Data Analysis Framework Research Articles

Development of Tools for the Automatic Processing of Advanced Driver Assistance System Test Data

Advanced driver assistance system (ADAS) technologies are key to improving road safety and promoting innovation in the automotive sector. The approval and analysis of ADAS systems, especially automatic emergency braking (AEB) tests, require complex procedures and in-depth data management. This work presents innovative tools developed to facilitate the post-processing of ADAS AEB test data, created in collaboration with Nardò Technical Center. The tool, called Autonomous Code Generation Intelligence (ACGI), introduces an intuitive and intelligent user interface that helps analyze and interpret ADAS test approval regulations. ACGI automates the generation of code sections within a data analytics framework, streamlining the compliance process and significantly reducing the time and programming skills required. This tool allows engineers to focus on high-value tasks, improving overall process efficiency. To achieve this objective, the tool encodes the DAART code framework (Data Analysis and Automated Report Tool) which allows users to carry out real post-processing of the tests conducted on the track. The results demonstrate that both tools simplify and automate critical steps in the ADAS automatic emergency braking test data analysis process. In fact, the tool not only improves the accuracy and efficiency of the analyses but also offers a high degree of customization, making it a flexible and adaptable tool to meet the specific needs of users. In future developments, ACGI could be extended to cover additional ADAS tests and could be equipped with artificial intelligence to suggest configurations based on new regulations.

Uncovering the causal relationships in plant-microbe ecosystems: A time series analysis of the duckweed cultivation system for biomass production and wastewater treatment

The complex interplay among plants, microbes, and the environment strongly affects productivity of vegetation ecosystems; however, determining causal relationships among various factors in these systems remains challenging. To address this issue, this study aimed to evaluate the potential of a data analytical framework called empirical dynamic modeling, which identifies causal links and directions solely from time series data. By cultivating duckweed, a promising aquatic plant for biomass production and wastewater treatment, we obtained a 63-day time series data of plant productivity, microbial community structure, wastewater treatment performance, and environmental factors. We confirmed that empirical dynamic modeling can identify the correct causal directions among temperature, light intensity and plant growth, solely from time series data. Extending the analysis to microbial community data suggested that the bacterial family Comamonadaceae positively affects host duckweed growth and nitrogen removal. Additionally, the predicted abundance of bacterial genes relevant to xenobiotics biodegradation was shown to have a positive effect on organic pollutant removal, supporting the significant role of bacterial metabolism in phytoremediation performance. These results demonstrate the effectiveness of empirical dynamic modeling in uncovering causal relationships within vegetation ecosystems, which are difficult to examine comprehensively through conventional experiment-based approaches.

Cognitive testing in 19 countries to refine WHO's Sexual Health Assessment of Practices and Experiences.

To refine a standard questionnaire on sexual practices, experiences and health-related outcomes to improve its cross-cultural applicability and interpretability. We aimed to explore participants' willingness and ability to answer the draft questionnaire items, and determine whether items were interpreted as intended across diverse geographic and cultural environments. We conducted cognitive interviews (n = 645) in three iterative waves of data collection across 19 countries during March 2022-March 2023, with participants of diverse sex, gender, age and geography. Interviewers used a semi-structured field guide to elicit narratives from participants about their questionnaire item interpretation and response processes. Local study teams completed data analysis frameworks, and we conducted joint analysis meetings between data collection waves to identify question failures. Overall, we observed that participants were willing to respond to even the most sensitive questionnaire items on sexual biography and practices. We identified issues with the original questionnaire that (i)affected the willingness (acceptability) and ability (knowledge barriers) of participants to respond fully; and/or (ii)prevented participants from interpreting the questions as intended, including poor wording (source question error), cultural portability and very rarely translation error. Our revisions included adjusting item order and wording, adding preambles and implementation guidance, and removing items with limited cultural portability. We have demonstrated that a questionnaire exploring sexual practices, experiences and health-related outcomes can be comprehensible and acceptable by the general population in diverse global contexts, and have highlighted the importance of rigorous processes for the translation and cognitive testing of such a questionnaire.

Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multitemporal, Multiwavelength, and Multimessenger Data from Blazar Observations

The Markarian Multiwavelength Data Center (MMDC) is a web-based tool designed for accessing and retrieving multiwavelength and multimessenger data from blazar observations. MMDC facilitates the construction and interactive visualization of time-resolved multiband spectral energy distributions of blazars by integrating (i) archival data from over 80 catalogs and databases, (ii) optical data from all-sky survey facilities such as the All-Sky Automated Survey for Supernovae, Zwicky Transient Facility, and Panoramic Survey Telescope and Rapid Response System, and (iii) newly analyzed data sets in the optical/UV band from Swift-UVOT, in the X-ray band from Swift-X-Ray Telescope and NuSTAR observations, and the high-energy γ-ray band from Fermi-Large Area Telescope observations. MMDC distinguishes itself from other online platforms by the large quantity of available data. For instance, it includes data from all blazar observations by Swift and NuSTAR, as well as the results of detailed spectral analysis in the γ-ray band during different emission states, covering the period from 2008 to 2023. Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. MMDC is an innovative tool that significantly enhances blazar research by providing a comprehensive framework for data accessibility, analysis, and theoretical interpretation, thereby advancing our understanding of blazar emissions and the underlying astrophysical processes.

SNoMaN: a visual analytic tool for spatial social network mapping and analysis

ABSTRACT Spatial social networks (SSNs) are node-link structures that evidence interpersonal or inter-organizational relationships, where nodes and edges have a defined geographic location. To model SSNs, users need both geographic and social network metrics. However, there are few GUI-based analytic tools that enable simultaneous spatial and social network exploration. In this paper, following the research framework of Exploratory Spatial Data Analysis (ESDA) and design principles of social network analysis tools, we derived three design goals of exploratory spatial social network analysis (SSNA). Guided by these design goals, we provide a visual analytic tool, SNoMaN, which links network and geographical layouts and helps users conduct SSNA by interactively computing and visualizing SSN metrics, describing spatial distributions, exploring associations, and detecting anomalies. We introduce new types of visual diagrams, including Cluster–Cluster Plots, Centralization Plots, on-the-fly mapping of geometrically bounded network modules, and Route Factor Diagrams. We illustrate these new approaches using use case studies of a 1960s network of Mafia members, a global flight network, and a food donation-sharing network in southwestern Virginia. We find that SNoMaN can be used to generate data insights that fuse a system’s spatial and social dimensions that are hard to obtain otherwise.

National occupational safety and health systems: Exploring the underlying networks for future sustainable development

Any intervention, initiative, or programme – whether implemented at the local or national level – builds upon existing infrastructure, which, if properly managed, enables sustainable development. Within this perspective, this study examines national Occupational Safety and Health (OSH) systems, specifically a set of European countries, by studying their dynamics through cross-national analyses and investigating their similarities and differences, fostering OSH improvement through new national strategies and possible collaboration between national entities. A secondary data analysis is conducted based on the OSHwiki database of the European Agency for Safety and Health at Work (EU-OSHA), which has a dedicated section on European national OSH systems. This database makes different countries easily comparable – under one single language and the same degree of granularity – which is not trivial if considering the other available primary sources. For a consistent sample, with a controlled heterogeneity, this study includes eleven European countries. An iterative inductive process of data comparison on national OSH systems is used to establish a suitable framework for data analysis. As a result, a range of fundamental functions and recurring bodies, covering – to different extents – the previously identified functions, constitute the framework, which are the most transversal functions for the selected countries. These findings provide valuable insights into how these functions are carried out differently across countries, detecting potential shareable best practices and improvement directions for the future sustainability of national OSH systems.

FAIR data management: a framework for fostering data literacy in biomedical sciences education

Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master’s thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.

Shadow Banking, Stock Market Volatility, and Stock-Money Market Correlation: Evidence from China

ABSTRACT The study on the relationship between shadow banking (SB) and stock market volatility is scarce. Based on sample data from January 2006 to May 2024, this paper dives deep to clarify whether and how China’s SB brings uncertainties to China’s stock market and the impact of SB on the stock-money market correlation. The novelty of this paper is that we connect low-frequency SB with high-frequency financial market information under the framework of mixed-frequency data analysis. Our findings indicate that the expansion of SB could directly enhance China’s stock market volatility. There exists a persistent correlation between China’s stock and money markets in the long run. Furthermore, the rapid development of SB also strengthens the stock-money market correlation in the long run. Since SB could reduce the controllability of China’s monetary policy and increase China’s money market uncertainties, we believe that China’s money market may serve as an important channel for SB to spread risks to the stock market volatility by pushing the two markets to move more closely. We shed new light on the literature regarding the relationship between SB and stock market. This paper makes the first attempt to identify the role of SB in driving the dynamics of stock-money market correlation.

An Evolving Multivariate Time Series Compression Algorithm for IoT Applications.

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach's potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.

A framework for data analysis from long-term monitoring in anthropized coastal areas

In heavily urbanised coastal areas, such as port basins, long-term monitoring of key parameters can provide deep insights into local physical and ecological processes, supporting sustainable coastal planning and enhancing effective environmental management strategies. This study introduces a novel, user-friendly framework for analysing multi-year time series data at monitored coastal sites, to assess variations arising from human or natural changes. It explores how physical drivers can influence biological and ecological conditions across various temporal and spatial scales. This approach makes monitoring a vital tool for planning and control. While the proposed data processing techniques have been discussed in previous literature, their integration into a unified framework is what differentiates the present work from prior studies. The framework’s applicability is demonstrated in the Bari Port area in Southern Italy, where monitoring stations have been in place since 2019 due to dredging operations. Despite its specific application, the framework is adaptable to any coastal area with meteo-oceanographic stations providing high-quality data. Key findings indicate that dredging primarily reduced turbulent mixing due to containment measures, affecting the distribution and transport of turbidity and oxygen. Additionally, our analysis shows that evaluating tracers’ diffusivity requires consideration of turbulence, waves, and tides.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

Conceptualising a data analytics framework to support targeted teacher professional development

ABSTRACT This paper proposes a conceptual framework enabling the development and adoption of descriptive, diagnostic, predictive and recommendatory data analytics in teacher professional learning by harnessing some of the affordances of digital technologies to convert data into actionable insights. The paper argues for a technology-enhanced approach that uses data to support teachers in selecting appropriate professional development (PD) options to improve their professional practice. The ultimate goal is to lay the foundations for a robust and adaptable data analytics framework that could offer tailored PD recommendations based on the developmental trajectories of individual teachers. The paper analyses data-supported personalised professional learning as meaning-making and the appropriation of cultural artefacts within the ‘mobile complex’ - consisting of structures, agency, and the dynamic interplay between cultural and technological tools and practices. This study undertakes a comprehensive literature review to identify key concepts, gaps, and theoretical insights, informing the development of a data analytics framework. The resultant framework integrates personalisation, teacher agency and autonomy, contextual relevance, and ethical safeguards into PD process, aiming to foster a responsive, collaborative, and context-aware data-supported PD.

A goodness-of-fit test for functional time series with applications to Ornstein-Uhlenbeck processes

High-frequency financial data can be collected as a sequence of time-ordered curves, such as intraday prices. The Functional Data Analysis (FDA) framework offers a powerful approach to uncover information embedded in the shape of the daily paths, often unavailable from classical statistical methods. A novel goodness-of-fit test for autoregressive Hilbertian (ARH) models is introduced, imposing only the Hilbert-Schmidt condition on the autocorrelation operator. The test statistic is formulated in terms of a Cramér–von Mises norm, with calibration achieved via a wild bootstrap resampling procedure. A simulation study examines the test's finite-sample performance in terms of power and size. Furthermore, a new specification test for diffusion models, including Ornstein-Uhlenbeck processes, is proposed, illustrated with an application to intraday currency exchange rates. Specifically, a two-stage methodology is proffered: firstly, the relationship between functional samples and their lagged values is assessed using an ARH(1) model; second, under linearity, a functional F-test is conducted.

Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework

Three AI developments, classified as forms of human enhancements, center around progress at the intersection of AI, nanotechnology and biotechnology. Our research advances the understanding of AI and human enhancement by data-driven analytics and offers practical tools for future research and societal applications. It is based on a survey that was launched by PRC in February 2021 to more than 5,000 respondents from the U.S. It consists of about 100 questions that are grouped using a prefix code by the 3 above-mentioned human enhancement, science role, concerns and excitements, perceived algorithm fairness and demographics. To investigate this survey and extract insights regarding the general attitude, a data analytics framework is proposed that consists of clustering using DBSCAN and K-means, ANOVA for clusters, PCA, t-SNE and UMAP for graphical visualization, prediction and advanced customers’ profiles analyses. Both clustering methods indicate distinct profiles for AI customers. Most of them are moderate, but two smaller groups define the tech-ethics advocates and tech-forward visionaries. For prediction, in the multi-class classification task, the ROC-AUC score is 0.852 and average F1 Score is 0.987. Following the results, both technology creators and legislators must work collaboratively to ensure that technological advancements are ethically grounded, widely accepted and aligned with societal values.

In silico framework for genome analysis

Genomes hold the complete genetic information of an organism. Examining and analyzing genomic data plays a critical role in properly understanding an organism, particularly the main characteristics, functionalities, and evolving nature of harmful viruses. However, the rapid increase in genomic data poses new challenges and demands for extracting meaningful and valuable insights from large and complex genomic datasets. In this paper, a novel Framework for Genome Data Analysis (F4GDA), is developed that offers various methods for the analysis of viral genomic data in various forms. The framework’s methods can not only analyze the changes in genomes but also various genome contents. As a case study, the genomes of five SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) VoC (variants of concern), which are divided into three types/groups on the basis of geographical locations, are analyzed using this framework to investigate (1) the nucleotides, amino acids and synonymous codon changes in the whole genomes of VoC as well as in the Spike (S) protein, (2) whether different environments affect the rate of changes in genomes, (3) the variations in nucleotide bases, amino acids, and codon base compositions in VoC genomes, and (4) to compare VoC genomes with the reference genome sequence of SARS-CoV-2.

Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity.

This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time-series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes intersubject similarities and differences between two predetermined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intrasubject similarities and differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a noniterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the GRIDY models is evaluated through simulations conducted under various scenarios. An application is also presented to compare resting-state functional MRI data collected from multiple subjects in autism spectrum disorder and controlgroups.

Analysing the modest fashion market: an empirical study of e-commerce best-selling products

Purpose This study examines the dynamics of the modesty-conscious market within the global fashion industry. Specifically, the research aims to understand and analyse the preferences of consumers in this market segment and provide guidance for fashion companies seeking to engage with this sizable audience consumer demand for modest fashion, such as loose-fitting garments and headscarves for women. Design/methodology/approach Employing a mono-method quantitative research approach, this study uses a comprehensive big data analytics framework to analyseashion data sets obtained from e-commerce websites. Findings The findings highlight a persistent and growing demand for modest fashion; that demand proved resilient in the face of challenges presented by the COVID-19 pandemic. Modest shoppers demonstrate price sensitivity, and their preference for premium brands over affordable ones varies considerably. Crucial factors contributing to the success of modest clothing as best-sellers include price, retailer, colour and fabric, while the discount feature is less important. Research limitations/implications While this research provides significant insights, it is important to acknowledge its limitations. This study relies on data gathered from certain e-commerce websites, and specific nuances of consumer behaviour may not be fully captured. In addition, the scope is limited to a specific timeframe and may not account for long-term market shifts. Practical implications Fashion companies could use the results of this study to customise their strategy for engaging the modesty-conscious demographic. Comprehending the significance of elements such as price, retailer, colour and fabric can allow firms to enhance their product offerings and marketing strategies. Social implications This study highlights the social ramifications of the modesty-focused industry, stressing the changing tastes and requirements of customers within this sector. By aligning their strategies with these societal shifts, fashion companies can contribute to a more inclusive and diverse industry landscape. Originality/value This research contributes to the academic literature on modest fashion by using a unique combination of exploratory data analysis and machine learning techniques with fashion e-commerce data sets. This study addresses a gap in the use of big data within this field, and provides novel insights into consumer demand for modest garments. This study challenges the prevailing assumption that consumers of modest fashion prioritise premium pricing, and offers fresh insights into their price sensitivity across both mass-market and luxury segments. It contributes to the literature on consumer behaviour in niche fashion markets and introduces a theoretical framework for understanding the intersection of fashion, culture and consumer economics within the context of modest fashion.

Evaluation of long-term care expenditures from the silver economy perspective

Silver economics is a field of science that evaluates the long-term care needs that develop because of aging from the economics perspective within the supply and demand framework. The expenditures and investments made to meet long-term care needs constitute the main subject of long-term care expenditures. This study empirically tested the relationship between long-term care expenditures and life expectancy. Expenditures on long-term care facilities (LTCFE) were taken as the dependent variable, and life expectancy at birth (LEAB) and life expectancy at 65 years and over (LEO65) were taken as independent variables. The analysis included data from 25 countries with regular LTCFE from 2004 to 2020. The variables were analyzed using the econometric model established within the framework of panel data analysis. According to the least squares analysis results, a 1% increase in LEAB increased LTCFE by 2.1%, while a 1% increase in LEO65 decreased LTCFE by 0.54%. Moreover, a unidirectional causality relationship was found between LTCFE and LEAB and LEO65. The empirical evidence suggests that life expectancy impacted LTCFE. According to the findings, they emphasize the importance of aligning policy frameworks with demographic changes to ensure sustainable long-term care systems in aging societies.

Critical Discourse Analysis on King Salman’s Speeches at the United Nations General Assembly

This study aims to reveal the ideology in King Salman's political speeches at the United Nations General Assembly. A critical qualitative design was applied in analyzing two of King Salman's speeches in 2020 and 2021. In line with the research objectives, the ideological square is used as the theoretical framework for data analysis. The results of microanalysis show that King Salman uses the basic strategy of emphasizing positive things about 'us' in 129 data (58.6%), emphasizing negative things about 'them' in 60 data (27.3%), and de-emphasizing positive things about 'them' in 31 data (14.1%). As for de-emphasizing negative things about ‘us’ not found in the data source. In the microanalysis dimension, among the rhetorical discursive strategies found were actor description in 77 data (35.0%), consensus in 16 data (7.3%), comparison in 6 data (2.7%), values expression in 76 data (34.5%), victimization-criminalization in 30 data (13.6%), and national self-glorification in 15 data (16.9%). Based on this analysis, the general ideologies found in King Salman's speech are nationalism and anti-imperialism. King Salman constructed Saudi Arabia's national identity in international eyes as a philanthropist, humanitarian, peace initiator, and non-violence country. These identities are then represented as the trademark of Saudi Arabia's foreign policy.