Big data analytics in healthcare: a systematic literature review

The current study performs a systematic literature review (SLR) to synthesise prior research on the applicability of big data analytics (BDA) in healthcare. The SLR examines the outcomes of 41 studies, and presents them in a comprehensive framework. The findings from this study suggest that applications of BDA in healthcare can be observed from five perspectives, namely, health awareness among the general public, interactions among stakeholders in the healthcare ecosystem, hospital management practices, treatment of specific medical conditions, and technology in healthcare service delivery. This SLR recommends actionable future research agendas for scholars and valuable implications for theory and practice

BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT

Big Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC). The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

publishedVersio

“Sana All”: Netizens’ Perception of Government Responses to COVID-19

Netizens posted views that contradicted the results released by research agencies about the Philippine government's responses to COVID-19. In this study, Twitter, which is a key communication channels, was the main source of data to explore the public’s perception of the Philippine government’s performance to the pandemic response. To limit tweets to be studied, sana all, a language phenomenon mostly used at the time of community lockdowns, was observed and utilized as a code identify relevant tweets. Between March and August 2020, 257 tweets were collected and researchers used presuppositions to extract socio-political context and truths implied in tweets. Then, the data underwent a 6-level thematic analysis and eleven categories were formed. The prevalent language intention emerging from the tweets is empathy. This paper will discuss how empathy associates the sound dissatisfaction of the netizens with the responses made by the current administration to combat the COVID-19 multi-effects

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability

Benchmarking operation readiness of the high-speed rail (HSR) network

At present, HSR networks have been significantly extended to accommodate increased passenger demand because the service is believed to unleash social benefits. Nevertheless, the investment in the HSR project is substantially higher than in other transportation projects. Also, most of the HSR network has faced unavoidable issues during operation, such as lack of passenger demand, low operating profit, and non-safety issues. Despite issues addressed, HSR organisations could not maintain their performance to reach the standard and the globe’s directions, especially the sustainability pillar. Those issues become ineffective for HSR organisations, impacting the passenger’s quality of life and socio-economic. This thesis aims to develop a systems-based benchmarking framework for all HSR networks to enhance operating costs, punctuality, productivity, risk and uncertainty, sustainability, and urbanisation efficiency. Those six KPIs are necessary for the sustainable development of the upcoming HSR network. The thesis has made several significant contributions to developing a benchmarking framework for long-term improvement. First, this thesis is the world’s first to integrate a Bayesian distribution and Python programming to improve safety across the railway network. As a result, the created model shows higher accuracy than previous models due to the combination of long-term data sets. Moreover, this thesis reveals the decision tree and the Petri-net models to identify the risk level. Thus, it is an advantage for the rail authorities to evaluate and enhance safety performance. Next, the thesis focuses on life cycle assessment (LCA) and life cycle cost (LCC) frameworks. The LCA model reflects the environmental perspectives of each rail network. This thesis provides an in-depth analysis of each life cycle stage that shows the energy consumption rate and CO2 emission rate. The outcome can point out energy consumption and CO2 emission performance. In addition, this thesis is the world's first study concerning uncertainty costs during HSR operations regarding the LCC analysis. The net present value calculation with a discount rate has been added with the Monte Carlo Simulation. In this section, the developed model allows HSR authorities to firmly manage the budget under uncertain conditions, especially during an operating stage. Lastly, this thesis concentrates on the social impacts of HSR service, particularly on a living quality, educational benefits, and economic opportunities. The long-term datasets have been analysed by using K-nearest neighbour and Pearson correlation techniques. The result can point out the company’s performance toward social advantages. By adopting the models in practice, people can obtain more benefits from the HSR service. By promoting the novelty framework into practice, benchmarking through diversification of current HSR networks is addressed. The selected routes and networks are chosen using a range of factors. For illustrate, the collected networks must be stable and trustworthy, as determined by their long-term operation for at least ten years. Furthermore, the selected HSR lines are mixed in geography, technology, and relevant conditions to avoid bias. The five noteworthy networks and routes consist of Beijing-Shanghai (China), Paris-Lyon (France), Tokyo-Osaka (Japan), Madrid-Barcelona (Spain), and Seoul-Busan (South Korea). The analysis results indicate that none of the HSR networks illustrates high performance in all pillars. An overview result demonstrates that the CR’s networks perform the best performance following the Renfe, SNCF, JR Central, and Korail networks. In addition, the thesis has provided policy implications for long-term development, in particular, safety services, social impacts, environmental impacts, and technology and innovation. Those suggestions can be applied practically to both existing and upcoming HSR networks

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission