Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite challenging considering a large number of heterogeneous cloud resources. Further, considering the fact that at each point of time, there will be a different and specific cloud service which may be massively required. Hence, cloud monitoring tools can assist a cloud providers or application developers in: (i) keeping their resources and applications operating at peak efficiency; (ii) detecting variations in resource and application performance; (iii) accounting the Service Level Agreement (SLA) violations of certain QoS parameters; and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes.In this paper, we identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools. We further discuss how aforementioned research dimensions and design issues are handled by current academic research as well as by commercial monitoring tools.

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

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The paradigm of service-oriented computing (SOC) has emerged as an approach to provide flexibility and agility, not just in systems development but also in business process management. This modular approach to defining business flows as technology independent services has gained unanimous popularity among end-users and technology vendors alike. Although there is a significant amount of ongoing research on the potential of service oriented architectures (SOAs), there is a paucity of research literature on the factors affecting the adoption of service-oriented computing in practice. This paper reviews the current state of the technology, identifies the factors influencing the decision to adopt service-oriented computing as an enterprise strategy and discusses the associated research literature, and concludes with a suggested research agenda and conceptual framework for investigating the use of service-oriented computing in practice.

Background: Early detection of disease outbreaks, using appropriate surveillance methods, is a basic principle for effective control of epidemics. Indicator-based surveillance methods, such as comprehensive surveillance, sentinel surveillance and syndromic surveillance, have been routinely utilized for early epidemic detection to minimize mortality and morbidity related to emerging infectious disease threats. In addition, event-based surveillance uses unstructured data sources to detect and monitor outbreaks such as media reports, social media and websites. The use of mobile phone technology is growing in many low and middle-income countries, which has made mHealth an efficient means of health communication in such countries for epidemic surveillance, mitigation and response. Mobile Apps may draw data from validated health sources or unvalidated public sources and convey information to responders. The aim of this study was to review mobile Apps used for epidemic surveillance and response.

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There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.