Review on audit data visualization method based on R language

Aims: The aim of this study is to investigate the impact of thresholds on the detection of outliers by comparing the performance of two estimators, namely the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD), at different sample sizes. The study uses simulated data generated from the standard normal distribution to assess how varying thresholds affect the ability of these estimators to detect outliers.
 Study Design: This study employs a quantitative research design. It involves the generation of simulated data, the application of the MCD and MRCD estimators for outlier detection, and the systematic manipulation of thresholds and sample size as independent variables.
 Place and Duration: The study is conducted using computational tools and did not require a physical location.
 Methodology: Simulated data is generated from the standard normal distribution to create a controlled environment for outlier detection experiments. The MCD and MRCD estimators are applied to the simulated data to detect outliers. These estimators are sensitive to deviations from the norm in the data. Different thresholds are systematically applied to the data, and the performance of the estimators is assessed at each threshold level. Thresholds may vary in their extremeness. The study investigates the impact of different sample sizes on outlier detection. This involves using datasets with varying numbers of observations. The r programming language and associated packages are used as the statistical tool for data generation, analysis, and visualization.
 Results: The study's findings indicate that the choice of thresholds in data analysis significantly affects the performance of the MCD and MRCD estimators in outlier detection. If the thresholds used for both estimators are the same, their performance is similar. However, differences emerge when thresholds differ from each other. Higher thresholds are shown to identify less extreme outliers, while lower thresholds are effective at identifying more extreme outliers. These results provide insights into the behavior of these estimators in outlier detection scenarios, shedding light on their sensitivity to threshold choices and sample size.Conclusion: Our study has shed light on the critical interdependencies among threshold choices, sample sizes, and the performance of the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD) estimators in the context of outlier detection. By conducting a systematic exploration in a controlled environment with simulated data, we have gleaned valuable insights that can inform both researchers and practitioners in the field of organizational science research.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

BackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

Development of public toxicogenomics software for microarray data management and analysis

Annotation. Big Data (BD) is one of the most commonly used terms in the modern world of business and information technology. The main features of BD (quantity, speed, and variety) introduce to unique processing of large information amounts, regardless of their scale, storage and computational complexity, analytical and statistical correlation. The significant emergence and potential use of BD has affected business accounting and financial auditing by replacing the long-used mechanical data collection and completion processes with automatic ones, comparing and searching for correlations between different structure and nature data. According to analysis, the main advantages of applying the BDA in the audit process are related to faster and more efficient execution of procedures, obtaining more detailed results, grouping and comparing data according to selected criteria. In the meantime, cons of BD application are related to the additional professional supervision requirements and the proper data analysis in order for the correct results interpretation. The paper presents the conceptual model, which shows the relationships between BDA tools and financial audit procedures. In addition, the model shows factors and risks, which have impacts on internal and external environment of clients, the applicability of specific audit procedures. It was found that the application of the model in the procedures includes testing of 5 relationships, i. e. classification, clustering, regression and time series analyses, the method of association rules and text research, visualization tool. The Aim of the Study is to identify the application of DDA tools in financial audit procedures. Research Methods: comparative and systematic analysis of the literature; content analysis; statistical data analysis; graphical analysis. Keywords: Big data, Big data Analytics, Financial Audit, Financial Audit Procedures. JEL Code: M15, M40, M42.

Big data represents a new technology for managing data with high velocity, volume, variety and contributes to creating value for companies. As quoted in1, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends. The Big Data Era has largely contributed in accelerating the development strategic plans issued from governments and research organisms, coving the management, exploitation and analysis of these data by taking into account the different V's of Big Data. Among these plans, we can cite for instance the development of: (a) large-scale platforms (ex. data-clusters, distributed data clusters), (b) Software Defined Environments (SDE) (ex. IBM SDE), (c) advanced programming paradigms (ex. map-reduce, Spark, etc.), Data Analytics Tools (Rapid Miner, Google Fusion Tables, Solver), (d) Visualization tools (Google Chart, Tableau, Oracle Visual Analyzer), and (h) high quality and valuable Knowledge Bases (KB), constructed either by academicians (e.g., Cvc, DBpedia, Freebase, and YAGO) and industrials (e.g., Google Knowledge Graph, Facebook Knowledge Graph, Amazon Knowledge Graph, Credit Rating Agencies, Enterprise Knowledge Base, etc.). In this talk, we would like to foster the creation of a think tank dedicated to getting the best from Big Data V's and the efforts related to it to revisit our research activities without compromising them. In this talk, we would like to share the experience conducted with our Model and Data Engineering Team of the LIAS Laboratory at ISAE-ENSMA, which aims at the design of data warehousing applications. Based on the literature, this design is based on two main approaches: (i) a supply-driven approach (also called data-driven) that starts with an analysis of operational data sources in order to identify all the available data and (ii) a user-driven approach (also known as requirement-driven or goal-orientated) which stems from the determination of the information requirements of different business users. Several studies and experiments show that resorting to these two approaches entails a high risk for companies, since some functional requirements cannot be satisfied. This is due to the lack of relevant data in sources. In parallel, reference studies have identified the crucial role of knowledge bases (KB) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality valuable KB is an asset for data warehousing designers and decision-makers to construct/exploit a valuable data warehouse. So, faced with this situation, we here present a value-driven approach that revisits the traditional life cycle of the design of data warehouses, by considering KB as an external resource. These different phases are illustrated via the YAGO KB.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Modern instruments have the capacity to generate and store enormous volumes of data and the challenges involved in processing, analyzing and visualizing this data are well recognized. The field of Chemometrics (a subspecialty of Analytical Chemistry) grew out of efforts to develop a toolbox of statistical and computer applications for data processing and analysis. This chapter will discuss key concepts of Big Data Analytics within the context of Analytical Chemistry. The chapter will devote particular emphasis on preprocessing techniques, statistical and Machine Learning methodology for data mining and analysis, tools for big data visualization and state-of-the-art applications for data storage. Various statistical techniques used for the analysis of Big Data in Chemometrics are introduced. This chapter also gives an overview of computational tools for Big Data Analytics for Analytical Chemistry. The chapter concludes with the discussion of latest platforms and programming tools for Big Data storage like Hadoop, Apache Hive, Spark, Google Bigtable, and more.

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.

A web based MDSplus data analysis and visualization system for EAST

In the modern business landscape, data visualization has become an indispensable element of business intelligence (BI), particularly within the context of US companies. The aim of this research paper is to examine how data visualization technologies might improve business intelligence and decision making in American businesses by examining adaptation, advantages and disadvantages. This research paper explores the transformative contribution of data visualization tools to improving business intelligence capabilities and their significant influence on the processes of decision-making. The study begins by examining the adoption of various data visualization tools and platforms, such as Tableau, Power BI, and Elkview within US companies. It delves into how these tools facilitate the conversion of complex data sets into visually intuitive formats, allowing stakeholders at all levels to grasp trends, patterns, and anomalies with greater ease. By integrating these visualization tools into their BI systems, companies can achieve a more nuanced understanding of their operational landscapes and market dynamics. The paper further evaluates the impact of data visualization on decision-making processes. Through an analysis of case studies and empirical data, it highlights several key benefits, including improved accuracy in data interpretation, enhanced ability to identify actionable insights, and accelerated decision-making timelines. The paper discusses strategies to overcome these challenges, such as ensuring data integrity and investing in user training to maximize the efficacy of visualization tools. Overall, the findings underscore that while data visualization is not a panacea, it significantly augments the decision-making capabilities of US companies by making data more accessible and actionable. The integration of advanced visualization techniques into BI practices is increasingly seen as a strategic advantage, driving both operational efficiencies and competitive differentiation. As data continues to proliferate, the role of data visualization in shaping business strategies and outcomes will likely become even more pronounced, making it a critical area of focus for future research and development in the field of business intelligence.

Significant advances have been made to nonintrusive flow field diagnostics in the past decade. Camera based techniques are now capable of determining physical qualities such as surface deformation, surface pressure and temperature, flow velocities, and molecular species concentration. In each case, extracting the pertinent information from the large volume of acquired data requires powerful and efficient data visualization tools. The additional requirement for real time visualization is fueled by an increased emphasis on minimizing test time in expensive facilities. This paper addresses a capability titled LiveView3D, which is the first step in the development phase of an in depth, real time data visualization and analysis tool for use in aerospace testing facilities

An interactive visualization and navigation tool for medical volume data

Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an American interactive data visualization software company focused on business intelligence and can be used across all disciplines. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations.[1] Power BI (Business Intelligence) is a suite of business analytics tools developed by Microsoft that is widely used across various industries for business reporting, data analysis, and decision-making. [2] Future directions of this research are discussed that include using other data mining and visualization methods such as image processing techniques for applications to data for plant pathology and human diseases.