With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

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The advent of the digital age has led to a rise in different types of data with every passing day.In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues and future research direction for this field of study.

In this age of heterogeneous systems, diverse technologies are integrated to create application-specific solutions. The recent upsurge in acceptance of technologies such as cloud computing and ubiquitous Internet has cleared the path for Internet of Things (IoT). Moreover, the increasing Internet penetration with the rising use of mobile devices has inspired an era of technology that allows interfacing of physical objects and connecting them to Internet for developing applications serving a wide range of purposes. Recent developments in the area of wearable devices has led to the creation of another segment in IoT, which can be conveniently referred to as Wearable Internet of Things (WIoT). Research in this area promises to personalize healthcare in previously unimaginable ways by allowing individual tracking of wellness and health information. This chapter shall cover the different facets of Wearable Internet of Things (WIoT) and ways in which it is a key driving technology behind the concept of personalized healthcare. It shall discuss the theoretical aspects of WIoT, focusing on functionality, design and applicability. Moreover, it shall also elaborate on the role of wearable sensors, big data and cloud computing as enabling technologies for WIoT.

Electroencephalography (EEG) is used to prognosticate recovery in comatose patients with hypoxic ischemic brain injury (HIBI) secondary to cardiac arrest. We sought to determine the prognostic use of specific EEG patterns for predicting disability and death following HIBI secondary to cardiac arrest. This systematic review searched Medline, Embase, and Cochrane Central up to January 2020. We included original research involving prospective and retrospective cohort studies relating specific EEG patterns to disability and death in comatose adult patients suffering HIBI post cardiac arrest requiring admission to an intensive care setting. We evaluated study quality using the Quality of Diagnostic Accuracy Studies 2 tool. Descriptive statistics were used to summarize study, patient, and EEG characteristics. We pooled study-level estimates of sensitivity and specificity for EEG patterns defined a priori using a random effect bivariate and univariate meta-analysis when appropriate. Funnel plots were used to assess publication bias. Of 5191 abstracts, 333 were reviewed in full text, of which 57 were included in the systematic review and 32 in metaanalyses. No reported EEG pattern was found to be invariably associated with death or disability across all studies. Pooled specificities of status epilepticus, burst suppression, and electrocerebral silence were high (92-99%), but sensitivities were low (6-39%) when predicting a composite outcome of disability and death. Study quality varied depending on domain; patient flow and timing performed was well conducted in all, whereas EEG interpretation was retrospective in 17 of 39 studies. Accounting for variable study quality, EEG demonstrates high specificity with a low risk of false negative outcome attribution for disability and death when status epilepticus, burst suppression, or electrocerebral silence is detected. Increased use of standardized cross-study protocols and definitions of EEG patterns are required to better evaluate the prognostic use of EEG for comatose patients with HIBI following cardiac arrest.

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Big data analytics has gathered immense research attention lately because of its ability to harness useful information from heaps of data. Cloud computing has been adjudged as one of the best infrastructural solutions for implementation of big data analytics. This research paper proposes a five-layer model for cloud-based big data analytics that uses dew computing and edge computing concepts. Besides this, the paper also presents an approach for creation of custom big data stack by selecting technologies on the basis of identified data and computing models for the application.

The excessive amounts of data generated by devices and Internet-based sources at a regular basis constitute, big data. This data can be processed and analyzed to develop useful applications for specific domains. Several mathematical and data analytics techniques have found use in this sphere. This has given rise to the development of computing models and tools for big data computing. However, the storage and processing requirements are overwhelming for traditional systems and technologies. Therefore, there is a need for infrastructures that can adjust the storage and processing capability in accordance with the changing data dimensions.Cloud Computing serves as a potential solution to this problem. However, big data computing in the cloud has its own set of challenges and research issues. This chapter surveys the big data concept, discusses the mathematical and data analytics techniques that can be used for big data and gives taxonomy of the existing tools, frameworks and platforms available for different big data computing models. Besides this, it also evaluates the viability of cloud-based big data computing, examines existing challenges and opportunities, and provides future research directions in this field.