Visual Data Mining Research Articles

Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.

Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

Probabilistic neural network based visual data mining for the healthcare sector.

The need for personalised care in the long-term management of patient health is paramount due to the variability in individual features and responses to specific medication. With the availability of large quantities of electronic patient records, big data analysis presents a valuable opportunity to gain insights into disease presentation and patient impact. This study aims to utilise data science in the medical field to extract unknown information from databases, validate previously obtained data, and enhance personalised patient care. An analytics suite is developed for monitoring patient health and treating cholesterol, thyroid, and diabetes disorders. This suite employs exploratory, predictive, and visual analytics to categorise patient data into multiple tiers and forecast related complication risk and treatment response. The study found that the analytics suite could successfully identify correlations between various biological indicators of patients and disorders. The suite also showcased potential in predicting health risks and responses to treatments. The analytics employed in this study suggest advanced methods of data analysis, which could serve as potential decision-making tools for healthcare providers. These methods might lead to improved treatment outcomes, contributing significantly to personalised patient care.

Perancangan Dashboard Visualisasi Data Kesehatan Ibu dan Anak di Dinas Kesehatan Kabupaten Jember

The amount of data generated is a challenge for agencies in the health sector to quickly analyze needs and interventions. The management and presentation of data carried out by the Jember District Health Office has not yet found an optimal method to present a lot of data into easily understood health information. This study aims to design a health data visualization dashboard using Microsoft Excel to assist in data management and facilitate policy formulation. The method used is research and development (R&D) with a visual data mining (VDM) model. The results showed that the data used for visualization was the MCH data of Jember Regency in 2021. The resulting dashboard consists of 4 maternal health dashboards and 5 child health dashboards. Verification of dashboard results shows that the dashboard is 100% functional, so it is accepted by the user. Dashboard testing using the User Experience Questionnaire (UEQ) has an average of positive evaluation results. Future researchers are expected to develop the types of data and visualization tools used in designing dashboards.

Extracellular vesicles in the treatment of oxidative stress injury: global research status and trends.

Objective: The aim of this study was to conduct a bibliometric analysis of the literature on "Extracellular Vesicles in the Treatment of Oxidative Stress Injury" and to reveal its current status, hot spots and trends. Methods: The relevant literature was obtained from the Web of Science Core Collection (WoSCC) on 29 April 2023. We performed clustering and partnership analysis of authors, institutions, countries, references and keywords in the literature through CiteSpace software and the bibliometric online analysis platform and mapped the relevant knowledge maps. Results: A total of 1,321 relevant publications were included in the bibliometric analysis, with the number of publications in this field increasing year by year. These included 944 "articles" and 377 "reviews". The maximum number of publications published in China is 512, and the maximum number of highly cited publications published in the United States is 20. Based on CiteSpace, the country collaboration network map shows close and stable collaboration among high-productivity countries. Based on WoSCC, there are 1706 relevant research institutions and 119 highly cited elite institutions, among which Kaohsing Chang Gung Men Hosp has the most extensive influence. Studies related to "Extracellular Vesicles in the Treatment of Oxidative Stress Injury" have been published in 548 journals. The keywords of the publications show the main research areas and breakthroughs. Based on WoSCC, the keywords of the research area "Extracellular Vesicles in the Treatment of Oxidative Stress Injury" were found to be as follows: exosome(s), extracellular vesicle(s), oxidative stress, inflammation, mesenchymal stem cells, apoptosis, microRNA (miRNA), mitochondria, biomarker, autophagy, angiogenesis and Alzheimer's disease. Analysis showed that "mesenchymal stem cells", "microRNA", "autophagy", "histology" and "therapeutic" emerged as highly explosive keywords. Conclusion: This study is the first to use visual software and data mining to assess the literature in the field of "Extracellular Vesicles in the Treatment of Oxidative Stress Injury". The research history, research status and direction in this field provide a theoretical basis for its scientific research.

Proposal of a Visualization System for a Hierarchical Clustering Algorithm: The Visualize Proximity Matrix

Visual data mining is a new and effective strategy for dealing with the growing phenomenon of information overload. There is an urgent need for effective visual data mining because the growth of data sets from different domains and sources has made exploring, managing and analyzing vast volumes of data increasingly difficult. Identifying location-based trends and anomalies in data from a numeric output is challenging. The outputs of data mining procedures are often quite difficult to interpret. There is no human input to data exploration or direct involvement in the data mining process. Thus, it is difficult to analyze, explore, understand and view the data. In data mining, massive data sets are clustered using an iterative process that includes human input but data mining algorithms can miss some knowledge and observations. In addition, many users cannot take the appropriate action to address problems or opportunities because the huge amounts of data prevent them from staying aware of what is happening in and around their environments. Visual data mining can help in dealing with information overload and it is effective in analyzing large complex data sets. Visual data mining assists users in gaining a deep visual understanding of data. In the information age, users need to study and observe vast amounts of data to acquire important knowledge, and thus the need for visual and interactive analytical tools is particularly pressing. Visual cluster analysis has long utilized shaded similarity matrices, and this study investigated how they can be used in clustering visualization. We focused on proposing an agglomerative hierarchical clustering method. Ensemble cluster visualization is also presented for handling large data sets. This study proposes the adoption of a shaded similarity matrix to visually cluster knowledge discovered using data mining. Using the technology acceptance model as the measuring tool, we questioned respondents to evaluate the visualization prototype. The findings demonstrated that the visualization was effective and easy to use, and satisfied users.

Emerging advances in deep learning and computer vision for visual data search and mining in E-commerce

Emerging advances in deep learning and computer vision for visual data search and mining in E-commerce

Network visual data mining and analysis of literature on electroacupuncture treatment of post-stroke sequelae

To analyze the optimum stimulating parameters and acupoint combination law of electroacupuncture (EA) treatment of post-stroke sequelae (PSS) through the network visual data mining and analysis of the literature，so as to provide reference for clinical application. The related articles of acupuncture treatment of PSS patients published from January 1, 2005 to December 31, 2021 were collected from databases of CNKI, Wanfang, Weipu（VIP), SinoMed, CHAOXING, DUXIU, PubMed, Ovid, Embase and Cochrane Library. After the articles were screened according to our formulated inclusion and exclusion criteria, a "Clinical Literature Database of EA Treatment of PSS" was established. The SPSS Modeler14.1 software was used for modeling (by using the parameters of EA, types of apoplexy sequelae, names of acupoints, methods of acupoint matching, acupoint-attributed meridians, etc.) and association rule analysis, and the Cytoscape 3.6.0 software was used for data network visualization analysis. A total of 354 articles were collected, including 13 types of PSS, among which the hemi-plegia accounts for the highest proportion (51.13%), followed by depression (17.51%) and dysphagia (14.97%). A total of 228 acupoints are used, with a total frequency-time of 1 690. Among the 8 PSS (hemiplegia, depression, dysphagia, cognitive impairment, urinary incontinence, urinary retention, speech disorders, constipation) with the frequency of application of acupoints greater than or equal to 3, 275 articles are involved, with the frequency of dense wave being the highest (123 times), followed by continuous waves (95 times). The stimulation frequency of EA appears 275 times, with the low frequency being the highest (188 times). For hemiplegia type of PSS, low frequency and continuous waves or dense waves are mostly used, and the high correlation acupoint groups are Zusanli(ST36)/Hegu(LI4), LI4/Quchi(LI11) and ST36/LI11. For depression, low frequency and dense waves are mostly used, and the high correlation acupoint groups are Baihui(GV20)/Yintang(EX-HN3), GV20/Shenting(GV24) and GV20/Neiguan(PC6). For swallowing disorder, the mostly used stimulation parameter is low frequency, and the high correlation acupoint groups are Lianquan(CV23)/ST36/Fenglong(ST40), Tongli(HT5)/Yifeng(TE17)/PC6/ST40, and TE17/HT5/PC6/LI4. EA therapy is frequently used in the treatment of PSS, for which ST36, LI4 and LI11 are most frequently used in the treatment of hemiplegia, with the stimulation parameters being low-frequency and continuous waves or dense waves. For depression, GV20, EX-HN3, GV24 and PC6 are used, with the stimulation parameters being low frequency and dense waves. For dysphagia, CV23, HT5, TE17, PC6, ST36, ST40 and LI4 are employed, with low frequency electrical stimulation.

Retraction

Retraction: Tang T, Hu P, Wu G. Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining. J Concurrency Computat Pract Exper. 2021; 33(12).The above article, published online on 26 Nov 2019 Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5587) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies were found, including general logical flaws and irrelevant citations, making the motivation and conclusions of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.

Visual Data Mining: Digital Visual Activities In Network Society

This article describes scientific discoveries that drive the phenomenon of visual cultural communication media in networked societies. Many types of research on visual culture have focused more on visual arts and mass media. However, few have yet addressed the problem of people's digital activities in searching for visual information on the Internet ( Visual search engine as a resource for visual data mining The amount of digital information, including visual information, is increasing exponentially.This research uses a literature study method with a qualitative approach. The primary data used in this study are studies on visual culture and visual search engines published in international peer-reviewed journals. Analysis of digital behavior and visual culture in a networked society is described as a research topic in previous research results. The conclusions drawn indicate that there is still a relatively deep and broad analysis of the study of visual culture when using digital communication media. There are few research studies on visual culture and digital activities in connected communities, especially people's visual information search behavior.

The effects of the aesthetics and composition of hotels’ digital photo images on online booking decisions

Photographic images help customers perceive product information more accurately and clearly. A customer’s perception of a particular product also influences their decision to purchase it. In the context of a hotel, guests evaluate digital hotel photos online during their booking decision process. While a large body of research has contributed to the understanding of how hotel online digital images shape hotel customer behaviour, little is known about the aesthetics, content, and composition of hotel images and their effects on booking decisions. In addition, previous research has routinely been criticised for having methodological limitations. These studies have routinely used surveys and experiments to explore how hotel pictures affect customer perception of the hotel and his/her booking intentions. Unlike prior studies, this research scopes a determination of the ‘selling’ properties pertinent to the hotel’s digital images placed online on the hotel-themed websites with the application of the latest technologies pursuant to visual data mining, processing and analysis. This study employed Google’s Inception v3 neural network as an AI solution for embedding and classifying hotel photo images with the further application of logistic regression and fuzzy cognitive mapping method. The results of the present study determined the hotel picture properties that may engender positive customer perception of the hotel and sequentially can precipitate hotel booking. The revealed ‘selling’ hotel image properties comprise (a) light and time of the photo shooting, (b) image colour scheme, (c) human presence, and (d) shooting angle. This study suggests a set of practical recommendations to hotel marketers to develop ‘selling’ photo images that generate hotel bookings online. The completed research is one of the first in the nascent literature stream in AI-powered computer vision solutions studies to determine the effects of photo aesthetics on online hotel bookings.

Generative Artificial Intelligence and 3D Virtual Simulation Technologies, Ambient Scene Detection and Automated Employment Decision Tools, and Visual Data Mining and Immersive Remote Collaboration Systems in Decentralized Autonomous Organizations

Generative Artificial Intelligence and 3D Virtual Simulation Technologies, Ambient Scene Detection and Automated Employment Decision Tools, and Visual Data Mining and Immersive Remote Collaboration Systems in Decentralized Autonomous Organizations

A multimedia knowledge discovery-based optimal scheduling approach considering visual behavior in smart education.

Nowadays, the convergence of intelligent computing technique and education has been a hot concern for both academia and industry, producing the conception of smart education. It is predictable that automatic planning and scheduling for course contents are the most practical important task for smart education. As online and offline educational activities are visual behaviors, it remains challenging to capture and extract principal features. To breakthrough current barriers, this paper combines the visual perception technology and data mining theory, and proposes a multimedia knowledge discovery-based optimal scheduling approach in smart education about painting. At first, the data visualization is carried out to analyze the adaptive design of visual morphologies. On this basis, it is supposed to formulate a multimedia knowledge discovery framework which can implement multimodal inference tasks, so as to calculate specific course contents for specific individuals. At last, some simulation works are also conducted to obtain analysis results, showing that the proposed optimal scheduling scheme can work well in contents planning of smart education scenarios.

The Bakken and Three Forks Formations Daily Crude Oil Production per Well Prediction Based on Support Vector Regression

In the oil and gas industry, there is a major challenge to accurately predict the crude oil production due to the complexity and sophistication of the subsurface conditions. Production forecasting is highly limited by the non-linearity between hydrocarbon production and any relevant petrophysical parameter. Trying to use just the conventional mathematical approaches might give inaccurate result because of the numerous assumptions employed by this approach. Therefore, there is a huge need to develop a reliable prediction model of hydrocarbon production. This will surely assist Petroleum Engineers to have a better understanding of the entire reservoir behavior to solve, evaluate, and optimize its overall performance. Utilizing data driven models which is the machine learning techniques can help to predict crude oil production with much more acceptable accuracy. In this paper, Python-Support Vector Regression and Orange-Linear Regression have been implemented to build the models that predict the daily oil production of a well in Bakken-Three Forks Formations. The statistical data for the Bakken-Three Forks formation oil production was from North Dakota Industrial Commission (NDIC) website. An open-source visual programmingbased data mining software Orange was used to train a multi-linear regression model of 817 datasets with addition of 200 lines of code algorithm written in Python which is a high-level programming language. Combination of these two software models gave a more robust and accurate predictions compared to the conventional method of using just a software model by others. The models developed can practically estimate the Daily oil production of a well in Bakken-Three Forks Formations. The R2 obtained is 0.98 from the low performance value of 0.35, the MAE became 10.593 and RMSE is 16.593 for SVR and linear regression with a cross validation of 10 folds for the 70% train dataset and 30 % test dataset shows MSE value of 2.826, RMSE of 1.681, MAE of 1.045 and R2 value of 0.998. The performance of this SVR model indicate that this developed model can be used to predict the Daily oil produced per well accurately with the supervised algorithm. The values obtained from the Orange-Linear regression show better performance when compared with the SVR and validates the values obtained from the Python Support Vector Regression from the model criteria evaluation of the results.

Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm

Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase.

Trends in Synthetic Biology in the Bioeconomy of Non-Food-Competing Biofuels

Despite the acknowledged relevance of renewable energy sources, biofuel production supported by food-related agriculture has faced severe criticism. One way to minimize the considered negative impacts is the use of sources of non-food biomass or wastes. Synthetic biology (SB) embraces a promising complex of technologies for biofuel production from non-edible and sustainable raw materials. Therefore, it is pertinent to identify the global evolution of investments, concepts, and techniques underlying the field in support of policy formulations for sustainable bioenergy production. We mapped the SB scientific knowledge related to biofuels using software that combines information visualization methods, bibliometrics, and data mining algorithms. The United States and China have been the leading countries in developing SB technologies. The Technical University of Denmark and Tsinghua University are institutions with higher centrality and have played prominent roles besides UC Los Angeles and Delft University Technology. We identified six knowledge clusters under the terms: versatile sugar dehydrogenase, redox balance principle, sesquiterpene production, Saccharomyces cerevisiae, recombinant xylose-fermenting strain, and Clostridium saccharoperbutylacetonicum N1-4. The emerging trends refer to specific microorganisms, processes, and products. Yarrowia lipolytica, Oleaginous yeast, E. coli, Klebsiella pneumoniae, Phaeodactylum tricornutum, and Microalgae are the most prominent microorganisms, mainly from the year 2016 onward. Anaerobic digestion, synthetic promoters, and genetic analysis appear as the most relevant platforms of new processes. Improved biofuels, bioethanol, and N-butanol are at the frontier of the development of SB-derived products. Synthetic biology is a dynamic interdisciplinary field in environmentally friendly bioenergy production pushed by growing social concerns and the emergent bioeconomy.

INTERNET OF THINGS BASED ZIGBEE SNIFFER FOR SMART AND SECURE HOME

The aim of this research is to determine the usage of energy or power with high spectrum allocation in ZigBee Protocol with the help of clustering in IoT. This research starts presenting an overview of the broadband network energy sector and the challenges that are facing. It is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group in order to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation. Most of these methodologies use the absolute values in 100 kWh, as they were more focused on identify the users with higher energy savings potential. In this case, it allows personalizing energy savings recommendations according to the specific characteristics of ZigBee protocol, improving the consumer experience by being able to provide the adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advises’ service for future ZigBee protocol.

The mitoXplorer 2.0 update: integrating and interpreting mitochondrial expression dynamics within a cellular context.

Mitochondria are subcellular organelles present in almost all eukaryotic cells, which play a central role in cellular metabolism. Different tissues, health and age conditions are characterized by a difference in mitochondrial structure and composition. The visual data mining platform mitoXplorer 1.0 was developed to explore the expression dynamics of genes associated with mitochondrial functions that could help explain these differences. It, however, lacked functions aimed at integrating mitochondria in the cellular context and thus identifying regulators that help mitochondria adapt to cellular needs. To fill this gap, we upgraded the mitoXplorer platform to version 2.0 (mitoXplorer 2.0). In this upgrade, we implemented two novel integrative functions, network analysis and transcription factor enrichment, to specifically help identify signalling or transcriptional regulators of mitochondrial processes. In addition, we implemented several other novel functions to allow the platform to go beyond simple data visualization, such as an enrichment function for mitochondrial processes, a function to explore time-series data, the possibility to compare datasets across species and an IDconverter to help facilitate data upload. We demonstrate the usefulness of these functions in three specific use cases. mitoXplorer 2.0 is freely available without login at http://mitoxplorer2.ibdm.univ-mrs.fr.

Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques

To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analysis using visualization and unsupervised data mining techniques including clustering and association rule mining. The visualization of data helps to uncover trends present in the crime dataset. The K-modes clustering algorithm is used to perform the exploratory analysis and risk identification of similar criminal activities that can happen in a particular location. The Apriori algorithm is applied to mine frequent patterns of criminal activities that can happen on a particular day, time, and location in the future. The data were acquired from paper-based records of three police stationsin the Urdu language. The data were then translated into English and digitized for automatic analysis. The result helped identify similar crime-related activities that can happen in a particular location, the risk of potential criminal activities occurring on a specific day, time, and place in the future, and frequent crime patterns of different crime types. The proposed work can help the police department to detect crime events and situations and reduce crime incidents in the early stages by providing insights into criminal activity patterns.

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the $$\delta$$ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.