Data Analytics Research Articles

Institutional pressure and responsible innovation: how big data analytics adoption drives manufacturing SMEs toward competitiveness

Institutional pressure and responsible innovation: how big data analytics adoption drives manufacturing SMEs toward competitiveness

Big data analytics for quality variation over work shifts in manufacturing systems

ABSTRACT In the manufacturing industry, mass production enables manufacturers to produce parts with high precision and lower costs. Multiple shifts operate production processes to maximize efficiency. Several researches have been conducted on the impact of shift work on labor’s health and habits. However, there have been few studies on the influence of shift work on the consistency of product quality. This paper provides a methodology to analyze the impact of different work shifts in a real electronic board manufacturing industry. The study uses big data and analytics to assess product quality from data. Non-parametric kernel density estimation is used to approximate the distribution of good products in each work slot. Then several metrics are used to measure the dissimilarity between the estimated densities. The approach can be leveraged in various problems related to process performance and quality. The obtained results show that there is no significant difference in terms of product quality between work shifts. These prove the consistency of the manufacturing processes and the homogeneity of performance across work shifts in the studied factory. Compared to the literature, the paper presents the first quantitative analysis to compare production process performance and product quality over shift works.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world.

The use of data and analytics in professional football organisations has grown steadily over the last decade. Nevertheless, how and whether these advances in sports analytics address the needs of professional football remain unexplored. Practitioners from national federations qualified for the FIFA World Cup Qatar 2022™ and professional football clubs from an international community of practitioners took part in a survey exploring the characteristics of their data analytics infrastructure, their role, and their value for elaborating player monitoring and positional data. Respondents from 29 national federations and 32 professional clubs completed the survey, with response rates of 90.6% and 77.1%, respectively. Summary information highlighted the underemployment of staff with expertise in applied data analytics across organisations. Perceptions regarding analytical capabilities and data governance framework were heterogenous, particularly in the case of national federations. Only a third of national federation respondents (~30%) perceived information on positional data from international sports data analytics providers to be sufficiently clear. The general resourcing limitations, the overall lack of expertise in data analytics methods, and the absence of operational taxonomies for reference performance metrics pose constraints to meaningful knowledge translations from raw data in professional football organisations.

Semantic-enabled architecture for auditable privacy-preserving data analysis

Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.

15 years of Big Data: a systematic literature review

Big Data is still gaining attention as a fundamental building block of the Artificial Intelligence and Machine Learning world. Therefore, a lot of effort has been pushed into Big Data research in the last 15 years. The objective of this Systematic Literature Review is to summarize the current state of the art of the previous 15 years of research about Big Data by providing answers to a set of research questions related to the main application domains for Big Data analytics; the significant challenges and limitations researchers have encountered in Big Data analysis, and emerging research trends and future directions in Big Data. The review follows a predefined procedure that automatically searches five well-known digital libraries. After applying the selection criteria to the results, 189 primary studies were identified as relevant, of which 32 were Systematic Literature Reviews. Required information was extracted from the 32 studies and summarized. Our Systematic Literature Review sketched the picture of 15 years of research in Big Data, identifying application domains, challenges, and future directions in this research field. We believe that a substantial amount of work remains to be done to align and seamlessly integrate Big Data into data-driven advanced software solutions of the future.

Predictive Maintenance in Bridge Infrastructure

Predictive maintenance, driven by artificial intelligence and data analytics, represents a transformative approach to infrastructure management. By continuously monitoring asset conditions through sensors (Vibration, Temperature, crack, moisture and corrosion, 3D vision sensor and Ultrasonic & sonic) and advanced algorithms, predictive maintenance can forecast maintenance needs before infrastructure failures occur, leading to efficient scheduling, reduced costs, minimized downtime, and improved safety. Beyond cost savings, this approach enhances sustainability by optimizing resource usage, extending asset service life, and reducing environmental impact. Real-world applications in road infrastructure and the power sector demonstrate its potential to ensure reliability, prevent accidents, and meet the growing demands placed on critical infrastructure. Embracing predictive maintenance technologies is essential for addressing 21st-century infrastructure challenges effectively and safeguarding the well-being of communities

Gas emissivities and absorptivities for H2O–CO2–CO mixtures

AbstractIn this work, an approximate solution is given to compute the gas emissivities and absorptivities for H2O–CO2–CO mixtures. The proposed model is valid for temperatures from 300 to 2700 K and pressure path‐length from 0.06 to 40 atm m. In the calculation of the analytical solution (AS), the nonlinear methods of Galerkin and Ritz were implemented based on the residual solution of the differential roots of the Finite Element Method. For each point value using the AS, the spectral absorptivity and emissivity were determined, while for the gas mixture the emissivity and absorptivity were computed using the Hottel Graphical Method (HGM) and the proposed method. In the emissivity calculations, the HGM shows less adjustment, with values of ±20% and ±15% for 79.1% and 56.2% of the available analytical data, while the proposed model correlates adequately with the available data, showing an average deviation of ±15% and ±10% for 91.4% and 76.2% of the available data. In the absorptivity estimations, the HGM shows a weaker fit, with values of ±20% and ±15% for 77.3% and 51.4% of the experimental data, respectively, while the proposed model shows good agreement with the available data, with a mean deviation of ±15% and ±10% for 89.8% and 74.1% of the test made.

A subbands study on the resistivity of field-effect CNT-based piezoresistive nanocomposites.

In this paper, an analytical model based on the percolation theory has been developed to predict the subbands effect on the effective electrical resistivity of carbon nanotubes (CNT)-based polymer nanocomposites. The CNTs are considered as randomly distributed or aligned channel material in the polymer transmitting electrons through tunneling. The tunneling effect takes into account the electron transmission between each connected pair of CNTs to evaluate electrical resistivity. The modeling approach contains two steps of primary prediction of resistivity and further calculation of CNTs' displacements and subsequent change of the resistance. A good agreement is found between the analytical model predictions and experimental data when the tunneling behavior was considered in the percolation transition region. The effect of CNT diameter, orientation state, and subbands on the resistivity has been investigated. The results depict that subbands increment is a collateral benefit to the aspect ratio in decreasing the resistivity. The analytical results demonstrate that a random CNT dispersion leads to a decreased piezoresistivity, while an increased strain range depicts a more non-linear behavior.

Crossroads between Big Data and entrepreneurship: current key trends

AbstractEntrepreneurship is vital for economic growth and development, as it fosters innovation, creates jobs, and enhances competition and competitiveness. At the same time, in contemporary society, where the Internet has assumed the role of the primary instrument for both communication and commerce, effective utilisation of vast amounts of data has become imperative for businesses, especially in the entrepreneurial environment. To identify the main connections in two of the currently most trending concepts in business, we undertook a systematic literature review focusing on Big Data, entrepreneurship, and their interconnections in business management through 301 research documents. While numerous studies have already delved individually into Big Data and entrepreneurship, the interconnection between these concepts still has research potential. Our results reflect a growing interest in leading research on these topics, with specific leading authors and countries. Furthermore, we show and discuss a conceptual map consisting of five clusters, unveiling interesting connections such as the impact of blockchain on firms, the correlation between Big Data and firm performance, the role of data science, and the significance of Big Data analytics for operational performance. The article ends by examining how Big Data and entrepreneurship change the business landscape. While entrepreneurs use data for decision-making, innovation, and tailored customer experiences, there are lingering concerns such as data security and privacy. The future of business and innovation will depend, in part, on the crossroads between entrepreneurs and Big Data technologies.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Automated passive income from stock market using machine learning and big data analytics with security aspects

Automated passive income from stock market using machine learning and big data analytics with security aspects

Is it about “them”? leveraging big data research to understand anti-immigrant discourse

The paper explores the potential of big data analytics for researching anti-immigrant discourse. We emphasize contextualization as an essential element of research and follow a hybrid approach inspired by best practices of computational content analysis, combining human hermeneutic expertise with supervised machine learning to classify a corpus of comments in online news communities in Singapore over 6 months ( N = 399,225). The paper highlights how big data analytics can provide a nuanced and critical apprehension of immigrant-related discourse in large social media datasets.

Inverting social innovation to transform health system responses to climate change adaptation and mitigation in the global south

Systems thinking is aimed at understanding and solving complex problems that cut across sectors, an approach that requires accurate, timely, and multisectoral data. Citizen-driven big data can advance systems thinking, considering the widespread use of digital devices. Using digital platforms, data from these devices can transform health systems to predict and prevent global health crises and respond rapidly to emerging crises by providing citizens with real-time support. For example, citizens can obtain real-time support to help with public health risks via a digital app, which can predict evolving risks. These big data can be aggregated and visualized on digital dashboards, which can provide decision-makers with advanced data analytics to facilitate jurisdiction-level rapid responses to evolving climate change impacts (e.g., direct public health crisis communication). In the context of climate change, digital platforms can strengthen rapid responses by integrating information across systems (e.g., food, health, and social services) via citizen big data. More importantly, these big data can be used for rapid decision-making,a paradigm-changing approach that can invert social innovation, which we define as co-conceptualizing societal solutions with vulnerable communities to improve economic development with a focus on community wellbeing. However, to foster equitable and inclusive digital partnerships that invert social innovation, it is critical to avoid top-down approaches that sometimes result when researchers in the Global North and South collaborate. Equitable Global South–North partnerships can be built by combining digital citizen science and community-based participatory research to ethically leverage citizen-driven big data for rapid responses across international jurisdictions.

Urgensi Pembuatan Akta Kesaksian dan Akta Pernyataan Ahli Waris Oleh Notaris

In matters of inheritance, an individual requires evidence to prove that they are legitimate heirs of the deceased.The Deed of Inheritance Testimony and The Deed of Heirs' Statement are deeds that are used as the basis for creating a Deed of Information on Inheritance Rights. In this research, we will discuss the urgency of creating a Deed of Inheritance Testimony and a Deed of Heirs' Statement and what consequences will occur to the Notary if they do not make these two deeds. The research method in this research is normative juridical, namely research using library materials or secondary data. This data will be analyzed qualitatively so that it will produce analytical descriptive data. Based on the research, it was found that the Deed of Inheritance Testimony and the Deed of Heirs' Statement have an important role in growing the Notary's confidence when creating a Deed of Information on Inheritance Rights and as security for the Notary himself because they can be used as evidence when there are problems related to heirs. As a result of not creating these two deeds, there are no sanctions given, but it can put the notary's position and position at risk of being exposed to problems from the Deed of Information on Inheritance Rights that has been made. KEYWORDS: Deed of Inheritance Testimony, Deed of Heirs' Statement, and Notary

Professional regulation in the digital era: A qualitative case study of three professions in Ontario, Canada.

Technology is transforming service delivery and practice in many regulated professions, altering required skills, scopes of practice, and the organization of professional work. Professional regulators face considerable pressure to facilitate technology-enabled work while adapting to digital changes in their practices and procedures. However, our understanding of how regulators are responding to technology-driven risks and the impact of technology on regulatory policy is limited. To examine the impact of technology and digitalization on regulation, we conducted an exploratory case study of the regulatory bodies for nursing, law, and social work in Ontario, Canada. Data were collected over two phases. First, we collected documents from the regulators' websites and regulatory consortiums. Second, we conducted key informant interviews with two representatives from each regulator. Data were thematically analyzed to explore the impact of technological change on regulatory activities and policies and to compare how regulatory structure and field shape this impact. Five themes were identified in our analysis: balancing efficiency potential with risks of certain technological advances; the potential for improving regulation through data analytics; considering how to regulate a technologically competent workforce; recalibrating pandemic emergency measures involving technology; and contemplating the future of technology on regulatory policy and practice. Regulators face ongoing challenges with providing equity-based approaches to regulating virtual practice, ensuring practitioners are technologically competent, and leveraging regulatory data to inform decision-making. Policymakers and regulators across Canada and internationally should prioritize risk-balanced policies, guidelines, and practice standards to support professional practice in the digital era.

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Friction in the Netflix machine: How screen workers interact with streaming data

Data-driven streamers like Netflix and Amazon Prime Video have expanded into the European screen landscape with a significant appetite for locally produced content. These players leverage advanced data analytics to gain deep customer insights, but they prefer to keep a lid on their algorithmic operations. This article examines how screen workers interact with streaming data despite widespread secrecy. Drawing on interviews and an interface ethnography, I explore the ways these workers access, sense, generate and resist streaming data throughout their creative process. As such, the article provides a framework for understanding the subtle and sometimes contradictory ways that screen workers engage with such data practices. I also demonstrate how researchers can circumvent and lower barriers to access in an industry marked by data secrecy. As a result, this article contributes to discussions about the datafication of cultural production, and it does so with novel insights from the European screen context.

Leveraging Distributed Systems for Fault-Tolerant Cloud Computing: A Review of Strategies and Frameworks

Ensuring system availability and reliability is crucial in the quickly developing field of cloud computing. The importance of fault tolerance in cloud infrastructure systems grows as organizations become more reliant on it to support their critical operations. The purpose of this article is to investigate the intricate realm of cloud computing and distributed systems. Specifically, the paper will investigate the numerous forms of cloud computing, fault tolerance methods, and frameworks that enable cloud services to be robust and durable. Cloud computing has transformed the way in which organizations and individuals access and administer computing resources. The paper discusses several deployment options, including public, private, hybrid, and multi-cloud environments, which provide organizations with the advantages of flexibility, scalability, and cost-effectiveness. The inherent flexibility of cloud computing renders it well-suited for a diverse range of applications, spanning from the hosting of websites to the execution of intricate data analytics processes. Generally, cloud computing encounters substantial obstacles, including the need of maintaining uninterrupted service in the face of hardware failures, network outages, or software errors, despite its tremendous benefits. The critical importance of fault tolerance in this particular situation cannot be overstated, as it plays a pivotal role in maintaining the dependability and availability of the system. The primary objective of this study is to examine the utilization of distributed systems as a means to augment fault tolerance within the realm of cloud computing and distributed systems. Distributed systems offer an optimal approach for addressing difficulties related to fault tolerance, owing to its intrinsic capability to divide workloads and data over several nodes. This approach utilizes redundancy, replication, and the ability to recover seamlessly from disturbances, hence enhancing the resilience and resource efficiency of cloud services. This research reviews novel techniques and frameworks that utilize distributed systems to create fault-tolerant cloud computing architectures, emphasizing their substantial influence on the cloud computing domain. In conclusion, this research report includes a comparative analysis table that encompasses twenty preceding works.

Survey on Optimal Crop Prediction using Soil and Weather Analysis

This survey paper provides a comprehensive overview of methodologies, technologies, and approaches for analyzing soil environments to optimize crop production. It covers traditional soil sampling, advanced techniques like remote sensing, and real-time monitoring using sensor networks. The integration of sensor data with IoT and machine learning for decision support is explored. Soil health assessment and site-specific management strategies enabled by precision agriculture are emphasized. Challenges such as data integration and sensor accuracy are addressed, along with future research directions including low-cost sensing solutions and improved data analytics. Overall, the paper serves as a valuable resource for optimizing crop production sustainably, contributing to efficient agricultural practices in a changing climate