Analytical Domain Research Articles

Evidence-based knowledge management: a topic modeling analysis of research on knowledge management and analytics

Purpose Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics. Design/methodology/approach A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature. Findings The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media. Originality/value Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

PHILOSOPHY IN BUSINESS ANALYTICS: A REVIEW OF SUSTAINABLE AND ETHICAL APPROACHES

The integration of philosophy into the realm of business analytics is a nuanced and evolving discourse that transcends the technicalities of data analysis. This paper encapsulates a comprehensive review that delves into the ethical and sustainable dimensions of business analytics, probing the philosophical underpinnings that shape the responsible use of data in corporate decision-making. The review examines the moral considerations inherent in business analytics, investigating the ethical implications of data collection, analysis, and utilization. It contemplates the evolving landscape of data privacy, transparency, and the ethical responsibilities of businesses in the era of pervasive data-driven decision-making. Furthermore, the paper explores sustainability in business analytics, acknowledging the environmental impact of data centers, energy consumption, and the life cycle of digital technologies. It evaluates the philosophical principles guiding sustainable practices in the field, aiming to harmonize the pursuit of data-driven insights with ecological responsibility. In essence, this study outlines a comprehensive exploration of how philosophy shapes and guides sustainable and ethical approaches in the domain of business analytics. It sets the stage for a critical examination of the evolving intersection between data-driven decision-making and the ethical principles that underpin responsible corporate practices.
 Keywords: Philosophy, Business Analytics, Sustainable, Ethical Approaches, Review.

Comparing Microbial Source Tracking Methods for Precision and Reliability

Microbial source tracking is a valuable tool in forensic science, specifically in the analysis of trace evidence. Numerous tools have been developed to estimate the proportion of different contamination sources within a mixture. In this study, we evaluate the accuracy of various source tracking methods using datasets from microbiome studies. In addition to assessing source tracking methods, we also incorporate two widely used cell type deconvolution methods, namely EPIC and PREDE, which are designed to identify missing cell types in a given dataset. Furthermore, we investigate the effectiveness of combined methods by integrating RAD, a source tracking method aimed at filtering out unimportant sources, with either EPIC or PREDE for enhanced accuracy in both source tracking and cell type deconvolution. This research represents a pioneering effort to examine the application of cell type deconvolution methods in source tracking and vice versa. Particularly noteworthy is our focus on scenarios involving missing sources or cell types in the reference data, shedding light on the intricate interplay between these two analytical domains.

Video Lessons for the Course Introduction to Computing through Online/Offline Mode (ICOM): Its Development and Evaluation

Conducting video lessons is a common trend in today’s new normal educational setup. Hence, it is crucial to initiate efforts to evaluate this strategy. This developmental research developed and evaluated an online/offline video lesson for the course Introduction to Computing. Selected via cluster sampling, twenty first-year students from a state university, four information technology experts, two curriculum experts, and one language teacher participated in the study. A researcher-made examination and an adapted evaluation form for video materials were utilized in the study. Results showed that the three least achieved computing skills were: (a) logical and critical thinking domains like explaining the different jobs, functions, professions, and careers in the field, identifying computing hardware and others; (b) technical domains like identifying input, output, processing, and storage devices and others; and (c) analytical domains such as text alignment, addition of line breaks and others. The overall evaluation of the acceptability of the video materials was very acceptable (M = 3.69) in terms of content (M = 3.79), instructional quality (M = 3.68), technical quality (M = 3.65), presentation and organization (M = 3.64), accuracy and up-to-datedness of information (M = 3.64), and assessment (M = 3.71). The students who achieved the objectives of the video lessons experienced a more personalized learning journey. Further research on and use of the strategy in various academic settings is recommended.

Impact analysis of real and virtual concept drifts on the predictive performance of classifiers

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are rarely stable in real-world circumstances and may evolve over time. Concept drifts, or dynamic changes in the data, have a major impact on how well classifiers predict outcomes. Furthermore, virtual concept drifts have become equally essential when dealing with the development of virtual environments and synthetic data production tools. This study employs a range of conventional classifiers, and ensemble models to examine the effects of real and virtual concept drifts on the predictive performance of classifiers. In order to ensure the conclusions are applicable to a wide range of domains, the study will be undertaken on a variety of datasets. The generation of incremental data streams from historical records with drifting patterns will replicate real concept drifts. Synthetic data will be incorporated into the training and testing sets to introduce virtual concept drifts. The classifier models are then assessed using the benchmark metrics for performance on these datasets. Comparative evaluations are conducted to evaluate the classifiers’ robustness and adaptability in the face of real and virtual concept drifts. The results of this study will shed light on the performance of various classifiers and models in dynamic data environments while offering helpful insights to businesses for developing machine learning applications that are more resilient. The research examined the impact of real and virtual concept drifts on classifier performance, using three models: Streaming Random Patches (SRP), Adaptive Windowing (ADWIN), and Streaming Random Patches with Adaptive Windowing (SRP-ADWIN). The study found that the SRP-ADWIN ensemble model outperformed SRP and ADWIN individually, effectively adapting to changing data distributions, and showed improved predictive accuracy of 98.63% for virtual drift and 86.46% for real drift.

IoT devices with data analytics capabilities Using machine learning algorithms for intelligent decision making: a review of patents and the cited academic literature

Innovation in IoT data analytics is essential for social and economic advancement as well as for opening up new business prospects. IoT data analytics applications have garnered a lot of attention lately for information mining, knowledge extraction, and prediction/inference making. Another field of study that has been effectively used to address a variety of networking issues, including resource allocation, routing, traffic engineering, and security, is machine learning. The use of machine learning (ML)-based techniques to enhance different Internet of Things applications has increased recently. Despite a wealth of research on machine learning and IoT data analytics, there is a dearth of studies that only address the development of ML-based methods for IoT data analysis. The present study examines the patenting practices of various international jurisdictions in the domain of IoT data analytics, in addition to the academically cited literature. Between 2000 and 2023, we gathered 24,931 patent applications, and 13,432 scholarly works were cited by the patents. Our research details the trends in the development of IoT data analytics technologies and the relationship between patenting activity and the referenced scientific literature. Additionally, it examines and displays the social networks found in scientific publications and patents. As a result, it discloses the patenting practices of international jurisdictions concerning IoT data analytics technologies, the degree to which different agents such as Lens Id and Applicants interact within social networks, and the connection between patents and science. According to this study, information retrieval, database structures, and file system structures make up the majority of the IPC categories for filed IoT data analytics patent applications. Chinese businesses and individuals make up the bulk of the patent Lens Id and Applicants. Computer science and medicine are scientific fields that are more strongly connected within the network of co-fields. The relationship between scientific works and inventions is not very strong.

Data Preprocessing for Cross-System Analysis: The DaProXSA Approach

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work.

MHD equilibria with nonconstant pressure in nondegenerate toroidal domains

We prove the existence of piecewise smooth MHD equilibria in three-dimensional toroidal domains of \mathbb R^3 where the pressure is constant on the boundary but not in the interior. The pressure is piecewise constant and the plasma current exhibits an arbitrary number of current sheets. We also establish the existence of free boundary steady states surrounded by vacuum with an external surface current. The toroidal domains where these equilibria are shown to exist need not be small perturbations of an axisymmetric domain, and in fact they can have any knotted topology. The building blocks we use in our construction are analytic toroidal domains satisfying a certain nondegeneracy condition, which roughly states that there exists a force-free field that is ergodic on the surface of the domain. The proof involves three main ingredients: a gluing construction of piecewise smooth MHD equilibria, a Hamilton–Jacobi equation on the two-dimensional torus that can be understood as a nonlinear deformation of the cohomological equation (so the nondegeneracy assumption plays a major role in the corresponding analysis), and a new KAM theorem tailored for the study of divergence-free fields in three dimensions whose Poincaré map cannot be computed explicitly.

FTIR and XRD Characterization of the Cements Available in Commercial Market of Nepal

In the engineering material characterizations and analytical domains where the specific physicochemical properties of the materials are probed through various sophisticated instrumental tools and techniques, the XRD and FTIR spectroscopy stand as the standalone applications as the former discloses the crystallographic structure, crystallite/grain sizes, and the chemical compositions of the specific materials nondestructively while the latter reveals details about the IR active vibrations modes of each covalent bond of their chemical constituents. In the present studies, we employ both of these techniques, and characterize the specifically selected variable manufactured ready-to-use dry OPC type cements available in the commercial markets of Nepal. The former technique derived the specific 2\(\theta\) value deterministic for identifying the closely packed interatomic layers with the significant levels of CaCO3, Ca(OH)2, and arsenic As5+ based AsO43- unit bearing crystalline phases in each samples. The calculated crystallite sizes of them are found as equal as that reported for the calcite based OPC cements, and are directly adopted to depict their utmost intermixing propensities with the carbon dust and hence, act as the most potential evidence of unlocking the possibility of inventing cement-carbon composites based supercapacitors and conductive devices, and of speculating their distinguishable hydration rates and setting times. And, the latter technique produced intense IR active bands in the designated wave number regions are used to confirm their silicate (SiO42-) tetrahedra, amorphous CaCO3 phases & the CO32-unit holding chemical constituents, 2CaO.SiO2 and 3CaO.SiO2 type chemical compounds explicitly. The presence of diffused type IR bands specific to the Al2O3 based chemical compounds signified that all of them contains significant levels of the alumina based hardening elements, but the complete absence of the peak typical to the Mg-O signified that none of them contains magnesia. The similar analyses of the IR bands further reveal that none of them possesses heavy and active type CaCO3, but have trace amounts of the AsO43- based chemical compounds as predicted by the XRD. The intensive peak area and the depth intensity studies of the designated IR bands speculate the dissimilar quantitative proportions and percentage compositions of these chemical constituents in them. The authors believe that the general results presented herewith illuminate the fundamental chemical constituents to be incorporated compulsorily into the OPC type cements so that they function normally and exhibit good cementitious abilities.

Highly luminescent heteroatom codoped carbon dots for multitudinous applications

Carbonaceous nanomaterials have shown great potential in the analytical and environmental domains due to their natural intriguing properties. Rich in quantum yield and reliable performances are necessary for carbon dots to be effectively used in the real-world applications. Carbon dots (CDs) exhibits high blue-emission with a quantum yield (ϕ) of 11% were synthesized through a single-step superficial pyrolysis technique. In this work, the derived carbon dots were employed as reducing and stabilizing agents in a unique one-step approach for the synthesis of AgNPs. The fluorescence of CDs was found to be quenched in the presence of dopamine with greater sensitivity, the minimum limit of detection was determined as 0.35 μM and possible mechanism could be the inner filter effect along with photoinduced electron transfer. In addition to this, the outstanding optical properties of CDs enable them to use as invisible ink in loading secret information and provide enhanced counterfeit protection. The solid-state quenching is successfully prevented when the CDs were dispersed in a polymer matrix for solid-state films preparation. The CDs-polymer composite is packed on top of a 365 nm UV LED chip, which emits blue light, to fabricate blue LED. Hence, we suggest that the synthesized CDs may serve as a future trend setter by as a single molecular probe for multifarious applications such as optoelectronics and forensic fields. Previously, we utilized these CDs for fingerprint detection and now apply them for new emerging applications.

Symphony: Orchestrating Sparse and Dense Tensors with Hierarchical Heterogeneous Processing

Sparse tensor algorithms are becoming widespread, particularly in the domains of deep learning, graph and data analytics, and scientific computing. Current high-performance broad-domain architectures, such as GPUs, often suffer memory system inefficiencies by moving too much data or moving it too far through the memory hierarchy. To increase performance and efficiency, proposed domain-specific accelerators tailor their architectures to the data needs of a narrow application domain, but as a result cannot be applied to a wide range of algorithms or applications that contain a mix of sparse and dense algorithms. This article proposes Symphony, a hybrid programmable/specialized architecture that focuses on the orchestration of data throughout the memory hierarchy to simultaneously reduce the movement of unnecessary data and data movement distances. Key elements of the Symphony architecture include (1) specialized reconfigurable units aimed not only at roofline floating-point computations but also at supporting data orchestration features, such as address generation, data filtering, and sparse metadata processing; and (2) distribution of computation resources (both programmable and specialized) throughout the on-chip memory hierarchy. We demonstrate that Symphony can match non-programmable ASIC performance on sparse tensor algebra and provide 31× improved runtime and 44× improved energy over a comparably provisioned GPU for these applications.

Optimizing Big Data Analytics for Reliability and Resilience: A Survey of Techniques and Applications

The advent of big data has revolutionized various industries, enabling organizations to make data- driven decisions and gain valuable insights. However, the sheer volume, velocity, and variety of big data pose significant challenges in ensuring the reliability and resilience of big data analytics pipelines. In this context, optimization techniques play a crucial role in enhancing the reliability and resilience of big data analytics. This paper provides a comprehensive survey of optimization techniques for reliable and resilient big data analytics. The paper first discusses the motivation for optimizing big data analytics for reliability and resilience. Then, it presents a detailed overview of various optimization techniques, including resource optimization, data partitioning, data compression, load balancing, and fault detection and tolerance. Finally, the paper discusses the applications of optimization techniques in various big data analytics domains, such as real-time analytics, fraud detection, recommendation systems, predictive analytics, and risk management.

Characterization of biases and their impact on the integrity of a risk study

Several recent publications have addressed the issue of risk study quality, which often calls for the minimization of biases. However, biases are prevalent and are often unavoidable in any analytic and decision-making domain. There remains little guidance on how to identify and address potential biases and how those biases can influence a risk study. This paper investigates biases related to systematic error, inclusion of events, models, and cognitive factors as they relate to the characterization of risk. With this understanding, we explore how the risk analyst can acknowledge and address those biases in support of a high-quality risk study. New insights are obtained by considering the biases in relation to the basic elements of a risk characterization: events and consequence, uncertainty measurements and descriptions, and the supporting knowledge. This paper will be of interest to risk analysts, policymakers, and other stakeholders for risk study applications.

Tailoring molecular recognition in predesigned multifunctional enzyme mimicking porphyrin imprinted interface for high affinity and differential selectivity; sensing etoposide in lung cancer patients

Nanozymes are cost-effective and robust but they lack specificity and selectivity, limiting their potential practical applications. Herein, molecularly imprinted polymers (MIPs) were grown in combination with multifunctional 5,10,15,20-tetrakis(4-hydroxyphenyl)-21H,23H-porphyrin (THPP) oxidase-like nanozyme to engineer THPP@MIP interface with high affinities and differential selectivity for structurally related target analytes. THPP nanozyme displayed a high level of predefined binding affinity for etoposide (ETO), and served as a predesigned functional monomer to rationally tailor the selectivity of THPP@MIP surface in the presence of different guest molecules. THPP nanozyme in combination with conventional monomers was imprinted on a portable and disposable cellulose paper matrix under UV light to create a UV-cured imprinted interface for optical detection of ETO. The THPP@MIP enzyme mimicking interface, having ETO specific and selective target recognition pockets, catalyzed the oxidation of colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to generate visible blue oxidized TMB (oxTMB) without exogenous hydrogen peroxide (H2O2). The ETO binding on the THPP@MIP surface blocked the channels for TMB access to THPP cavities. The THPP@MIP sensor permitted to detect ETO in the linear range of 0.005–10 μg mL−1, with a limit of detection (LoD) of 0.002 μg mL−1, and showed a remarkable specificity and selectivity against other drug molecules. Furthermore, the THPP@MIP sensor successfully differentiated the serum samples of lung cancer patients and healthy volunteers. The obtained results were validated with standard High performance liquid chromatography-mass spectrometry (HPLC/MS) analysis of the serum samples. Additionally, ETO injection/infusion solutions and ETO-free serum samples were used to perform the matrix effect and recovery studies. This work demonstrates that molecular imprinting with predesigned, enzyme mimicking, high-affinity functional monomer can serve as a highly selective and specific universal interface for broad spectrum sensing applications in various analytical domains.

Higher-continuity s-version of finite element method with B-spline functions

This paper proposes a strategy to solve the problems of the conventional s-version of finite element method (SFEM) fundamentally. Because SFEM can reasonably model an analytical domain by superimposing meshes with different spatial resolutions, it has intrinsic advantages of local high accuracy, low computation time, and simple meshing procedure. However, it has disadvantages such as accuracy of numerical integration and matrix singularity. Although several additional techniques have been proposed to mitigate these limitations, they are computationally expensive or ad-hoc, and detract from the method's strengths. To solve these issues, we propose a novel strategy called B-spline based SFEM. To improve the accuracy of numerical integration, we employed cubic B-spline basis functions with C2-continuity across element boundaries as the global basis functions. To avoid matrix singularity, we applied different basis functions to different meshes. Specifically, we employed the Lagrange basis functions as local basis functions. The numerical results indicate that using the proposed method, numerical integration can be calculated with sufficient accuracy without any additional techniques used in conventional SFEM. Furthermore, the proposed method avoids matrix singularity and is superior to conventional methods in terms of convergence for solving linear equations. Therefore, the proposed method has the potential to reduce computation time while maintaining a comparable accuracy to conventional SFEM.

Human Resource (HR) Practices - A Comprehensive Review

The Human Resource (HR) practices are critical cornerstone in contemporary organizational management. The field of HR has evolved significantly over the years, transitioning from a primarily administrative function to a strategic partner in achieving business objectives. Today, HR professionals are responsible for aligning HR practices with the organization's goals, ensuring that the right talent is in place, and creating a culture that supports high performance and employee well-being. Amidst the world of Technology, even HR is having its analytical wing and almost all function of HR is getting routed through the HR Analytics domain.
 This comprehensive review, led by researchers, delves into the multifaceted landscape of HR practices, exploring their significance, evolution, and benefits on organizational success. With a focus on both theoretical frameworks and real-world applications, the researchers examine the spectrum of HR practices, ranging from recruitment and selection to performance management and employee development. Employing a Descriptive and Exploratory Research design, this study encompasses both Primary and Secondary data. Primary data takes the form of interviews with HR Heads from five companies, offering insights into the HR practices of organizations. The research aims to understand the essence and significance of HR practices, while also investigating the benefits these practices bring to organizations. This study provides valuable insights into HR practices and highlights their adaptive nature in various organizational contexts. The analysis presented in this paper serves as a resource for HR professionals, managers, and researchers seeking to enhance their understanding of HR practices that drive organizational excellence. By critically assessing best practices, challenges, and emerging trends, this research contributes to the broader discussion on HR practices' impact on organizational success.

2D fast Fourier transform analytical solutions in all space for all gravity and magnetic components

ABSTRACTForward modelling of potential field data is an important part of optimization algorithms used to invert large datasets such as those involving rugged terrain or borehole data. Two‐dimensional fast Fourier transform modelling with a prism or a dipole is one of the most efficient methods compared to the forward modelling in the space domain. However, the exact solution of a prismatic source is limited to the case of a half‐space with the computation of data on a horizontal datum above the topography. Starting from the three‐dimensional Fourier forward modelling analytical formulation for a prism, an integration according to the wavenumber w is accomplished which allowed to find a two‐dimensional Fourier exact analytical formulation outside, at the interfaces of, and inside a prism for all potential field components. This new formulation requires the calculation of only four integrals. The gravity and magnetic fields are computed with this two‐dimensional fast Fourier transform formulation in the entire domain and compared with the analytical space domain and the three‐dimensional fast Fourier transform formulations. From the three‐dimensional calculated field, each component can be interpolated with the tri‐linear interpolation method along a borehole or on a drape surface simulating an airborne survey. Based on experiments demonstrated in this work, the two‐dimensional formulation in the Fourier domain gave accurate results with greater speed of execution in comparison to modelling in the space domain. The forward modelling method is tested on real gravity data from the north of Alberta (Canada).

Generalized Sine Functions, Complexified

Summary Generalized sine functions, primarily viewed as real-valued functions, are studied herein as complex analytic functions. Their natural domains of analyticity are identified and their mapping properties are detailed. Some implications of this theory for calculus and geometry are explored.

A visual-based toolkit to support mobility data analytics

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results.To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching.To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience.Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data.

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics

Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. Due to these frequent changes to the parameters of the game, esport analytics models can have a short life-spam, a problem which is largely ignored within the literature. This paper extracts information from game design (i.e. patch notes) and utilises clustering techniques to propose a new form of character representation. As a case study, a neural network model is trained to predict the number of kills in a Dota 2 match utilising this novel character representation technique. The performance of this model is then evaluated against two distinct baselines, including conventional techniques. Not only did the model significantly outperform the baselines in terms of accuracy (85% AUC), but the model also maintains the accuracy in two newer iterations of the game that introduced one new character and a brand new character type. These changes introduced to the design of the game would typically break conventional techniques that are commonly used within the literature. Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature.