Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare (patient care) domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.      

Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry

In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.     

Publication trends in gamification: A systematic mapping study

The term gamification and gamified systems are a trending area of research. However, gamification can indicate several different things, such as applying the game-like elements into the design of the user interface of a software, but not all gamification is necessarily associated with software products. Overall, it is unclear what different aspects are studied under the umbrella of ‘gamification’, and what is the current state of the art in the gamification research. In this paper, 1164 gamification studies are analyzed and classified based on their focus areas and the research topics to establish what the research trends in gamification are. Based on the results, e-learning and proof-of-concept studies in the ecological lifestyle and sustainability, assisting computer science studies and improving motivation are the trendiest areas of gamification research. Currently, the most common types of research are the proof-of-concept studies, and theoretical works on the different concepts and elements of gamification.     

Human factors in order picking: a content analysis of the literature

Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems.     

Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017

ABSTRACT

Currently, a large number of industrial robots have been deployed to replace or assist humans to perform various repetitive and dangerous manufacturing tasks. However, based on current technological capabilities, such robotics field is rapidly evolving so that humans are not only sharing the same workspace with robots, but also are using robots as useful assistants. Consequently, due to this new type of emerging robotic systems, industrial collaborative robots or cobots, human and robot co-workers have been able to work side-by-side as collaborators to accomplish tasks in industrial environments. Therefore, new human–robot interaction systems have been developed for such systems to be able to utilize the capabilities of both humans and robots. Accordingly, this article presents a literature review of major recent works on human–robot interactions in industrial collaborative robots, conducted during the last decade (between 2008 and 2017). Additionally, the article proposes a tentative classification of the content of these works into several categories and sub-categories. Finally, this paper addresses some challenges of industrial collaborative robotics and explores future research issues.     

TMT-HCC: A tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA.     

新型冠状病毒肺炎综述文献的网络结构分析

对新型冠状病毒肺炎综述文献进行网络结构分析可为新冠疫情的应对提供有效的理论支持。采用可视化软件CiteSpace对Web of Science数据库中新型冠状病毒肺炎综述文献的引文数据进行共被引网络分析，经统计发现文献共被引网络的度分布呈幂律分布，网络连通性较强且存在小世界现象。运用团渗流算法对其进行重叠社团挖掘，检测到7篇同时跨3个社团的核心文献，结合被引频次对网络重叠节点的功能进行验证。借助网络重叠节点的ID符号、介数中心性指标发现相应研究主题间联系紧密。通过不同特征的重要网络节点，能够有效挖掘到相关的研究主题。     

A systematic literature review of studies on business process modeling quality

ContextBusiness process modeling is an essential part of understanding and redesigning the activities that a typical enterprise uses to achieve its business goals. The quality of a business process model has a significant impact on the development of any enterprise and IT support for that process.ObjectiveSince the insights on what constitutes modeling quality are constantly evolving, it is unclear whether research on business process modeling quality already covers all major aspects of modeling quality. Therefore, the objective of this research is to determine the state of the art on business process modeling quality: What aspects of process modeling quality have been addressed until now and which gaps remain to be covered?MethodWe performed a systematic literature review of peer reviewed articles as published between 2000 and August 2013 on business process modeling quality. To analyze the contributions of the papers we use the Formal Concept Analysis technique.ResultsWe found 72 studies addressing quality aspects of business process models. These studies were classified into different dimensions: addressed model quality type, research goal, research method, and type of research result. Our findings suggest that there is no generally accepted framework of model quality types. Most research focuses on empirical and pragmatic quality aspects, specifically with respect to improving the understandability or readability of models. Among the various research methods, experimentation is the most popular one. The results from published research most often take the form of intangible knowledge.ConclusionWe believe there is a lack of an encompassing and generally accepted definition of business process modeling quality. This evidences the need for the development of a broader quality framework capable of dealing with the different aspects of business process modeling quality. Different dimensions of business process quality and of the process of modeling still require further research.     

Exploring the motivational factors on continuous usage intention of smartwatches among actual users

Nowadays, smart wearable technology comes up to different sectors and is gaining momentum to be implemented in everyday objects. Smartwatches are the most popular type of wearable devices. The current study attempts to understand the motivational factors of consumers’ continuous intention to use smartwatches. In particular, a netnography research has been conducted and consumers’ opinions on smartwatches on Amazon have been analysed. Eight key determinants of continuous usage intention of smartwatches have been identified. Some of them (healthology, complementary goods and enabling technologies) were not included in previous technology adoption models, highlighting that existing models need to be updated when it comes in the domain of continuous usage intention of wearable technologies.     

Industrial Dynamics: A Review of the Literature 1990–2009

This paper reviews the literature in the field of industrial dynamics (ID) as it has emerged since I first introduced the term in 1985. Nearly 8,000 articles in 12 major journals have been reviewed and classified under five broad themes that constitute the basic questions in ID: (1) the causes of industrial development and economic growth, including the dynamics and evolution of industries and the role of entrepreneurship; (2) the nature of economic activity in the firm and the dynamics of supply, particularly the role of knowledge; (3) how the boundaries and interdependence of firms change over time and contribute to economic transformation; (4) technological change and its institutional framework, especially systems of innovation; (5) the role of public policy in facilitating adjustment of the economy to changing circumstances at both micro- and macro-levels. Under each theme, the main findings and their implications for theory and policy are summarized. The paper concludes with a summary of the contribution of industrial dynamics to a better understanding of industrial transformation and economic growth and reflections on challenges for future research.