Data Analysis Tools Research Articles

Big Data Analytics: Future of Database

Abstract: Big data analytics is a revolutionary method for processing massive and complicated datasets, analysing them, and drawing important conclusions from them. Big data's introduction has caused a paradigm change in data management and analysis, giving businesses previously unheard-of opportunity to gain insightful information and make informed decisions. This study explores how big data analytics affects how well organization’s function, concentrating on the methods and tools applied across a range of sectors. We undertook an empirical analysis of case studies from businesses in the retail, healthcare, and financial sectors after conducting a thorough assessment of the literature. According to our research, using big data analytics leads to better decision-making, increased operational effectiveness, and a stronger competitive advantage. Data quality, analytics tools, and corporate culture are the main success determinants. However, issues with data privacy and the necessity for qualified data experts make the effective application of big data analytics difficult. This study adds to our understanding of big data analytics' revolutionary potential and offers useful information for practitioners and decision-makers who want to use data to their advantage.

Sales Data Analysis Using BI and WEKA

In today's competitive business landscape, organizations rely heavily on data-driven decision-making to gain a competitive edge. Sales data analysis serves as a cornerstone in this endeavor, offering invaluable insights into consumer behavior, market trends, and the overall health of a business. This abstract highlights the significance of sales data analysis and its role in informing strategic business decisions. The abstract begins by outlining the importance of sales data analysis in understanding customer preferences, identifying emerging market trends, and optimizing sales strategies. It emphasizes the need for businesses to harness the power of data analytics tools and techniques to extract actionable insights from vast volumes of sales data. Furthermore, the abstract discusses the various dimensions of sales data analysis, including sales performance metrics, customer segmentation, and product performance analysis. It underscores the importance of leveraging advanced analytical methods such as predictive analytics, machine learning, and data visualization to unlock hidden patterns and correlations within sales data. Moreover, the abstract explores the practical applications of sales data analysis across different industries, ranging from retail and e-commerce to manufacturing and finance. It showcases real-world examples of how organizations have successfully utilized sales data analysis to enhance revenue generation, streamline operations, and improve customer satisfaction. The performance of the forecasting models is evaluated using appropriate metrics and validated against holdout datasets to ensure robustness and reliability. Model selection and tuning are conducted to optimize forecasting accuracy and mitigate potential biases. The forecasting is done using weka tool. Keywords – Insights, Visualization, Analytics, Sales trends.

State Dependent Delay Maps: Numerical Algorithms and Dynamics of Projections

This work concerns the dynamics of a certain class of delay differential equations (DDEs) which we refer to as state dependent delay maps. These maps are generated by delay differential equations where the derivative of the current state depends only on delayed variables, and not on the un-delayed state. However, we allow that the delay is itself a function of the state variable. A delay map with constant delays can be rewritten explicitly as a discrete time dynamical system on an appropriate function space, and a delay map with small state dependent terms can be viewed as a “non-autonomous” perturbation. We develop a fixed point formulation for the Cauchy problem of such perturbations, and under appropriate assumptions obtain the existence of forward iterates of the map. The proof is constructive and leads to numerical procedures which we implement for illustrative examples, including the cubic Ikeda and Mackey-Glass systems with constant and state-dependent delays. After proving a local convergence result for the method, we study more qualitative/global convergence issues using data analytic tools for time series analysis (dimension and topological measures derived from persistent homology). Using these tools we quantify the convergence of the dynamics in the finite dimensional projections to the dynamics of the infinite dimensional system.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Learning Aligned Vertex Convolutional Networks for Graph Classification.

Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.

Abstract 6475: Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health

Abstract Pancreatic cancer is the third leading cause of cancer-related death in the United States. Current therapeutic options offer a dismal overall survival with the 5-year survival at just ~12%. Analysis of the clinical and molecular underpinnings of pancreatic cancer is critical to developing both early detection methodologies as well as novel therapeutic options. The aggressiveness and deadly nature of this disease warranted the development of a data repository and analysis system dedicated to pancreatic cancer data. The Pancreatic Cancer Action Network’s (PanCAN) SPARK platform is a cloud-based data and analytics platform, powered by Velsera, that integrates real-world patient health data from PanCAN research initiatives and accelerates research by making pancreatic cancer data easier to access and use. Encompassing clinical, imaging, and genomics data from over 600 patients with pancreatic cancer within PanCAN’s Know Your Tumor ® (KYT) precision medicine service, the SPARK platform connects with petabytes of publicly available cancer data via the Cancer Genomics Cloud (CGC), also powered by Velsera. The CGC is part of NCI’s Cancer Research Data Commons (CRDC), a cloud-based data science infrastructure that connects data with analytics tools to allow researchers to share, integrate, analyze, visualize, and drive scientific discovery. Here, we demonstrate the application of these datasets by providing a case study demonstrating how to combine and enrich data to accelerate pancreatic cancer research. Currently, the genomic and proteomic data available on CRDC amounts to 402 and 304 cases of pancreatic tumor samples, respectively. We will use the capabilities of the SPARK and CGC platforms, which provide ready-to-use tools for multi-omics analysis that require no coding knowledge. Using the KYT and CRDC open-access pancreatic cancer data, we aim to demonstrate how to perform integrated analysis of data from diverse scientific domains, and share with collaborators all in one space, streamlining and increasing the potential for new scientific discoveries. Further expansion of the PanCAN and CGC datasets will undoubtedly provide a more comprehensive understanding of pancreatic cancer tumor biology. SPARK and CGC’s cloud based computation infrastructure, along with numerous available cancer datasets and easy-to-use multi-omics data processing workflows and data analytic tools will be instrumental in this process. Citation Format: Kawther Abdilleh, Zelia Worman, Rowan Beck, Cera Fisher, Divya Sain, Jack DiGiovanna, Lynn Matrisian, Sudheer Doss, Brandi Davis-Dusenbery. Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6475.

Monitoring the psychological component of higher education quality using ICT tools

This study examines the use of information and communication technologies (ICT) to monitor the psychological component of higher education quality. A literature review covers the concepts of higher education quality and its monitoring, the psychological factors involved, and the potential of ICT tools, including Google services. The study details a monitoring procedure using Google Forms to assess indicators like relationships between participants, satisfaction levels, and organisational climate. The computer program “Personnel – Ψ” is also presented as a tool for comprehensive psychological diagnostics of teachers. The advantages of using ICT tools for efficient data collection, analysis and management are discussed. The results demonstrate the feasibility and effectiveness of using ICT to improve monitoring of the psychological aspects of educational quality and make evidence-based decisions to enhance the educational environment. The use of user-friendly tools like Google Forms facilitates regular assessment of quality indicators and aids institutions in ensuring participant satisfaction and optimal conditions for learning and growth.

Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking

OCCUPATIONAL APPLICATIONS Musculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue. In most cases, the fatigue indicator is a functional variable, meaning that the fatigue score or measurement is a curve captured over time, which could be modeled as a function. In this study, we demonstrate that functional data analysis tools, such as functional analysis of variance (FANOVA), prove more effective than traditional methods in specifying how task factors contribute to the development of fatigue in the upper extremities. Furthermore, since there are inherent differences among workers that could affect their fatigue development process, the data heterogeneity could be tackled by employing clustering methods.

Integrated Service Architecture to Promote the Circular Economy in Agriculture 4.0

Innovation has been the transforming tool of precision agriculture as a response to population growth and the demand for more food with quality, less waste, food security, and sustainable management of environmental resources. The challenges are to increase the productivity of cultivated areas, both for current and future areas, to manage the use of potable water, scarce in many regions, to keep the soil fertile, and to reduce waste through reuse, optimization, resource sharing, and operational and strategic management based on accurate information of planting, harvesting, and management of environmental conditions, which are also objectives of the Circular Economy. Therefore, using Industry 4.0 technologies in agriculture becomes fundamental to facing such challenges. This paper presents a systematic literature review on Industry 4.0 technologies adopted in agriculture for sustainable development, considering environmental, economic, and social benefits. The research pointed to the use of IoT in irrigation control systems by sending automatic commands, monitoring soil and weather conditions, in the use of machinery with some automation features and in cloud data storage systems, and with the use of Big Data analytical tools, with access by mobile devices, these uses contribute to operational and strategic decision making in the management of planting and harvesting. However, the literature review did not find a technological architecture for Integrated Services in Agriculture 4.0. Thus, this paper proposes a Service Architecture that enables the promotion of a Circular Economy in Agriculture 4.0. The contribution of this article to the theory is in the expansion of knowledge of the use of technologies in Agriculture 4.0. In terms of practice, this article provides an Integrated Service Architecture so that new products can be developed for Agriculture 4.0 and thus contribute to society in reducing food insecurity, generating environmental, economic, and social benefits, and promoting the Circular Economy in Agriculture 4.0.

Assessment of Determinant Sources of Plastics and Microplastics Pollution in Inland Waterways Navigation in Lower River Niger

This study was aimed at assessing the impact of plastics and micro plastics pollution in Nigerian inland waterways operations. The main aim of this study is to provide a baseline assessment of plastic pollution in the River Niger, along with recommendations for further research. Using a survey and appropriate data analysis tools, the study investigated the determinant sources of plastic pollution in the River Niger, as well as the most significant impact and preferred means of plastic pollution and regulation. The main findings indicate that disposal of plastics is the determinant source of plastics and micro plastics in the River Niger. The most significant impact of plastic pollution was identified as reduction in the accessibility of certain areas of the river. Behavioral instrument is indicated to be the most preferred means of plastic pollution control and regulation. Hence, there is an urgent need for improved awareness among local communities. This can be achieved through education programs, which will help to reduce the use of single-use plastics while also reducing littering in these areas. The paper also recommends that more research be conducted on how these pollutants affect human health and ecosystems.

Behavioral intentions in embracing cloud-based accounting information systems

In facing the digital era and a more competitive environment, every organization is required to increasingly implement information systems that can facilitate various organizational needs. Cloud Based Accounting Information Systems (CBAIS), as part of information systems (IS), have also gained attention of organizations. This research was conducted to obtain empirical evidence of the interest in using CBAIS in various medium-sized companies in Indonesia measured using Institutional Theory. The data analysis tool used was SEM- PLS. The respondents in this study were accounting and finance departments employees working for logistics companies that still use conventional accounting information systems (non-cloud-based). A total of 89 responses were collected from 5 logistics companies in Indonesia. The research results indicated that mimetic pressure positively influenced the interest in using CBAIS, coercive pressure does not positively influence the interest in using CBAIS, and normative pressure positively influenced the interest in using CBAIS. An interesting aspect of this study is the absence of regulation or even demand in Indonesia that obligates the use of CBAIS. Hence, the use of CBAIS has not become a commonly adopted best practice in logistics companies.

Emerging Data Processing Methods for Single‐Entity Electrochemistry

AbstractSingle‐entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single‐entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five‐year advances in signal processing techniques for single‐entity electrochemistry and highlight their importance in obtaining high‐quality data and extracting effective features from electrochemical signals, which are generally applicable in single‐entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single‐molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single‐entity electrochemical measurements would revolutionize the field of single‐entity analysis, leading to new fundamental discoveries.

Emerging Data Processing Methods for Single-Entity Electrochemistry.

Single-entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single-entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five-year advances in signal processing techniques for single-entity electrochemistry and highlight their importance in obtaining high-quality data and extracting effective features from electrochemical signals, which are generally applicable in single-entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single-molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single-entity electrochemical measurements would revolutionize the field of single-entity analysis, leading to new fundamental discoveries.

A Novel Approach for Mixed-Methods Research Using Large Language Models: A Report Using Patients' Perspectives on Barriers to Arthroplasty.

Mixed-methods research is valuable in health care to gain insights into patient perceptions. However, analyzing textual data from interviews can be time-consuming and require multiple analysts for investigator triangulation. This study aims to explore a novel approach to investigator triangulation in mixed-methods research by employing a large language model (LLM) for analyzing data from patient interviews. This study compared the thematic analysis and survey generation performed by human investigators and ChatGPT-4, which uses GPT-4 as its backbone model, using data from an existing study that explored patient perceptions of barriers to arthroplasty. The human- and ChatGPT-4-generated themes and surveys were compared and evaluated based on their representation of salient themes from a predetermined topic guide. ChatGPT-4 generated analogous dominant themes and a comprehensive corresponding survey as the human investigators but in significantly less time. The survey questions generated by ChatGPT-4 were less precise than those developed by human investigators. The mixed-methods flowchart proposes integrating LLMs and human investigators as a supplementary tool for the preliminary thematic analysis of qualitative data and survey generation. By utilizing a combination of LLMs and human investigators through investigator triangulation, researchers may be able to conduct more efficient mixed-methods research to better understand patient perspectives. Ethical and qualitative implications of using LLMs should be considered.

Individual Learning Plan (ILP) Effectiveness: EFL Islamic Students’ Writing Skill Enhancement

This study investigates the efficacy of implementing Individual Learning Plan (ILP) to enhance the writing skills of eleventh-grade students at , (Madrasah Aliyah Swasta) Guppi Mamuju, an Islamic private senior high school in West Sulawesi, Indonesia. Conducted through a pre-experimental approach, the research population comprised eleventh-grade students from MAS Guppi Mamuju, totaling one class. Utilizing total sampling, one experimental class of eight students was selected. The study employed a writing test as the primary research instrument, assessing various aspects including content, organization, vocabulary, language use, and mechanics using Jacobs' scoring scale. Data analysis was conducted using Microsoft Excel's Automatic Data Analysis tool. The results indicated a notable improvement in students' writing ability following the implementation of ILP, as evidenced by the significant increase in mean scores from the pre-test (57.00) to the post-test (70.63), with a range of 13.37 points. Statistical analysis using the t-test supported the rejection of the Null Hypothesis (H0) and acceptance of the Alternative Hypothesis (H1). In conclusion, the integration of ILP effectively enhanced the writing skills of eleventh-grade students at MAS Guppi Mamuju.

Design and implementation of breast cancer prediction system based on machine learning

With the increasing incidence of breast cancer worldwide, early diagnosis and treatment of breast cancer has become the key to improving patient survival and quality of life. As a powerful data analysis tool, machine learning is increasingly widely used in the medical field, especially in disease prediction and assisted diagnosis. This paper aims to design and implement a machine learning-based breast cancer prediction system to improve the early diagnosis rate of breast cancer and reduce medical costs. Through an in-depth analysis of the global incidence of breast cancer and the current application of machine learning in the medical field, this study clarified the importance of breast cancer prediction and the problems existing in the existing prediction system. This paper further discusses the theoretical basis of machine learning in breast cancer detection, evaluates the advantages and disadvantages of commonly used machine learning algorithms, and reviews the latest research progress in this field at home and abroad. In the part of system design and implementation, the architecture design, data flow and processing process of the prediction system, as well as the method of data preprocessing and feature selection are introduced in detail. In addition, this paper also constructs a machine learning model suitable for breast cancer prediction, and carries out systematic implementation and testing through the actual development environment. In the discussion section, the applicability of machine learning model in breast cancer prediction is analyzed, the causes of model inefficiency are discussed, and the corresponding solutions are proposed. Finally, the paper summarizes the content of the full text, points out the limitations of the research, and puts forward the direction of future research. The results of this study not only provide a new technical means for the early diagnosis of breast cancer, but also provide valuable experience for the application of machine learning in the medical field.

Surface enhanced Raman spectroscopy for the characterization of the metabolites of the biodesulfurization of dibenzothiophene carried out by Tsukamurella paurometabola

Large amount of sulphur is released by the combustion of fossil fuels in the form of SoX which affects human health and leads to acid rain. To overcome this issue, it is essential to eliminate sulphur moieties from heterocyclic organo-sulphur compounds like Dibenzothiophene (DBT) present in the petrol. In this study Surface enhanced Raman scattering (SERS) spectroscopy is used to analyze the desulfurizing activity of Tsukamurella paurometabola bacterial strain. The most prominent SERS peaks observed at 791, 837, 944 and 1032 cm−1, associated to C–S stretching, are solely observed in dibenzothiophene and its metabolite-I (DBTS) but absent in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Moreover, the SERS peaks observed at 974 (characteristic peak of benzene ring) and 1015 cm−1 is associated to C–C ring breathing while 1642 and 1655 cm−1 assigned to CC bonds of aromatic ring. These peaks are only observed in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Notably, these peaks are absent in the Dibenzothiophene and its metabolite-I which indicate that aromatic ring is carrying sulfur in this fraction. Moreover, multivariate data analytical tools like principal component analysis (PCA) and PCA-loadings are applied to further differentiate between dibenzothiophene and its metabolites that are Dibenzothiophene sulphone (metabolite-I) and 2-Hydroxybiphenyl (metabolite-II).

Evaluating Students’ User Experience on Student Management Information Systems

Studies evaluating students’ UX of applications that will influence their continuance use of educational systems in Higher Educational Institutions (HEIs) have not been sufficiently addressed in the African region, specifically in Ghana. Thus, conducting a study on students’ UX of systems in HEIs will enhance students’ interest in continuing to use such systems. Therefore, this study examines students’ user experience (UX) and how it impacts their continued use of the Student Management Information System (SMIS). The study proposed a research model by integrating user experience questionnaire (UEQ) constructs with continuance intention to use. The study adopted an online questionnaire to collect data from 415 students at Koforidua Technical University (KTU). The partial least square-structural equation model (PLS-SEM) method was used to evaluate the proposed model’s reliability, validity, and relationship among the constructs. The UEQ data analysis tool was used in analysing the data. The study’s findings showed that attractiveness, perspicuity, efficiency, stimulation, and novelty significantly influenced students’ continuance intention to use the SMIS. However, dependability did not significantly affect students’ continuance intention to use the SMIS. Also, the study’s findings on the benchmark results showed that attractiveness, perspicuity, and stimulation were categorized as good, while dependability, efficiency, and novelty were categorized as excellent. These findings offer valuable insights for UX designers and developers aiming to create engaging and intuitive SMIS solutions. Also, it will provide students feedback on system UX that can be incorporated into future release updates. Furthermore, this research contributes significantly to the understanding of student UX with SMIS in developing country HEIs and its impact on continued usage.

The Dynamics of School Environment and the Impact of Parental Attention on the Academic Achievement of Students at SMA Negeri 1 Ketapang Sampang

In the current era of globalization, students are faced with demands to consider the factors that influence the achievement of their learning outcomes. This research aims to reveal the role of an effective school environment and parental attention in improving the quality of student education. Quantitative methods were used in this research. In this research, this approach was chosen to provide a more measurable understanding of the role of the school environment and parental attention on student learning outcomes. The research population includes all students of SMA Negeri 1 Ketapang Sampang so that the research results can be more generally applied to this group. The use of multiple linear regression as a data analysis tool allows researchers to understand the extent to which certain variables, such as school environment and parental attention, contribute to variability in student learning outcomes. The findings show that the school environment has a fairly large role in determining student learning outcomes. Apart from that, emotional involvement and parental support also play a role in developing student academic achievement. With a school environment that supports learning and positive contributions from parents, it is hoped that student learning outcomes will improve.

Analyzing Depreciation: A Financial Accounting Data Analytics and Visualization Case

ABSTRACT This case uses a financial accounting context to help instructors incorporate data analytics in the accounting curriculum. The case’s flexible design allows it to be used in a data analytics or financial accounting course for instructors with little to no data analytics experience. For an analytics course, students extract, transform, and load fixed asset and depreciation data from a company’s five sites. Students will then validate the accuracy and completeness of the data by creating visualizations. The case can be accomplished using any data analytics tool; Teaching Notes are presented using Power BI. The case and Teaching Notes include modifications for a financial accounting course where students are given data visualizations to identify depreciation anomalies (where no analytics software is required). An extension for audit courses is also discussed. The case serves to develop students’ analytics skills while simultaneously reinforcing their understanding of depreciation concepts.