Big Data Approach Research Articles

Spatio-temporal behaviour of cruise passengers at the destination: a big data approach using mobile location events

Purpose The purpose of this paper is to provide a deeper understanding of the spatio-temporal behaviour of cruise passengers at a given destination using the city of Hamburg as a use case. Design/methodology/approach Big data in the form of passive location events from smartphones were analysed to determine the spatio-temporal behaviour of cruise tourists. The data were filtered using a definition created specifically for determining the signals of cruise passengers and were analysed using spatial statistics and network analysis methods. Findings In Hamburg, cruise passengers have a relatively small area of activity that is concentrated in the city's core tourist area. Foreign cruise passengers are less spatially concentrated than German passengers. Foreign passengers generate most of their visits at the airport and in the city's shopping areas, while German passengers are most frequently seen at the railway station and at the city's maritime experience locations. Originality/value Although numerous publications have examined the spatio-temporal behaviour of cruise passengers at the destination, to the best of the authors’ knowledge, no studies have used big data sources for this purpose. This paper uses passive location events from smartphones to determine the routes taken by cruise tourists in Hamburg, shows the strengths and weaknesses of this data source and suggests how cruise tourists can be defined from the data.

A multi-center big-data approach for precise PICC-RVT prognosis and identification of major risk factors in clinical practice

BackgroundThe Peripherally Inserted Central Catheter (PICC) is a widely used technique for delivering intravenous fluids and medications, especially in critical care units. PICC may induce venous thrombosis (PICC-RVT), which is a frequent and serious complication. In clinical practice, Color Doppler Flow Imaging (CDFI) is regarded as the gold standard for diagnosing PICC-RVT. However, CDFI not only requires prominent time and effort from experienced healthcare professionals, but also relies on the formation and development of PICC-RVT, especially at early stages of PICC-RVT, when PICC-RVT is not apparent. A prognosis tool for PICC-RVT is crucial to bridge the gap between its diagnosis and treatment, especially in resource-limited settings, such as remote healthcare facilities. ObjectiveEvaluate over 14,885 models from various machine learning techniques to identify an effective prognostic model (referred to as PRAD - PICC-RVT Assessment via Deep-learning) for quantifying the risks associated with PICC-RVT. MethodsTo tackle the challenges associated with PICC-RVT diagnosis, we gathered a comprehensive dataset of 5,272 patients from 27 healthcare centers across China. From a pool of 14885 models from various machine learning techniques, we systematically screened a data-driven prognostic model to quantify the risks associated with PICC-RVT. This model aims to provide objective evidence, and facilitate timely interventions. ResultsThe proposed model displayed exceptional predictive accuracy, achieving an accuracy of 86.4 % and an AUC of 0.837. Based on the prognosis model, we further incorporated a weight analysis to identify the major contributing factors for PICC-RVT risk during catheterization. Albumin levels, primary diagnosis, hemoglobin levels, platelet levels, and education level are emphasized as important risk factors. ConclusionsOur method excels in predicting early PICC-RVT risks, especially in asymptomatic patients. The findings in this paper offers insights into controllable PICC risk factors that could benefit vast patients and reduce disease burden through stratification and early intervention.

New French height velocity growth charts: An innovative big-data approach based on routine measurements.

Height velocity is considered a key auxological tool to monitor growth, but updated height velocity growth charts are lacking. We aimed to derive new French height velocity growth charts by using a big-data approach based on routine measurements. We extracted all growth data of children aged 1 month-18 years from the electronic medical records of 42 primary care physicians, between 1 January 1990 and 8 February 2018, throughout the French metropolitan territory. We derived annual and biannual height velocity growth charts until age 15 years by using the Lambda-Mu-Sigma method. These new growth charts were compared to the 1979 French and 2009 World Health Organisation (WHO) ones. New height velocity growth charts were generated with 193 124 and 209 221 annual and biannual values from 80 204 and 87 260 children, respectively, and showed good internal fit. Median curves were close to the 1979 French or 2009 WHO ones, but SD curves displayed important differences. Similar results were found with the biannual height velocity growth charts. We produced new height velocity growth charts until age 15 years by using a big-data approach applied to measurements routinely collected in clinical practice. These updated growth charts could help optimise growth-monitoring performance.

Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.

Myoelectric control, the use of electromyogram (EMG) signals generated during muscle contractions to control a system or device, is a promising input, enabling always-available control for emerging ubiquitous computing applications. However, its widespread use has historically been limited by the need for user-specific machine learning models because of behavioural and physiological differences between users. Leveraging the publicly available 612-user EMG-EPN612 dataset, this work dispels this notion, showing that true zero-shot cross-user myoelectric control is achievable without user-specific training. By taking a discrete approach to classification (i.e., recognizing the entire dynamic gesture as a single event), a classification accuracy of 93.0% for six gestures was achieved on a set of 306 unseen users, showing that big data approaches can enable robust cross-user myoelectric control. By organizing the results into a series of mini-studies, this work provides an in-depth analysis of discrete cross-user models to answer unknown questions and uncover new research directions. In particular, this work explores the number of participants required to build cross-user models, the impact of transfer learning for fine-tuning these models, and the effects of under-represented end-user demographics in the training data, among other issues. Additionally, in order to further evaluate the performance of the developed cross-user models, a completely new dataset was created (using the same recording device) that includes known covariate factors such as cross-day use and limb-position variability. The results show that the large data models can effectively generalize to new datasets and mitigate the impact of common confounding factors that have historically limited the adoption of EMG-based inputs.

Sexual selection for single song repertoires.

Despite birdsong being one of the most studied models of sexual selection, how it operates in birds that sing only one song remains poorly understood. A recent study using a big data approach reveals a novel aspect of song that may potentially function as an honest signal of male quality and a way to maintain listener attention.

Examining differential effects of target and context repetition in visual search: Insights from a big data approach

Examining differential effects of target and context repetition in visual search: Insights from a big data approach

Comparing apples to oranges to bananas: A big data approach to understanding the joint influences of stimulus properties, trial history, and individual differences

Comparing apples to oranges to bananas: A big data approach to understanding the joint influences of stimulus properties, trial history, and individual differences

Drowning Out Dissent: The Thai Military’s Quest to Fabricate Popular Support on Twitter

Why and how do autocracies discursively conduct digital astroturfing against their populations? As these regimes increasingly co-opt social media to manipulate online political discourse, the current political disinformation literature continues to privilege a Cold War paradigm, focusing on countries that dominate Western foreign policy priorities and concerns. Its normative underpinnings overshadow other understudied cases that have also been weaponizing social media to manipulate domestic online discourses. Concurrently, findings from ethnographic interviews and big data approaches remain theoretically disconnected from the long-held logics of regime stabilization. I argue that regime digital astroturfing follows offline events that spark online discourses with collective action potential. In countering these discourses, regime-linked digital astroturfers exploit social media affordances to discursively cheerlead the regime and devalue regime challengers to create a semblance of popular support for the former. I adopt a mixed-methods approach informed by Contextual Text Coding to examine 14,430 tweets linked to the Royal Thai Army in February 2020 to subject my central propositions to empirical scrutiny. My findings demonstrated that astroturfing efforts on Twitter throughout this period, which discursively cheerleaded the military/autocratic regime of General Prayut Chan-o-cha and devalued their pro-democratic opposition with negative frames, were spurred by a mass shooting incident in the city of Korat that exposed the ruling elites to widespread online backlash. This contribution deepens case-specific knowledge about an understudied case in the literature and also strengthens our understanding about the adaptations of authoritarian legitimation strategies in the digital era and the militarization of social media communication.

Tweeting climate change: analyzing actor diversity and influence in climate-change discourse post-Paris agreement

The Paris Agreement represents the notable characteristics of global environmental governance, namely multi-actor and multi-level governance. Despite these changes, limited empirical studies have investigated the actors in global environmental governance who participate in political discourse on climate change. To comprehend whether or not the Paris Agreement’s actors represent the transformation of global environmental governance, we adopted a big-data approach to analyze Twitter’s climate-change discourse. The classification of actors provided an understanding of the diversity of the actors involved in providing public information. We also calculated the closeness centrality and identified the changes in the actors’ influence, comparing the changes in the 5 years since the Agreement was adopted. The results showed an increased diversity of actor types. Non-state actors are not homogeneous in their changes, and Intergovernmental Organizations exhibited an increased influence. The findings indicated that the actors participating in the climate-change discourse post-Paris Agreement partially reflect the changes in global environmental governance.

A Big Data Approach to Forecast Injuries in Professional Sports Using Support Vector Machine

A Big Data Approach to Forecast Injuries in Professional Sports Using Support Vector Machine

Generalized Denoising Coder with Multi-modal Approach for Handling Incomplete Information in Public Health Big Data Analytics

A data-driven health model incorporates many missing and incomplete values, and the effectiveness of a health model that relies on characteristics not captured by the consumer is low. A Deep Learning (DL) health paradigm aims to improve the precision of learning weights. A DL-based health model is trained by optimizing the weights to maximize accuracy. If the learning process is excluded, the accuracy of applying a DL-based health model to the user's circumstance may be reduced, and it becomes the responsibility of people to manage Public Health Big Data Records (PHBDR). To handle incomplete information, this study suggested a generalized denoising coder with a multi-modal approach in public health big data records (GDC-MM-PHBDR). The suggested technique uses a Generalized Denoising Coder (GDC), which estimates the output values following the relevant input values using Neural Networks (NN). The National Health and Nutrition Examination Study (NHNES) provided data for this investigation. This approach allows for the estimation of incomplete information in the PHBDR. Multimodality, which the PHBDRs provide, enables compiling data for a single item from many sources. The layout of the GDC is multi-modal. The GDC-MM-PHBDR approach continuously demonstrates excellent performance, attaining maximum accuracy across all noise levels. It begins at 98.31% accuracy with a noise factor of 0.05 and significantly decreases to 94.5% with a noise factor of 0.25.

AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol

IntroductionThis paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologies and their relevance to the opioid crisis. Fatal opioid...

The metaverse experience: a big data approach to virtual service consumption

Purpose The experience of leaving the real world and entering a virtual service environment makes many individuals happy. This study heeds the call by multiple researchers to conceptualize, interpret and illustrate the impact of the perceived service experience in the metaverse in a holistic way. In particular, this study aims to understand how the consumption of experiences is perceived in a metaversal space. Design/methodology/approach The authors analyze mega virtual live events with famous artists broadcast in virtual worlds. The authors take a big data approach and include two studies to gain insight into the online public audience’s perceptions and experiences in the metaverse. In the first study, the authors analyze text from YouTube with Leximancer. In the second study, the authors go one step further to refine the conceptual model from Study 1. The authors scrutinize additional Facebook comments using seeded Latent Dirichlet Allocation (LDA). Findings The findings reveal that the meta service experience (MEX) encompasses four dimensions: immersion, metascape, immediacy and hedonism. Originality/value This research provides important guidance not only for consumer behavior scholars but also for service marketers and event planners. The study proposes research opportunities to advance service experience research in the metaverse.

Cartographies of warfare in the Indian subcontinent: Contextualizing archaeological and historical analysis through big data approaches

Some of the most notable human behavioral palimpsests result from warfare and its durable traces in the form of defensive architecture and strategic infrastructure. For premodern periods, this architecture is often understudied at the large scale, resulting in a lack of appreciation for the enormity of the costs and impacts of military spending over the course of human history. In this article, we compare the information gleaned from the study of the fortified cities of the Early Historic period of the Indian subcontinent (c. 3rd century BCE to 4th century CE) with the precolonial medieval era (9-17th centuries CE). Utilizing in-depth archaeological and historical studies along with local sightings and citizen-science blogs to create a comprehensive data set and map series in a “big-data” approach that makes use of heterogeneous data sets and presence-absence criteria, we discuss how the architecture of warfare shifted from an emphasis on urban defense in the Early Historic period to an emphasis on territorial offense and defense in the medieval period. Many medieval fortifications are known from only local reports and have minimal identifying information but can still be studied in the aggregate using a least-shared denominator approach to quantification and mapping.

"I don't wanna die, but my brain insists that I should": a big qualitative data approach to the lived experiences of suicidal thoughts.

There remains a dearth of knowledge concerning the phenomenology of suicidal thoughts, with research focusing on reasons for feeling suicidal rather than their mental expression. While clinical interviews remain the standard phenomenological approach, such exploration of lived experiences may prove challenging for this sensitive topic. As a complementary alternative, the use of naturally-occurring online data is opportune for capturing elaborations on tabooed phenomena. In this phenomenological study, we present a thematic analysis on lived experiences of suicidal thoughts as spontaneously reported by non-identified users of a Reddit online board (r/Depression), collecting 668 posts using the search terms "suicidal ideation," "suicidal thoughts," and "suicide." Codes were grouped into descriptive categories summarizing the properties of thoughts, their effects, and their relation to suicide. Then, an interpretative synthesis yielded global themes connecting salient meanings on the experience of suicidal thoughts. With a long-term and recurring nature, thoughts of suicide appear in the form of vivid imagery and daydreaming's, initially bringing relief to adverse feelings but eventually becoming conditioned and all-consuming. Rather than a wonderment, they are experienced as intrusive thoughts by people struggling to make meaning of their occurrence. When conciliating the presence of unwanted thoughts, users express intricate relations to wishing or not to die, as well as varying perceptions of control over actions or fear of suicidal behavior. With an innovative application of big qualitative data into phenomenological analysis, this study contributes to an initial characterization of suicidal thoughts, uncovering findings that are not contemplated into current conceptualizations of suicidality. The analysis is limited by a restricted context of posts and unknown demographics, and further research with clinical interviews is warranted for in-depth exploration of suicidal thoughts.

Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial

The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual’s data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform’s potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.

Quality control for serological testing

ObjectivesThe quality control of serological assays remains controversial. The aim of this project was to describe the problems associated with a working model for controlling these assays and solutions, including using a source of well-defined targets and acceptable limits, a process to identify lot-to-lot reagent variation and an interpretation of the result that accounted for the clinical situation. False-negative results are problematic but can be reduced by identifying and comparing reagent lot variation with previous results. MethodsThe components of the Quality Assurance strategy are the following: Lot-to-lot reagent and calibrator variation assessment; dynamic, big-data approach to determine accurate targets and acceptable limits for manufacturer-provided QC material; negative QC monitoring process; use of commutable EQA with a sufficient method subgroup size to assess bias; clinical assessment of any statistically flagged error; and provision of support to the clinician for the interpretation of results. ResultsThe model described has been used for twelve months, and acceptable variation has been maintained. ConclusionsThe paper presents a solution that emphasizes the early detection of reagent lot variation and patient risk rather than instrument control.Reducing the risk of a false result to patients requires optimal assay quality control and an effective mechanism to support the clinician’s use of these results in diagnosis and monitoring. The problems of serological assays are well-known, but there remain few integrated solutions in the literature.

A holistic big data approach to understand and manage increasing pollen-induced respiratory allergies under global change.

A holistic big data approach to understand and manage increasing pollen-induced respiratory allergies under global change.

A big data approach to risk management and control: Cybersecurity in accounting

A big data approach to risk management and control: Cybersecurity in accounting

Exploring new realms or losing touch? Assessing public beliefs about tourism in the metaverse–a big-data approach

Purpose The Metaverse is rapidly reshaping the understanding of tourism, yet the public perception of this new domain remains largely uncharted empirically. This paper aims to build on the technology acceptance model (TAM) and diffusion of innovations theory (DIT) to fill this gap, offering crucial insights that could inform scholars and practitioners in both the tourism and technology sectors. Design/methodology/approach Using a big-data approach, the authors applied machine learning to scrape comments made by social media users on recent popular posts or videos related to tourism in the Metaverse from three prominent social media platforms. The cleaning process narrowed down 15,461 comments to 2,650, which were then analysed using thematic, emotion and sentiment analysis techniques. Findings The thematic analysis revealed that virtual tourism evokes a complex range of public beliefs. While many express awe and excitement toward its immersive capabilities, others remain sceptical about authenticity compared to physical travel. Additional themes show people draw comparisons to real-world tourism, discuss technology’s role and note educational value and novelty. However, some comments raise concerns about potential societal harms, exploitation and mental health impacts. Sentiment analysis found over half of the comments positive, though some were negative. Emotion analysis showed contentment, happiness and excitement as most frequent, though sadness, worry and loneliness also featured. Overall, perceptions of Metaverse tourism encompass enthusiasm yet substantial ambivalence. Originality/value To the best of the authors’ knowledge, this study is one of the first to comprehensively analyse public discussions on Metaverse tourism. It takes TAM and Rogers’s DIT a step further and provides fresh insights into how these theories can be employed in the emerging field of Metaverse tourism. The themes revealed new conceptual insights into multidimensional factors shaping public beliefs about Metaverse tourism and thus informing scholarly research on virtual interaction and technology acceptance regarding Metaverse tourism. In addition, the results can help tourism providers, platforms and marketers address salient public beliefs and sentiments/attitudes in developing marketing offerings, experiences and communications. Over time, this analysis methodology can be used to track the evolving public perceptions of Metaverse tourism.