Prioritising Organisational Factors Impacting Cloud ERP Adoption and the Critical Issues Related to Security, Usability, and Vendors: A Systematic Literature Review

Abstract: Cloud ERP is a type of enterprise resource planning (ERP) system that runs on the vendor’s cloud platform instead of an on-premises network, enabling companies to connect through the Internet. The goal of this study was to rank and prioritise the factors driving cloud ERP adoption by organisations and to identify the critical issues in terms of security, usability, and vendors that impact adoption of cloud ERP systems. The assessment of critical success factors (CSFs) in on-premises ERP adoption and implementation has been well documented; however, no previous research has been carried out on CSFs in cloud ERP adoption. Therefore, the contribution of this research is to provide research and practice with the identification and analysis of 16 CSFs through a systematic literature review, where 73 publications on cloud ERP adoption were assessed from a range of different conferences and journals, using inclusion and exclusion criteria. Drawing from the literature, we found security, usability, and vendors were the top three most widely cited critical issues for the adoption of cloud-based ERP; hence, the second contribution of this study was an integrative model constructed with 12 drivers based on the security, usability, and vendor characteristics that may have greater influence as the top critical issues in the adoption of cloud ERP systems. We also identified critical gaps in current research, such as the inconclusiveness of findings related to security critical issues, usability critical issues, and vendor critical issues, by highlighting the most important drivers influencing those issues in cloud ERP adoption and the lack of discussion on the nature of the criticality of those CSFs. This research will aid in the development of new strategies or the revision of existing strategies and polices aimed at effectively integrating cloud ERP into cloud computing infrastructure. It will also allow cloud ERP suppliers to determine organisations’ and business owners’ expectations and implement appropriate tactics. A better understanding of the CSFs will narrow the field of failure and assist practitioners and managers in increasing their chances of success

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

Pilvialustojen identiteetin- ja pääsynhallinnan käytettävyys ja vertailu: Heuristinen asiantuntija-arviointi

Työn aiheena on markkinoiden suurimpien pilvipalveluntarjoajien pilvipalveluiden identiteetin- ja pääsynhallinnan toimintojen käytettävyyden arviointi ja palveluiden vertailu keskenään. Heuristiseen asiantuntija-arviointiin käytettiin Nielsenin kymmentä heuristiikkaa, sekä sovellettua rajatun kognitiivisen läpikäynnin menetelmää, joiden pohjalta tutkittiin, esiintyykö palveluissa selkeitä käytettävyysongelmia ja oliko tuloksissa merkittäviä eroja palveluntarjoajien välillä. Tuloksista nousi esiin neljä keskeistä teemaa: roolien ja oikeuksien ongelmat, epäselvät termit, tiedon kulkuun liittyvät ongelmat ja ongelmat navigoinnissa. Palveluntarjoajien välisessä vertailussa ei erottunut yksikään palvelu selkeästi suhteessa muihin, ja kaikissa palveluissa havaittiin tasaisesti käytettävyysongelmia. Amazon Web Services (AWS) palvelun kohdalla havaittiin 39 käytettävyysongelmaa, rikkoen heuristiikkoja 72 kertaa. Microsoft Azuressa havaittiin 38 käytettävyysongelmaa, rikkoen heuristiikkoja 69 kertaa. Google Cloud Platformin (GCP) osalta havaittiin 34 käytettävyysongelmaa, rikkoen heuristiikkoja yhteensä 63 kertaa. Yleisimmin rikottu heuristiikka kaikissa palveluissa oli Nielsenin 1. heuristiikka liittyen järjestelmän tilan näkyvyyteen. Tutkimuksen otannan ollessa suhteellisen pieni, on aiheesta hyvä tehdä vielä enemmän jatkotutkimusta, varsinkin liittyen termeihin palveluissa ja palveluiden välillä, joissa ei ole käytössä mitään vakiintunutta standardia, ja samaa tarkoittavat termit eroavat palveluiden välillä, tehden aiheesta jo nykyistä haastavamman, sekä vaikuttaen palveluiden käytettävyyteen

Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data

Generating a domain-specific inspection method through an adaptive framework

Many recent innovations and inventions have contributed to rapid technological development, which in turn have produced a wide variety of products that have had a major impact on many businesses in several different domains. These products have their own contextual attributes that have made their usability evaluation, by using traditional usability evaluation methods (UEMs), all the more critical. Almost all previous usability studies have used the Heuristic Evaluation (HE) and User Testing (UT) methods; however, the majority of such studies have described these methods as being not directly applicable to the product being tested, not directly related to the context of the tested product, and not able to identify specific areas and types of usability problems. Furthermore, the lack of a methodological framework that can be used systematically to generate a domain-specific inspection method, which can then be used to assess the usability for a product in any chosen domain and to improve the usability assessment process, represents a missing area in usability testing. Thus, the goal of this research is to generate a domain-specific inspection evaluation method that does not involve users in an actual testing session, i.e. one that is applied by only experts. To reach this goal, firstly, a systematic adaptive framework is presented, called Domain Specific Inspection (DSI), which is characterized as being pertinent to the context and specific target of a chosen domain. This framework is designed to generate a method that avoids the drawbacks of having to use both HE and UT, although it combines their advantages. In addition, this framework assists researchers as it combines feedback from both expert evaluators and potential users in the chosen domain in order to create a focused method. Secondly, this research seeks to validate the adaptive framework practically by generating a DSI method for assessing the usability of selected products. In this regard, websites are chosen as the targeted product, and two experiments are conducted; the first examines the utility of the generated DSI method on the educational domain. The second examines another generated DSI method on the social network domain. In both experiments, the DSI methods are tested intensively through rigorous validation methods and a number of usability metrics to verify the extent to which it achieves the identified goals, needs and requirements that the methods were originally developed to address, and to identify which problems are identified by UT but not identified by HE and/or DSI, and vice versa. Also, an investigation into whether it is essential to conduct the DSI method in conjunction with UT or HE will be undertaken. Furthermore, the roles and numbers of evaluators (together with their types) and users will be examined. The results show that the adaptive framework is able to generate a DSI method that can be used to generate ideas from the different perspectives of multidisciplinary teams in order to create engaging user experiences and to facilitate interactive design. This method enables the discovery of a larger number of serious problems than UT and HE. In addition, it provides optimal results with regard to the identification of comprehensive usability problem areas, and it is more efficient and effective than UT and HE, with minimum input in terms of cost and time. Furthermore, it is able to improve the evaluator performance; thus, the results of the single evaluators, who used the DSI method, provided results that approached or outperformed the effectiveness of the double evaluators, who used HE. Consequently, few evaluators are needed to find a majority of the usability problems if DSI is used

Orchestrating Cloud Infrastructures to Manage Sensitive Data

Presentation for the IDCC 2017 Conference in Edinburgh. <br>This paper describes the Qualitative Data Repository’s configuration of a private cloud to manage sensitive data. In making the transition from a centralized host, QDR has relied on two evaluative frameworks – a questionnaire produced by the Cloud Security Alliance Consensus Assessments Initiative, and the NIST Framework for Cloud Usability