Investigation of Computational Topology for Data Analysis and Visualization Applications

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.

Focus on software

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.

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Chapter 2 - Information Visualization: Scope, Techniques and Opportunities for Geovisualization

As the philosophy of foreign language teaching constantly evolves in the direction of precision, informatization and intelligence, the corresponding teaching modes also transforms with the aid of information and internet technology, advocating the precise teaching design, teaching evaluation and the data-driven decision-making in teaching. Based on the statistical and visualization module of JASP, this article explores the application of data visualization software like JASP in blended foreign language teaching, shedding light on the visualization and statistical analysis of qualitative data accumulated in four rounds of blended teaching, highlighting the significance and value of data analysis and data visualization techniques for the blended teaching of foreign languages in the age of intelligence.

A study on the influence of leadership orchestration on e-readiness accession in higher education in the Republic of Kenya concluded in 2018 showed that there is a correlation between the leadership performance on selected leadership indices and the institution’s digital age readiness. Does this correlation also apply to adoption of evidence-based decision-making and, thus, application of data analytics, visualization and business intelligence? Cross-sectional survey design was employed. Quantitative data was collected through simple random sampling using an online questionnaire in nine purposively selected universities. Seven key questions formulated around process and data were posed to the participants. Qualitative data was obtained using focus group discussion and interviews. Pearson Moment Co-efficient of Correlation and bivariate regression were done. Content analysis was applied on the qualitative data. Significant correlation was observed with six out of seven items. It is concluded that institutional perspective on process performance and evidence-based decision-making is correlated with leadership orchestration. Effective adoption and application of data analytics, visualization and business intelligence in the digital age is dependent on institutional leadership. Recommendation is made to re-orient leadership in higher education to evidence-based decision-making and data-driven culture in order to harness the power of data analytics, visualization and business intelligence to improve the quality and productivity of higher education in Kenya.

The exponentially increasing volumes of health data of different types is a challenge for reaching robust conclusions. The Health Vision platform overcomes this issue by providing advanced and multi-scaling data analysis and visualisation. The platform functions through user defined workflows, each organised into four distinct layers: 1. Raw Data, 2. Data Analytics, 3. Decision Support and 4. Data visualisation. In the Raw Data Layer, data from medical records, healthcare platforms, smart homes, monitoring sensors, and medical devices, clinical features from Electronic Health Records (EHRs), etc. can be considered, while the Data Analytics Layer provides data analysis services for data mining. The Decision Support Layer facilitates the selection of appropriate visualisation methods taking into consideration the specifications of the raw data and the output of the data analysis. Finally, the Data visualisation Layer provides intelligent visualisation methodology in order for the user to interact with the data and gain meaningful results. Visualisation is supported by a reactive workflow mechanism, allowing the composition of analysis and visualisation services in a custom application-specific data flow. We demonstrate and evaluate this novel approach in several use cases, where different types of data are involved, including: Clustering of Sjogren's syndrome patients, visualisation of Parkinson's disease patients, mutation association analysis in Chronic Lymphocytic Leukemia patients, behavioral analysis of asthma patients and clinical analysis based on clinical similarities of older adults.

Persistent homology is used for computing topological features of a space at different spatial resolutions. It is one of the main tools from computational topology that is applied to the problems of data analysis. Despite several attempts to reduce its complexity, persistent homology remains expensive in both time and space. These limits are such that the largest data sets to which the method can be applied have the number of points of the order of thousands in ℝ3. This paper explores a technique intended to reduce the number of data points while preserving the salient topological features of the data. The proposed technique enables the computation of persistent homology on a reduced version of the original input data without affecting significant components of the output. Since the run time of persistent homology is exponential in the number of data points, the proposed data reduction method facilitates the computation in a fraction of the time required for the original data. Moreover, the data reduction method can be combined with any existing technique that simplifies the computation of persistent homology. The data reduction is performed by creating small groups of similar data points, called nano-clusters, and then replacing the points within each nano-cluster with its cluster center. The persistence homology of the reduced data differs from that of the original data by an amount bounded by the radius of the nano-clusters. The theoretical analysis is backed by experimental results showing that persistent homology is preserved by the proposed data reduction technique.

SummaryPathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.Availability and implementationThe data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.Supplementary informationSupplementary data are available at Bioinformatics online.

With the rapid development of social media, massive user-generated data continues to emerge, making it increasingly challenging for enterprises and researchers to extract effective insights from unstructured information. Data visualization, as a key technology that transforms complex data into intuitive graphics, plays a vital role in addressing this issue. This study adopts a combined approach of theoretical analysis and case research to systematically explore the application of data visualization in social media analytics. It outlines basic types and commonly used tools of data visualization suitable for social media scenarios. Subsequently, through specific cases involving international sports brands, chain restaurants, and cosmetics companies, it conducts an in-depth analysis of three core application areas: user behavior analysis, public opinion monitoring and crisis management, and marketing effectiveness evaluation. The findings indicate that the proper use of visualizations such as network diagrams, time series charts, and heat maps can accurately identify opinion leaders, predict user activity patterns, and quantify marketing return on investment (ROI). This research provides practical references for enterprises to enhance social media analysis efficiency and formulate data-driven decisions.

Regardless of how it may seem at times, the President of the United States is not in charge of all the major decisions made by the government. The laws that we must adhere to are made, formed, and passed by Congress which is composed of the House of Representatives and the Senate. Since 2017, members of Congress proposed over 9000 bills of which 100 became law but mainstream media only covers a small proportion of these bills. Even though this information about the legislative process is available online, the method of presentation and the sheer quantity of information is overwhelming. The current state of affairs makes it difficult for an individual to keep track of the activities and affiliations of their representative and senators, let alone understand the movements and trends in Congress as a whole. Here I approach an automated method of data collection, analysis, and visualization to mitigate this problem and make transparent the activities of members of Congress. By creating metrics of productivity and partisanship for each member along with analysis of Congress member networks, I identify specific techniques of quantifying individual and collaborative efforts of Congress members. Moreover, topic modeling of bill names and summaries allows for a compression of text content in bills and creation of networks between legislative topics and Congress members. With this analysis, I hope to make the legislative process in the United States more accessible to everyone.

Visualization is a part of data science, and essential to enable sophisticated analysis of data. The visualization ensures the human participation in most decisions when analyzing data. In this paper, we review methods and software for visualization of multidimensional data. The emphasis is put on the web-based DAMIS solution for data analysis, allowing researchers to carry out the primary data analysis and to investigate the projection of multidimensional data on a plane, the similarities between the data items, the influence of individual features, and their relationships by visual analysis techniques, using the high-performance computing resources. DAMIS is applied to the visual efficiency analysis of regional economic development to evaluate how regional resources are reflected in the economic results. The projection methods (principal component analysis, multidimensional scaling) and artificial neural networks (self-organizing map, SAMANN) are the core strategies for the analysis.

The amount of data generated is increasing exponentially. However, processing data and producing fast results is a technological challenge. Parallel stream processing can be implemented for handling high frequency and big data flows. The MPI parallel programming model offers low-level and flexible mechanisms for dealing with distributed architectures such as clusters. This paper aims to use it to accelerate video analytics and data visualization applications so that insight can be obtained as soon as the data arrives. Experiments were conducted with a Domain-Specific Language for Geospatial Data Visualization and a Person Recognizer video application. We applied the same stream parallelism strategy and two task distribution strategies. The dynamic task distribution achieved better performance than the static distribution in the HPC cluster. The data visualization achieved lower throughput with respect to the video analytics due to the I/O intensive operations. Also, the MPI programming model shows promising performance outcomes for stream processing applications.

Today, modern software and hardware of information technologies are used to automate production processes, starting from modeling of technical developments and automated computer systems. This study is aimed at describing the process of implementing software support for economic analysis of the enterprise, which will calculate the main economic indicators and informatively display the calculated data. The object of the study is an automated system for calculating and maintaining the main economic indicators of the enterprise. The subject of research is methods of database design and development, data visualization and web applications. A review of literature sources allowed to conclude that most often to assess the economic performance of the enterprise, standard technologies are used, which the authors adapt to specific economic problems. In addition, the analysis of modern research has not identified a single software product that would collect, accumulate, analyze information and calculate the main indicators of economic activity of the enterprise, as well as monitor changes in such indicators in the dynamics. Automation of calculation of economic indicators, statistical analysis and visualization of data gives the chance to carry out effective management of the enterprise in modern economic conditions. The developed system allows to analyze the use of fixed assets; working capital analysis; analysis of the use of labor resources; profitability analysis; analysis of financial stability of enterprises. The paper shows the process of developing a functional model of a web application for the automated calculation of key economic indicators, and presents the advantages and disadvantages of working with the PostgreSQL database management system. The structure of the developed database, which consists of four tables, is given. An example of the work of the developed web application is presented.