Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

A New Big Data Architecture for Analysis: The Challenges on Social Media

The streams of social media big data are now becoming an important issue. But the analytics method and tools for this data may not be able to find the useful information from this massive amount of data. The question then becomes: how do we create a high-performance platform and a method to efficiently analyse social networks' big data;how to develop a suitable mining algorithm for finding useful information from social media big data. In this work, we propose a new hierarchical big data analysis for understanding human interaction, and we present a new method to measure the useful tweets of Twitter users based on the three factors of tweet texts. Finally, we use this test implementation score, in order to detect useful and classification tweets by interested degree.

Perceived differences in peer-to-peer accommodation before and after COVID-19: evidence from China

PurposeBased on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.Design/methodology/approachA guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.FindingsThis research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed;perceived emotional values associated with peer-to-peer stays were more complex;perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend;tourist preferences for types of experiences changed, and people changed their destination selections;perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.Research limitations/implicationsThis research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.Originality/valueTo the best of authors' knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

Information: From Cognitive-Sentient Exploration of Reality to Predictive Big Data Assisted Informational Era

In this article it is shown that information is a concept not only useful and used for the communication systems and data operation, but also for the understanding of the matter non-living and living structuration. On this basis it is described the Informational Model of Consciousness and the functions and operability of the Informational System of the Human Body, allowing to define and understand the Cognitive-Sentient Exploration of Reality used as a tool by our predecessors to create some archaic models of the universe and human, remained up to date as references from philosophic point of view, and still operating nowadays in the creative sciences and arts fields in our informational era. Introducing the concept of information-related matter, it was possible to explain the informational processes in human body and eukaryotic/prokaryotic cells, the basic units of life, and to show that in the living entities an informational system operating similarly with that of the human can be identified and defined as Informational System of the Living Structures. The predictive properties of the Cognitive-Sentient Exploration of Reality is nowadays assisted by the predictive big data analysis, helping in various scientific domains where a high quantity of data was accumulated, including in the bioengineering field and human health, particularly for the prediction of the evolution of COVID-19 cases in the world, with examples for Romania, Spain and USA. Multiple applications involving artificial intelligence and the informational models of human and living structures for a healthy society are mentioned, in particular in neurosciences, biosciences and bioengineering. The priority of the Romanian concepts on information and the important contribution of the Romanian informational science oriented to understand the brain functions/operability and applications in biomedical field is highlighted. © 2023, Publishing House of the Romanian Academy. All rights reserved.

Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.

Over the past two years, organizations and businesses have been forced to constantly adapt and develop effective responses to the challenges of the COVID-19 pandemic. The acuteness, global scale and intense dynamism of the situation make online news and information even more important for making informed management and policy decisions. This paper focuses on the economic impact of the COVID-19 pandemic, using natural language processing (NLP) techniques to examine the news media as the main source of information and agenda-setters of public discourse over an eight-month period. The aim of this study is to understand which economic topics news media focused on alongside the dominant health coverage, which topics did not surface, and how these topics influenced each other and evolved over time and space. To this end, we used an extensive open-source dataset of over 350,000 media articles on non-medical aspects of COVID-19 retrieved from over 60 top-tier business blogs and news sites. We referred to the World Economic Forum's Strategic Intelligence taxonomy to categorize the articles into a variety of topics. In doing so, we found that in the early days of COVID-19, the news media focused predominantly on reporting new cases, which tended to overshadow other topics, such as the economic impact of the virus. Different independent news sources reported on the same topics, showing a herd behavior of the news media during this global health crisis. However, a temporal analysis of news distribution in relation to its geographic focus showed that the rise in COVID-19 cases was associated with an increase in media coverage of relevant socio-economic topics. This research helps prepare for the prevention of social and economic crises when decision-makers closely monitor news coverage of viruses and related topics in other parts of the world. Thus, monitoring the news landscape on a global scale can support decision-making in social and economic crises. Our analyses point to ways in which this monitoring and issues management can be improved to remain alert to social dynamics and market changes.

An Analytical Survey of Difficulty Faced in an Online Lecture During COVID-19 Pandemic Using CRISP-DM

COVID-19 has greatly disturbed life in many ways and has changed the way we live. Various surveys have been conducted in different fields, and the teaching-learning process has been affected to a great extent. During this pandemic, various online tools and technologies have been available for guiding students without attending school. Many governments, corporations, and research fields have officially ordered to use of online media for the teaching-learning process. Platforms such as Google Meet, Microsoft Team, and Web-e-X have allowed and arranged for online video conferencing mediums to achieve the goal of the teaching-learning process. However, as mentioned above, there are some serious issues with the online teaching-learning process. These include problems with continuous network bandwidth during sessions, physical and mental presence in the class, difficulties handling mathematics classes, and the potential for non-sense activities that may disturb the entire class. In order to discover knowledge, I am using a new approach to data mining technology called CRISP-DM. This study addresses the effectiveness of online teaching mode and learning and the challenges faced by students and teachers who are taking online classes during COVID-19. According to this study, 88.2% of students did not have proper internet or technology facilities, 58.30% of students were not satisfied with online learning, 85.3% of students complained about eyesight issues from taking online classes on devices, and 50.01% of students were unable to manage university affairs © 2023 Manmohan Singh, Vinod Patidar, Shaheen Ayyub, Anita Soni, Monika Vyas Dharmendra Sharma and Amol Ranadive. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license

Application of text mining in PTT forum in analysis of consumer preference for online shopping platforms

With the advent of economic development and Internet technology, offline retail stores have gradually shifted to virtual shopping networks, and consumers' online shopping has become increasingly prosperous. Moreover, since the COVID-19 pandemic, the public has taken the initiative to reduce the number of outdoor activities, which has increased consumers' willingness to shop online. This research takes Shopee, PChome, and MOMO online platforms as the research subjects. We obtained data from 2020 to 2021 on PTT e-shopping and lifeismoney boards. In addition, we used web text crawling analysis, R data text mining and positive/negative sentiment analysis, and word cloud to determine popular keywords related to online shopping issues, and consumers' preferences for online shopping platforms are studied. The results show that "seller", "problem", and "offer" are the most discussed keywords indicating that people care about the consumer experience to a certain extent. The next most frequent keywords are "coat", "dress", "shopee", "discount", "cheap", "Taobao", and "Taiwan", which will appear according to the needs of consumers in different seasons. Based on the sentiment analysis, the consumers posted more positive articles than negatives in PChome (2.33) and MOMO (2.34) compared to Shopee (1.11). Through term frequency analysis, we can understand the trends and suggestions brought by popular keywords of online shopping to consumers and online store sellers, and also allow online store sellers to analyze the key decision concerns and the possibility of customers' behavior. © 2023, International Journal of Systematic Innovation. All rights reserved.

COVID-19 Vaccination Data Visualization: Issues and Challenges

Data Visualization plays an important role for patterns and trends analysis in trillion of data rows Big Data analysis, where the data can be represented in some graphical forms. Hence, the data could be more comprehensible in its visual summary in dashboards and storyboards. This study aims to discuss some issues and challenges in visualizing COVID-19 vaccination datasets. There are some possible issues in data visualization, as it is not easy and may be challenging to produce a good dashboard that are interesting and easy for viewers to understand. Therefore, this study focuses on some issues that may arise during performing a data visualization on the COVID-19 dataset. In this study, there are three dashboards have been studied, which are the COVID-19 tracker, its effectiveness, and its acceptance. The first two dataset are derived from Ministry of Health Malaysia bank data, whereas the third dataset is from a survey to support this analysis. The selected attributes are states, the number of people who have received the vaccine as adults, children, and teenagers, and the number of people who already received boosters, and reasons to not get a booster. The visualization issues found within the dashboard are mis-choice of colors, mis-choice of visual object type, lack of interactivity, and plotting too much data. As a result, this proposed alternative solutions for those issues such as color deliberately, pick a suitable visual object, create an interactive dashboard, and reduce the information overload in visualizing the data. © 2022 IEEE.

A Retrospective Database Analysis of Before and After Social Distancing in Relation to Pediatric Infection Rate and Healthcare Services Usage During the COVID-19 Pandemic.

BACKGROUND: Social distancing policy was introduced in Israel in 2020 to reduce the spread of COVID-19. The aim of this study was to analyze the effect of social distancing on other infections in children, by comparing disease rate and healthcare utilization before and after social distancing. METHODS: This was a before-and-after study. Within this retrospective database analysis of parallel periods in 2019 (Period 1 and 2) and 2020 (period 3 - pre-lockdown period, and Period 4 - lockdown period) we included all pediatric population registered in the electronic medical records of the Maccabi Healthcare Services, Israel, looking at the occurrence of non-COVID infections, antibiotic purchasing, doctor visits, Ambulatory Emergency Care Centers visits, Emergency Departments' visits, and hospitalizations. RESULTS: 776,828 and 777,729 children from 2019 and 2020, respectively, were included. We found a lower infection rate in 2020 vs 2019. We did not find a difference in infection rate between Periods 1-2, while a significant difference was found between Periods 3- 4. We found a significant difference between Periods 2-4, with a higher RR than in Periods 1-3. A modest decrease in Ambulatory Emergency Care Center visits, and lower increase in emergency department visits and hospital admissions was found in 2020. We found decreases in antibiotic purchasing between Periods 1-3 and Periods 2-4, more pronounced in 2020 than in 2019. CONCLUSIONS AND RELEVANCE: Analysis of before and after social distancing and masking showed reduced prevalence of non-COVID pediatric infections, consumption of health care services, and antibiotics consumption.

Nursing process, derived from the clinical care classification system components, as an earlier indicator of nursing care during a pandemic.

BACKGROUND: During COVID pandemic response, an early signal was desired beyond typical financial classifications or order sets. The foundational work of Virginia K Saba informed the essential, symbiotic relationship of nursing practice and resource utilization by means of the Clinical Care Classification System [CCC]. Scholars have confirmed the use of the CCC as the structure for data modeling, focusing on the concept of nursing cost [1]. Therefore, the purpose of this retrospective, descriptive study was to determine if analysis of CCC Care Component codes could provide a high granularity signal of early shifts in patient demographics and in nursing care interventions and to, then, determine if nursing care intervention shifts indicated changes in resource utilization. METHODS: For a large multi-facility healthcare system in the USA, patients cared for in an acute care setting/hospital-based care unit were the population of interest. Through prior and ongoing efforts of ensuring Evidenced-Based Clinical Documentation [EBCD], a data model was utilized to determine changes in the patient's nursing diagnoses, nursing interventions, during care episodes, for patients with acute symptoms or diagnosed/confirmed COVID. RESULTS: The structure of CCC revealed 22 billion individual instances of the CCC Care Component/Concept codes for the data sets for 2017 and during COVID, a considerably large data set suitable for pre- and post- event analyses. The component codes were included in a string data set for concept/diagnosis/intervention. DISCUSSION: By our analysis, these CCC Information Model elements determined a clear ability to detect increasing demands of nursing and resources, prior to other data models, including supply chain data, provider documented diagnostic codes, or laboratory test codes. Therefore, we conclude CCC System structure and Nursing Intervention codes allow for earlier detection of pandemic care nursing resource demands, despite the perceived challenges of "timeliness of documentation" attributed to more constrained timelines of data models of nursing care.

Using the big data analysis and basic information from lecture Halls to predict air change rate

School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate. © 2023 Elsevier Ltd

Supply chain insights from social media users' responses to panic buying during COVID-19: the herd mentality

Purpose>This study applied the herd mentality theory to explore local and global social media users' responses to panic buying across the USA, UK and Australia during the COVID-19 crisis to understand the implications on operations and supply chains.Design/methodology/approach>A total of 208,806 social media user-generated content (UGC) pieces were collected from Twitter in three countries – the USA, UK and Australia. The analysis of this big qualitative data was performed using machine learning–based software – Leximancer.Findings>Positive and negative sentiment towards panic buying during the COVID-19 crisis was observed in the UGC. No significant differences in social media UGC sentiment between the three countries were found;however, differences did exist in key themes. This suggests that the focus, not the sentiment, of consumers' responses to panic buying differed across countries. Social media users follow their location-based and topic-consonant social "herd”, rather than the global "herd”.Research limitations/implications>This study was the first to show that social media users' herd mentality differs in a crisis. The herd mentality of social networks is dependent on factors such as the geographic location of the social network (herd), which can differ from the global herd's reaction, specifically in terms of topics evident in UGC.Practical implications>Operations and supply chain managers need to include social media UGC analysis in their strategies in crisis management responses. The topics, not the sentiment, of consumers' responses to panic buying require managerial actions.Originality/value>This is the first study to show that herd mentality during a crisis, such as COVID-19, is not unidimensional and varies according to the location of the social media network with profound implications for operations and supply chain managers.

Research on the Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on the Global Trade Economy Based on Big Data Analysis

The coronavirus disease 2019 (COVID-19) epidemic that began in early 2020 quickly formed a global trend, bringing unprecedented shocks to many countries' and even the global trade economy. Big data is the main feature of the Internet era, which has transformed the industrial development pattern of modern society and has now flourished in the field of trade economy;therefore, it is of great significance to apply the big data analysis technology to study the impact of the COVID-19 epidemic on the global trade economy. On the basis of summarizing and analyzing previous research works, this paper, expounded the research status and significance of the impact of the COVID-19 epidemic on the global trade economy, elaborated the development background, The study results of this paper provide a reference for further researches on the impact of the impact of the COVID-19 epidemic on the global trade economy based on big data analysis.

Digital IT Innovation to Improve Supply Chain Resilience: A Systematic Literature Review

The advance of digital technologies such as big data, cloud computing, and artificial intelligence ushers in the digital era for modern societies. Digital IT innovation plays an increasingly important role in helping supply chains recover from disruptions due to disastrous events like the COVID-19 outbreak. Nevertheless, there is a lack of systematic literature review on the phenomenon. As such an attempt, this paper explores the role of digital technology innovation in enhancing supply chain resilience and answers this question through a literature review and summarizes six dimensions of supply chain resilience, which provides some theoretical guidance for subsequent studies. © 2022 IEEE.

Digital Innovations and AI in Mental Health as Facilitators of Sustainable Competitive Economy in Post-COVID-19 Recovery

The COVID-19 pandemic has presented a serious threat to mental health on a global scale. With unemployment worldwide reaching staggering highs and the loss of job security for millions of people, recent studies suggest that the pandemic can be linked to elevated anxiety levels, psychological distress, depression, PTSD, and suicidal behavior. While the case number of those infected by the COVID-19 virus (more than 170 million infected and more than 3.5 million deaths on May 30, 2021) certainly poses an unprecedented global health challenge, the detrimental effects on the population's mental health are far more difficult to measure, and thus address. Governments around the world are therefore tasked with providing more effective and affordable solutions to tackling both aspects of the pandemic's impact via increased investment in healthcare, medication, and vaccine distribution, as well as through an emphasis on innovative approaches to improve mental healthcare. Digital innovation and AI have already shown their potential for both prevention and treatment of mental health problems, as valid supplements for mental health practitioners.Effective combatting of the pandemic's consequences on mental health is crucial for rebuilding sustainable and competitive economies in the post-COVID-19 recovery. © 2022 by World Scientific Publishing Co. Pte. Ltd.

Perceived differences in peer-to-peer accommodation before and after COVID-19: evidence from China

PurposeBased on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic. Design/methodology/approachA guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19. FindingsThis research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed;perceived emotional values associated with peer-to-peer stays were more complex;perceived social values decreased, host-guest interactions were reduced and online communication became a stronger trend;tourist preferences for types of experiences changed, and people changed their destination selections;perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased. Research limitations/implicationsThis research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19. Originality/valueTo the best of authors' knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

Using the big data analysis and basic information from lecture Halls to predict air change rate

School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate.

Evidence of SARS-CoV-2 reinfection: analysis of 35,000 subjects and overview of systematic reviews.

BACKGROUND: Reinfection by SARS-CoV-2 is a rare but possible event. We evaluated the prevalence of reinfections in the Province of Modena and performed an overview of systematic reviews to summarize the current knowledge. METHODS: We applied big data analysis and retrospectively analysed the results of oro- or naso-pharyngeal swab results tested for molecular research of viral RNA of SARS-CoV-2 between 1 January 2021 and 30 June 2021 at a single center. We selected individuals with samples sequence of positive, negative and then positive results. Between first and second positive result we considered a time interval of 90 days to be sure of a reinfection. We also performed a search for and evaluation of systematic reviews reporting SARS-CoV-2 reinfection rates. Main information was collected and the methodological quality of each review was assessed, according to A Measurement Tool to Assess systematic Reviews (AMSTAR). RESULTS: Initial positive results were revealed in more than 35,000 (20%) subjects; most (28%) were aged 30-49 years old. Reinfection was reported in 1,258 (3.5%); most (33%) were aged 30-49 years old. Reinfection rates according to vaccinated or non-vaccinated subjects were 0.6% vs 1.1% (p < 0.0001). Nine systematic reviews were identified and confirmed that SARS-CoV-2 reinfection rate is a rare event. AMSTAR revealed very low-moderate levels of quality among selected systematic reviews. CONCLUSIONS: There is a real, albeit rare risk of SARS-CoV-2 reinfection. Big data analysis enabled accurate estimates of the reinfection rates. Nevertheless, a standardized approach to identify and report reinfection cases should be developed.

Prediction and Analysis of COVID-19’s Prevention and Control Based on AnyLogic in the Background of Big Data

Based on the surging situation of COVID-19 and its rapid propagation speed and wide range, schools as a crowded place are prone to outbreak of large areas. Therefore, in order to ensure the normal progress of campus teaching order, campus should be the focus of epidemic prevention and control. By using AnyLogic simulation software, using system dynamics model and combined with the actual data operation of College D Teaching Building, this paper simulates the D Teaching Building, and intuitively shows the simulation and control effect through the Time Plot. The simulation results show that the school can reduce the number of infected people by taking effective measures to control the contact rate of students and vaccinating them in time;Finally, effective treatment can greatly increase the rehabilitation rate of infected people and reduce the number of deaths. This method has certain guiding significance in today’s severe epidemic prevention and control. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.