Advancing the state of the art in the modelling and simulation of information systems evaluation

It is widely accepted that Information Systems Evaluation (ISE) is a powerful and useful technique that can be used to assess IT/IS investments in an a-priori or a-posteriori sense. Traditional approaches to ISE have tended to centre upon financial and management accounting frameworks, seeking to reconcile tangible and intangible costs, benefits, risks and value factors. Such techniques, however, do not provide the IS researcher or practitioner with further insight or appreciation of any inherent and implicit inter-relationships, in the investment justification process. Thus, this paper outlines and discusses via a taxonomy and resulting classification, alternative and complementary approaches that can be applied to ISE from the fields of Artificial Intelligence (AI), Operational Research (OR) and Management Science (MS). The paper subsequently concludes that such approaches can be potentially used by researchers and practitioners in the field, as a basis for carrying out further research in the field of applied ISE

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Impact of COVID-19 on water and sanitation in Mumbai slums.

Mumbai City (19.07° N, 72.87° E) is the true example of ‘diversity in extreme level’. This well-known city is commonly known as the financial capital of India and is the 12th richest city in the world. Mumbai city (Municipal Corporation of Greater Mumbai or MCGM) spreads around 437.5 km2, with 12.5 million population as per Census 2011, with a population density of 83,660 per km2 and approximately 6.5 million are living in the slums without proper access to water, sanitation and hygiene (WASH). There is a debatable topic, ‘water is a blessing or a curse?’ We know water means life but in monsoon season these views might lead to conflicts. Mumbai alone has recorded 585.5 mm precipitation in July resulting in severe flooding across the city. The slum communities of Mumbai are at the receiving end of these erratic patterns due to inefficient drainage and lack of basic facilities. This pandemic situation has proved again the urgency of WASH. WHO has already listed the COVID-19 virus as one of the most contagious diseases which has been spreading exponentially due to the poor toilet facilities, lack of access to clean water and unhygienic activities in slums. The survey data from different slum communities configures their perception related to WASH and our study links it with the pandemic and the resultant adaptive capacity ranking. Although most of the Mumbai slum has a good literacy rate (69%) but lack of awareness among these slum communities lead to a vulnerable situation. The slum clusters of Mumbai have become COVID-19 hotspots and also resulted in losses of jobs and human lives. Through FCM (Fuzzy Cognitive Mapping) and SWOT analysis, the study discovers present social, technical, and economic aspects and perception of these slum communities to analyze their adaptive capacity towards COVID-19. Keywords: WASH, COVID-19, Slum, Mumba

Development of an Adaptive Environmental Management System for Lejweleputswa District: A Participatory Approach through Fuzzy Cognitive Maps

Published ThesisEnvironmental pollution caused by mines within the district of Lejweleputswa in Free State is a major contributor to health issues and the inability to grow crops within the mining communities. Mining industries continue to develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of environmental pollution affecting the society. Though there are Information Communication and Technology (ICT) based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of how they disseminate information. Furthermore, non-adopting community members are being regarded as inherently conservative or irrational, but these community members argue that the recommendations and technologies brought to them are not always appropriate to their circumstances. There was concern that local people’s knowledge of their environment, farming systems, and their social as well as economic situation had been ignored and underestimated when ICTs solutions are being implemented (Warburton & Martin, 1999). Another challenge is that there is no station to monitor pollution for small communities such as Nyakallong in the district. This result in mining communities depending on their own local knowledge to observe and monitor mining related environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, fuzzy cognitive maps were used to model, analyse and represent this linguistic local knowledge. Although this local knowledge assists in mitigating environmental pollution, incorporating it with scientific knowledge will improve its relevance, trustworthiness and acceptability by majority of community members and policy-makers. Information and Communication Technologies (ICTs) can accelerate this integration; this is the focus of this research. The increased usages of Information Technology being witnessed today makes it the most important factor for the world to depend on for solutions to many of today’s and tomorrow’s problems. These solutions make use of various forms for dissemination purposes, one of the most versatile dissemination device is a mobile phone since majority of the world’s population do own a mobile phone. In this way information is easily accessible by almost everyone that needs it. A novel environmental management solution was designed to work within the mining communities of Lejweleputswa. The research started off by designing a unique integration framework that creates the much-needed link between local knowledge and scientific knowledge. The framework was then converted into an adaptable environmental pollution management system prototype made up of three components; (1) gathering environmental pollution knowledge; (2) environmental monitoring and; (3) environmental dissemination and communication. To achieve sustainability, relevance and acceptability, local knowledge was integrated in each of the three components while mobile phones were used as both input and output devices for the system. In order to facilitate collection and conservation of local knowledge on environmental monitoring, an elaborate android-based mobile application was developed. Wireless sensor-based gas sensor boards were acquired, and deployed as a compliment to conventional monitoring stations, they were used to gather scientific knowledge. To allow for public access to the system’s data, a web portal and an SMS-based component were also implemented. In order to collect local knowledge from community, a case study of Nyakallong community in Lejweleputswa was carried out. On completion of the system prototype, it was evaluated by participants from the community; 90% of respondents gave a score of ‘excellent ‘

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering

Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions