, the thesis contains an experimental case study where a web portal is prototyped to assist firms with the management of agile project management and engineering related activities. Literature review, semi-structure interviews as well as direct observations from the industry use case are used as data sources. Underpinned by an Activity Theory analytical framework, the qualitative data is analysed by leveraging content and thematic oriented techniques. This study aims to contribute to software engineering as well as the information systems body of knowledge in general. The research hence ambitions to propose a practical framework to promote the delivery of quality software projects and products. For this thesis, such a framework was designed around an information system which helps organizations better manage agile project management and engineering related activities.Information SciencePh. D. (Information Systems

Personalised Environmental Monitoring of Building Occupants: Integration of Scalable Technologies

Urbanised societies spend most of their time indoor. These are places to conduct habitual activities that impact across the life course and are generating discussions on the built environment and its interplay with health and wellbeing. To understand the effect buildings and their enclosed spaces have on people/occupants, there is a need to monitor Indoor Environmental Quality (IEQ) and occupant responses. State-of-the-art monitoring approaches exist, but they have limited utility outside of bespoke scenarios due to their limited pragmatism and large cost. Other emergent technologies exist but questions remain relating to e.g., validity. Other routine/traditional subjective approaches for evaluating building IEQ often negate to account for the experiences of individual occupants, adding to complications. This thesis explores current monitoring IEQ trends, uncovering the needs to make the individual the unit of analysis. Research undertaken explores contemporary needs and shifting trends to pragmatic approaches, localised sensors to provide richer data that could enable a better understanding of environmental and occupant changes. Quantitative measurement of the environmental conditions local to individuals are explored to understand whether spatial density in monitoring can 1) reinforce data pertaining to how building occupants experience indoor conditions and 2) provide additional context to current approaches for data capture, which traditionally focus on qualitative approaches. Through a series of original research this thesis broadly presents the design and development of a multi-modal IEQ monitoring device and a supporting methodological process for monitoring individuals. It identifies that low-cost multi-modal monitoring deployed longitudinally can add significant context to traditional qualitative approaches, with the individual as the unit of analysis. Findings from the thesis present a paradigm shift that could have practical implications for researchers and practitioners, changing the way building performance is assessed and the way its impact on health and wellbeing could be evaluated

Studying the Executive Perception of Investment in Intelligent Systems and the Effect on Firm Performance

This research was conducted to examine the relationship between investment in intelligent systems resources and capabilities (based on artificial intelligence and machine learning algorithms) and the effect on company performance. Despite existing research on the benefits of adopting intelligent systems, companies have been slow to adopt as there is lack of research on intelligent systems use cases that will increase firm performance. This research study used resource-based view (RBV) and dynamic capabilities (DCF) theory to investigate firms’ investment in intelligent systems resources that build intelligent systems capabilities and the association to organization performance dimensions, revenue and profits. To answer this question, an online survey was administered and received responses from 165 participants from companies in Canada and USA. The study findings provide empirical evidence that intelligent systems infrastructure resources and intelligent systems IT human resources increase firm performance, but intelligent systems business resources constructs selected for the study do not contribute to firm performance

AIRM: a new AI Recruiting Model for the Saudi Arabian labour market

One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product. This research aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market bymatching job seekers with vacant positions. Currently, human experts carry out this process; however, this is time consuming and labour intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate, or analyse labour market data, resulting in inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour market without a national labour market data centre. This research proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research-collaborative Human Resources (HR) professionals. As this research is data-driven in nature, it requires collaboration from domain experts. The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERTt pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 minutes, whereas human experts took more than six days on average. Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data