Process Of Analyzing Data Research Articles

Integrating Process Re-Engineering Models in Cement Production to Improve Energy Efficiency

The demand for cement has significantly increased, growing by 8% in the year 2022 and by a further 12% in 2023. It is highly anticipated that this trend will continue, and it will result in significant growth by 2030. However, cement production is highly energy-intensive, with 70 to 80% of the total energy consumed during the clinker formation, which is the main cement production process. Minimising energy losses requires a radical approach that includes optimising the performance of the kilns and significantly improving their energy efficiency. One of the most efficient approaches to optimise the performance of the kilns and reduce energy losses is by integrating process re-engineering models, which leverage process data analytics, machine learning, and computational methods. This study employed a model-based integration approach to improve energy efficiency during clinker formation. Energy consumption data were collected from two semi-automated cement production plants. The data were analysed using a regression model in Minitab (Minitab 21.1.0) statistical software. The analysis resulted in a linear energy consumption equation that links energy consumption to both production and energy loss. Dynamic simulations and modelling using Simulink in MATLAB were performed based on a proportional–integral–derivative (PID)-controlled system. The dynamic behaviour of the model was evaluated using data from Plant A and validated with data from Plant B. The energy efficiency equation was established as a mathematical model that explains energy improvements based on incorporating parameters for the cement kiln system and disturbances from the environment.

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.

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.

Cross-industry frameworks for business process reengineering: Conceptual models and practical executions

Cross-industry frameworks for business process reengineering (BPR) have emerged as pivotal tools in enhancing organizational efficiency, agility, and competitiveness across diverse sectors. These frameworks amalgamate conceptual models with practical executions to streamline operations, optimize resource allocation, and foster innovation. This review delves into the essence of cross-industry frameworks for BPR, elucidating their conceptual underpinnings and real-world applications. At the core of cross-industry frameworks lies the recognition that business processes transcend sector boundaries, encompassing common elements such as customer interactions, supply chain management, and operational workflows. Conceptual models form the foundational framework by articulating key principles, methodologies, and best practices for BPR initiatives. One prominent conceptual model is the Business Process Reengineering (BPR) methodology, which advocates for radical redesign rather than incremental improvement of processes. This approach emphasizes fundamental questioning of existing practices, aiming to achieve dramatic enhancements in efficiency, quality, and customer satisfaction. Another influential model is the Capability Maturity Model Integration (CMMI), which provides a roadmap for organizations to systematically improve their processes by advancing through defined maturity levels. Practical executions of cross-industry frameworks entail the translation of conceptual models into actionable strategies tailored to specific organizational contexts. This involves a multifaceted approach encompassing process analysis, stakeholder engagement, technology integration, and change management. Process analysis entails mapping current workflows, identifying bottlenecks, and pinpointing areas for optimization. Stakeholder engagement is crucial for garnering buy-in and fostering a culture of continuous improvement. Technology integration plays a pivotal role in modern BPR initiatives, leveraging digital tools such as business process management (BPM) software, data analytics, and robotic process automation (RPA) to streamline operations and enhance decision-making. Moreover, emerging technologies like artificial intelligence (AI) and blockchain offer new avenues for process innovation and optimization. Change management constitutes a critical component of practical executions, as BPR initiatives often entail significant organizational transformation. Effective change management involves communication, training, and leadership support to mitigate resistance and facilitate smooth transition. Real-world applications exemplify the effectiveness of cross-industry frameworks in driving tangible business outcomes. Case studies spanning diverse sectors, including manufacturing, healthcare, finance, and retail, demonstrate how organizations have leveraged BPR to achieve breakthrough improvements in efficiency, cost reduction, and customer satisfaction. In the manufacturing sector, for instance, companies have adopted lean manufacturing principles to streamline production processes, minimize waste, and enhance flexibility. In healthcare, BPR initiatives have led to streamlined patient care pathways, reduced administrative burdens, and improved clinical outcomes. Financial institutions have leveraged BPR to optimize loan approval processes, mitigate risk, and enhance regulatory compliance. Similarly, retailers have utilized BPR to optimize supply chain management, enhance omnichannel capabilities, and personalize customer experiences. Cross-industry frameworks for business process reengineering represent a potent paradigm for organizational transformation in today's dynamic business landscape. By integrating conceptual models with practical executions, these frameworks empower organizations to enhance operational efficiency, drive innovation, and maintain competitive advantage across diverse sectors.

Differentiated instruction for English learners: Teachers' understanding and practices

AbstractThe increase in the English learner (EL) student population in the United States and these students' placement in U.S. public schools have created high priority demands to address their language and learning needs. General education teachers, however, are often unprepared or underprepared to work with ELs, with limited knowledge and expertise to integrate differentiated instruction (DI) for them. This study examined two elementary teachers' understanding of differentiated instructional practices for EL students in a mainstream setting. Employing qualitative methods and following inductive data analytic processes, the study examined what these elementary teachers say facilitates DI for ELs. The researcher used semi‐structured individual interviews, a joint interview, and photo elicitation as the primary sources of data collection, and documents such as teachers' differentiated lesson plans and statements of teaching philosophy as secondary data sources. Findings revealed that a caring relationship between the teacher and ELs was essential and played a key role in student learning. By building caring relationships, the teachers learned about their EL students' needs and their interests and used that knowledge to implement DI for these students. Furthermore, teachers' proficiency in a language other than English and their metalinguistic awareness to bridge between one language and the other guided the teachers' differentiated instructional practices for their ELs.

Bloom filter empowered smart storage/access in IoMT [edge‐fog‐cloud] hierarchy for health‐care data ingestion

AbstractToday's era of smart technologies has shifted the paradigm of how things work in almost every domain. This change has also affected the utmost need of human beings, that is, sustainability in healthcare. With the help of technologies like the Internet of medical things (IoMT) devices and cloud‐based applications, doctors can now monitor and diagnoses their patients remotely. In the data analytics process for health‐care data, more precision and challenges are present. Data must be transferred from a lower layer to final storage (cloud‐based) during data ingestion so that subsequent operations such as mining, accessing, and streaming can be performed on these data. This article addresses some of the important issues to maintain sustainability in healthcare like authentication, smart scheduling of devices, the removal of redundant data on the final layer, and improving access times for stored data. Bloom filter (a probabilistic data structure)‐based solutions are proposed at different layers of IoMT (edge‐fog‐cloud). The comparative analysis of state‐of‐art techniques with the proposed has shown significant improvement over various metrics. The proposed framework has been evaluated experimentally on real health‐care datasets.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1.

The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition. The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test

Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.

Modeling and Statistical Analysis of Data Breach Problems in Python

The subject of the work is electronic medical record linkage threat analysis and modeling with the use of the submitted data breaches list published by the U.S. Department of Health and Human Services. Multipronged data analysis with the use of statistics utilities and data visualization has been conducted. The model forecasting the number of data breaches based on a time series mathematical model has also been built. The article reviews the tools and techniques used in data security analysis and presents practical examples of modeling and analysis that can be used in practice to improve data protection. It was shown how important it is to protect personal data, especially medical data, and what tools can be used in the educational process of data analytics for students to effect data analysis, trend assessment, and data prediction.

Big Data Analytics e governo local: considerações sobre um modelo de processos com foco em gestão

The paper analyzes the aspects that should be considered when proposing a model of Big Data Analytics (BDA) processes in local government with a focus on management. We sought to: carry out a systematic review of the literature to identify the main works aligned with the theme; analyze the trend of BDA use in local governments through the synthesis of publications; and propose, through the key points identified in the analyzed publications, a model of big data analytics processes in local government with a focus on management. The methodological trajectory is defined by a qualitative approach with an exploratory and descriptive objective. To answer the question that guides the conceptual fragment of the study, we chose to carry out a systematic review of the literature. Among the standardized procedures, the Prisma`s model was chosen. The sources of information were some of the main electronic databases in the fields of applied social sciences, focused on administration and public administration, including Web of Science, Scopus, Ebsco and Emerald. A promising trend was noticed in the use of BDA in local governments, with several application cases and a very clear emphasis on its still unexplored potential to offer or transform the relationship between local governments and their citizens. It is concluded that democratic processes can be substantially improved with data-oriented policies, but several factors need to be carefully analyzed and discussed so that the use of opportunities derived from BDA is effectively greater than their possible investment costs and implementation risks.

Exploring the use of focused ethnography in social work research: A scoping review

Focused ethnography (FE) is an emerging method for social work researchers who examine social justice issues within specific sub-cultures and service systems. FE methods may include researchers with background knowledge, specific research questions, and the use of intensive, short-term data collection methods in time-limited settings. Although relevant to applied studies in social work, FE methods remain underspecified. This scoping review examines the extent, variety, and characteristics of FE in social work research. The protocol follows the PRISMA Extension for Scoping Reviews Checklist and Explanation (PRISMA-ScR). We searched Social Science databases between 2018 and 2022. The critical appraisal of articles is guided by published FE methodology and American Psychological Association (APA) journal reporting standards for qualitative research. Researchers justified the use of FE in relation to their ethnographic goals, research populations, and specific questions. However, they were inconsistent in their discussion of the integration of theory and reflexive processes in research methods. While most studies used thematic analysis or coding of qualitative data, some lacked the recommended elements outlined in the journal reporting standards for qualitative research. In particular, they lacked transparency when discussing the impact of background knowledge and positionality on data analytic processes and findings. In this review, we discuss the strengths and limitations of FE for social work research and offer recommendations for methodological improvement. To enhance understanding and trustworthiness of reported findings, we recommend transparency in discussions of data analytic and reflexive processes, as well as uniform reporting in accordance with APA standards for qualitative research.

Technological Forensic Auditing and Financial Crime Detection in Nigeria: A Study of Selected Deposit Money Banks

Financial crime has been one of the current challenges faced by organizations especially deposit money banks. To ameliorate this ugly development, technology-enabled forensic auditing was introduced in addition to conventional auditing. This study examined the impact of technological forensic auditing on financial crime detection in Nigeria; a study of selected deposit money banks in Nigeria. The theories reviewed in the study are the strain theory, the theory of inspired confidence, and the credibility theory. The study adopted the survey design. The population of the study is made up of the accounting staff of five selected deposit money banks in Delta state namely; Zenith, First Bank, United Bank for Africa (UBA), Fidelity Bank, and Sterling Bank with a statistic of fifty-eight (58) staff/respondents. Data for the study were generated through the distribution of well-structured questionnaires to the corresponding respondents. Data was analyzed with the multiple linear regression method. The study found that robotic process automation, textual analysis, and data analytics contribute significantly to detecting financial crime in selected deposit money banks in Nigeria. It is therefore the recommendation of this study that forensic audit firms and deposit money banks should strategically employ the usage of software applications that can permit data analytics, textual analysis, and robotic process automation to redeem the credibility image of the forensic auditors’ report.

Smart process analytics for the end-to-end batch manufacturing of monoclonal antibodies

For many modern biopharmaceutical processes, manufacturers develop data-driven models using data analytics/machine learning (DA/ML) methods. The challenge is how to select the best methods for a specific dataset to construct the most accurate and reliable model. This article describes the application of smart process data analytics software to industrial end-to-end biomanufacturing datasets for monoclonal antibody production to automate the determination of the best DA/ML tools for model construction and process understanding. The application demonstrates that smart process data analytics software captures product- and process-specific characteristics for two different monoclonal antibody productions. This study provides tools that can be widely applied to biomanufacturing processes for root cause analysis, prediction, and control.

Robotic Process Automation (RPA) in Human Resource Functions

Automation has become a game-changer in the field of Human Resource (HR), revolutionizing traditional HR operations and transforming the way organizations manage their workforce. This study explores the concept of automation in HR and its impact on various HR functions including recruitment, onboarding, employee data management, payroll administration, performance management and employee engagement. The study delves into the benefits of HR automation such as increased efficiency, improved accuracy, reduced manual errors and enhanced employee experiences. It highlights the different technologies and tools used for HR automation including Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML) and data analytics. Overall, automation in HR operations brings numerous benefits to organizations including improved efficiency, accuracy, compliance and employee satisfaction. HR professionals can focus on strategic initiatives and value-added tasks, resulting in better talent management and organizational success in the dynamic business landscape.

Semantics-Aware Document Retrieval for Government Administrative Data

The process of data analytics on large-scale government administrative data — that belong to various domains like education, transport, energy, and health — can be enhanced by retrieving pertinent documents from diverse data sources. Without a supporting framework of metadata, big data analytics can be daunting. Even though statistical algorithms can perform extensive analyses on a variety of data with little help from metadata, applying these techniques to heterogeneous data may not always result in reliable findings. Recently, semantics-aware (or semantic search) search techniques received much attention as they utilize implicit knowledge to enhance the search. Similarly, traditional search engines rely on the inherent linkages within the underlying data model to improve their search quality. In the case of general-purpose information retrieval systems, to gather information from the internet (open access data) or to access open government administrative data, a domain agnostic ontology shall be employed to supply background knowledge. This paper draws on research undertaken by the authors at IIIT Bangalore Center for Open Data Research (CODR) in developing a semantics-aware data lake framework to host and analyze government administrative data. In this study, we present an ontology-based document retrieval solution where an ontology serves as an intermediary to close the gap between what the user seeks and what the search retrieves. Although our study settings are based on the Government of Karnataka (GoK, India), we believe the findings have wider resonance. Our experimental results based on agricultural data from the GoK look promising.

RETRACTED: Obodoekwe et al. Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy. Electronics 2022, 11, 2128

The journal retracts the article entitled “Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy” [...]

Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data.

In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework's ability to generate various analytics from clinical data represented in the FHIR resources.

Big data analytics and innovation in e-commerce: current insights and future directions

Big data analytics (BDA), as a new innovation tool, played an important role in helping businesses to survive and thrive during great crises and mega disruptions like COVID-19 by transitioning to and scaling e-commerce. Accordingly, the main purpose of the current research was to have a meaningful comprehensive overview of BDA and innovation in e-commerce research published in journals indexed by the Scopus database. In order to describe, explore, and analyze the evolution of publication (co-citation, co-authorship, bibliographical coupling, etc.), the bibliometric method has been utilized to analyze 541 documents from the international Scopus database by using different programs such as VOSviewer and Rstudio. The results of this paper show that many researchers in the e-commerce area focused on and applied data analytical solutions to fight the COVID-19 disease and establish preventive actions against it in various innovative manners. In addition, BDA and innovation in e-commerce is an interdisciplinary research field that could be explored from different perspectives and approaches, such as technology, business, commerce, finance, sociology, and economics. Moreover, the research findings are considered an invitation to those data analysts and innovators to contribute more to the body of the literature through high-impact industry-oriented research which can improve the adoption process of big data analytics and innovation in organizations. Finally, this study proposes future research agenda and guidelines suggested to be explored further.

Analisis Penerapan Pendekatan Audit Jarak Jauh Dampak dari Pandemi Covid-19 (Studi Kasus : Inspektorat Jenderal Kementerian Kesehatan RI)

The Covid-19 pandemic has greatly impacted many aspects, including how the audit works at the Inspectorate General of the Indonesian Ministry of Health. With various kinds of restrictions ranging from travel restrictions and interaction restrictions, it becomes an obstacle for internal audit activities to be carried out directly. Therefore the Inspectorate General needs alternative procedures or ways of conducting audit activities, namely by using a remote audit approach. This study aims to obtain references to develop guidelines for implementing the remote audit approach during a pandemic and under normal conditions while still paying attention to audit quality. In this study analyze the resources owned and alternative ways of conducting audits using a remote audit approach. The research method used is descriptive qualitative with data sources derived from interviews with employees and officials within the Inspectorate. The results of the study show that there are different ways at each stage of the audit that require information technology assistance and the development of data analytics processes in its implementation as well as several things that need to be considered in developing a strategy for implementing the remote audit approach, spesifically the readiness of human resources, adequate information technology, budget available, as well as compensation that can be given to the auditor in terms of carrying out this remote audit.

Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain penalties to search for the change points. The Bayesian approach is another, which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point. The paper investigates how the two approaches for change point detection can be applied to process data analytics. In addition, a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data. The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data. The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions, which will be useful for other machine learning tasks.