The Data Visualization Corpus

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Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

Colour patterns and their visual backgrounds consist of a mosaic of patches that vary in colour, brightness, size, shape and position. Most studies of crypsis, aposematism, sexual selection, or other forms of signalling concentrate on one or two patch classes (colours), either ignoring the rest of the colour pattern, or analysing the patches separately. We summarize methods of comparing colour patterns making use of known properties of bird eyes. The methods are easily modifiable for other animal visual systems. We present a new statistical method to compare entire colour patterns rather than comparing multiple pairs of patches. Unlike previous methods, the new method detects differences in the relationships among the colours, not just differences in colours. We present tests of the method's ability to detect a variety of kinds of differences between natural colour patterns and provide suggestions for analysis.

bellabeats was a case study given to me by my google course capstone project. The case study's focus was to use smart devices and find insights. The dataset was taken from https://www.kaggle.com/arashnic/fitbit where they surveyed about 33 participants who took the survey on Amazon Mechanical Turk. I took my analysis and use my conclusions to give bellabeats recommendations for their own product marketing strategies.

The data was bias because the sample was about 33 participants. The analysis was focused on smart devices and the data was only tracking information given by users who had a Fitbit. Currently Fitbit has many competitions and different electronics that help users track their health.

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

• The HR dataset is a collection of employee data that includes information on various factors that may impact employee performance. To explore the employee performance factors using Python, we begin by importing the necessary libraries such as Pandas, NumPy, and Matplotlib, then load the HR dataset into a Pandas DataFrame and perform basic data cleaning and preprocessing steps such as handling missing values and checking for duplicates.

• The dataset also use various data visualization to explore the relationships between different variables and employee performance. For example, scatterplots to examine the relationship between job satisfaction and performance ratings, or bar charts to compare the average performance ratings across different gender or positions.

Context

This dataset is about Grand City Games. It helps people to analyze the game easily. The dataset has 52099 rows and 16 columns, and there are no missing values. Grand City is a famous open-world game. With this dataset, you can explore game details, find patterns, and understand the Grand City Games world through data.

Source

This dataset comes from Grand City Games and is created to make game data easy and useful for analysis.

Column Descriptions

• ID: Unique Number.
• Review: Players Feedback.
• Created: Data when entry was made.
• Voted Up: Positive Vote.
• Comment count: Number of comments count. ## Acknowledgement

This dataset is created to be easy to understand and useful for people who want to explore and analyze game data.

eBird data is surveyed per Caterpillars Count circle so it is easy to visualize patterns with the arthropod data. More birds were found near trees without arthropods on this particular day. It would be interesting to see if this pattern is consistent over the season or if this date may be an outlier because it is the last day of the season. Are you completing Caterpillars Count with your organization or community group? Try this method with eBird and see what patterns you find on your site! Send an email to info@ecospark.ca if you are interested in creating maps or learning more about Caterpillars Count and eBird.Caterpillars Count: https://caterpillarscount.unc.edu/ eBird: https://ebird.org/home

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.

AI in Data Visualization Market Outlook

According to our latest research, the global AI in Data Visualization market size reached $3.8 billion in 2024, demonstrating robust growth as organizations increasingly leverage artificial intelligence to enhance data-driven decision-making. The market is forecasted to expand at a CAGR of 21.1% from 2025 to 2033, reaching an estimated $26.6 billion by 2033. This exceptional growth is fueled by the rising demand for actionable insights, the proliferation of big data, and the integration of AI technologies to automate and enrich data visualization processes across industries.

A primary growth factor in the AI in Data Visualization market is the exponential increase in data generation from various sources, including IoT devices, social media platforms, and enterprise systems. Organizations face significant challenges in interpreting complex datasets, and AI-powered visualization tools offer a solution by transforming raw data into intuitive, interactive visual formats. These solutions enable businesses to quickly identify trends, patterns, and anomalies, thereby improving operational efficiency and strategic planning. The integration of AI capabilities such as natural language processing, machine learning, and automated analytics further enhances the value proposition, allowing users to generate dynamic visualizations with minimal technical expertise.

Another significant driver is the growing adoption of business intelligence and analytics platforms across diverse sectors such as BFSI, healthcare, retail, and manufacturing. As competition intensifies and consumer expectations evolve, enterprises are prioritizing data-driven decision-making to gain a competitive edge. AI in data visualization solutions empower users at all organizational levels to interact with data in real-time, uncover hidden insights, and make informed decisions rapidly. The shift towards self-service analytics, where non-technical users can generate their own reports and dashboards, is accelerating the uptake of AI-driven visualization tools. This democratization of data access is expected to continue propelling the market forward.

The rapid advancements in cloud computing and the increasing adoption of cloud-based analytics platforms are also contributing to the growth of the AI in Data Visualization market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling organizations to process and visualize vast volumes of data without substantial infrastructure investments. Additionally, cloud-based solutions facilitate seamless integration with other enterprise applications and data sources, supporting real-time analytics and collaboration across geographically dispersed teams. As more organizations transition to hybrid and multi-cloud environments, the demand for AI-powered visualization tools that can operate efficiently in these settings is poised to surge.

From a regional perspective, North America currently dominates the AI in Data Visualization market due to the presence of leading technology providers, high digital adoption rates, and significant investments in AI and analytics. However, the Asia Pacific region is anticipated to witness the fastest growth over the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing awareness of the benefits of AI-driven data visualization. Europe is also expected to see substantial adoption, particularly in industries such as finance, healthcare, and manufacturing, where regulatory compliance and data-driven strategies are critical. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually embracing these technologies as digital transformation initiatives gain momentum.

Component Analysis

The Component segment of the AI in Data Visualization market is bifurcated into Software and Services, each playing a pivotal role in shaping the industry landscape. Software solutions encompass a wide array of platforms and tools that leverage AI algorithms to automate, enhance, and personalize data visualization. These solutions are designed to cater to varying business needs, from simple dashboard creation to advanced predictive analytics and real-time data exploration. The software segment is witnessing rapid innovation, with vendors continuously integrating new AI capabilities such as natural language queries, automated anomaly detection, and adaptive visualization techniques. This has significantly reduced the learning

Common tern tracking data for analysis in R via the momentumHMM package:*REQUIRES R statistical software which is freely available here: https://cran.r-project.org. The package and data analysis are all within the R statistical framework.For information: dcatlin@vt.edu, reference COTE tracking project # R version 4.3.3 "Angel Food Cake".These are tracking data collected from 18 common terns that were nesting on the South Island of the HRBT tunnel. For full description of the model and the package, see the publication.Also see momentuHMM vignette:https://cran.r-project.org/web/packages/momentuHMM/vignettes/momentuHMM.pdfAlso see:McClintock, BT, T Michelot. 2018. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution 9: 1518–1530. Doi: 10.1111/2041-210X.12995Common tern tracking repeatability data:*REQUIRES R statistical software which is freely available here: https://cran.r-project.org. The package and data analysis are all within the R statistical framework.Data used for repeatability analysis. We quantified the proportion of the total variation in space associated with the Foraging state that was explained by within-individual level variation relative to among-individual variation. We used a nested, generalized linear mixed effects model (GLMM) to decompose the spatial variance of all model-assigned foraging locations into variance components attributed to variation within and among individuals at four levels.We specified this GLMM within R with the package ‘jagsUI’ to call JAGS. For each model, we generated posterior distributions from four chains of 50,000 iterations (thin = 2) with additional adapt and burn-in periods of 25,000 iterations each.Citation for the method used:Wolak, M.E., D.J. Fairbairn, and Y.R. Paulsen. 2012. Guidelines for estimating repeatability. Methods in Ecology and Evolution 3: 129–137.Analysis code for COTE movement study:This information can be found as supplemental materials to the manuscript.For information: dcatlin@vt.edu, reference COTE tracking project # R version 4.3.3 "Angel Food Cake".These are tracking data collected from 18 common terns that were nesting on the South Island of the HRBT tunnel.Full description of the model and the package. Also see momentuHMM vignette.R package for generalized hidden Markov models of animal movement.Required packages. Install prior to running:install.packages('momentuHMM')install.packages('jagsUI')library(momentuHMM)library(jagsUI)

Set Visualization Tools Market Outlook

According to our latest research, the global set visualization tools market size reached USD 3.2 billion in 2024, driven by the increasing demand for advanced data analytics and visual representation across diverse industries. The market is expected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 9.1 billion by 2033. This significant growth is primarily attributed to the proliferation of big data, the rising importance of data-driven decision-making, and the expansion of digital transformation initiatives worldwide.

One of the primary growth factors fueling the set visualization tools market is the exponential surge in data generation from numerous sources, including IoT devices, enterprise applications, and digital platforms. Organizations are increasingly seeking efficient ways to interpret complex and voluminous datasets, making advanced visualization tools indispensable for extracting actionable insights. The integration of artificial intelligence (AI) and machine learning (ML) into these tools further enhances their capability to identify patterns, trends, and anomalies, thus supporting more informed strategic decisions. As businesses across sectors recognize the value of data visualization in driving operational efficiency and innovation, the adoption of set visualization tools continues to accelerate.

Another key driver is the growing emphasis on business intelligence (BI) and analytics within enterprises of all sizes. Modern set visualization tools are evolving to offer intuitive interfaces, real-time analytics, and seamless integration with existing IT infrastructure, making them accessible to non-technical users as well. This democratization of data analytics empowers a broader range of stakeholders to participate in data-driven processes, fostering a culture of collaboration and agility. Additionally, the increasing complexity of datasets, especially in sectors like healthcare, finance, and scientific research, necessitates sophisticated visualization solutions capable of handling multidimensional and hierarchical data structures.

The rapid adoption of cloud computing and the shift towards remote and hybrid work environments have also played a pivotal role in the expansion of the set visualization tools market. Cloud-based deployment models offer unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations to access visualization capabilities without significant upfront investments in hardware or infrastructure. Furthermore, the emergence of mobile and web-based visualization platforms ensures that users can interact with data visualizations anytime, anywhere, thereby enhancing productivity and decision-making speed. As digital transformation initiatives gain momentum globally, the demand for advanced, user-friendly, and scalable set visualization tools is expected to remain strong.

From a regional perspective, North America currently dominates the set visualization tools market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology companies, a mature IT infrastructure, and high investment in analytics and business intelligence solutions contribute to North America's leadership position. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding enterprise IT budgets, and increasing awareness about the benefits of data visualization. As emerging economies in Latin America and the Middle East & Africa continue to invest in digital transformation, these regions are also expected to offer lucrative growth opportunities for market players over the forecast period.

Component Analysis

The set visualization tools market by component is primarily segmented into software and services, each playing a crucial role in the overall ecosystem. The software segment holds the majority share, driven by the continuous evolution of visualization platforms

Pre, post and rerefy statisticsAll coding and markdown, summary statistics for each stage of data manipulation for biofilm taxonomic composition and all raw data.

This dataset contains data collected during a study "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries" conducted by Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

It is being made public both to act as supplementary data for "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries", Government Information Quarterly*, and in order for other researchers to use these data in their own work.

Methodology

The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems.

This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.

Description of the data in this data set

The file "OpenDataIndex_2013_2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:

1) Global Open Data Index (GODI) (4 editions)

2) Open Data Maturity Report (ODMR) (8 editions)

3) Open Data Inventory (ODIN) (6 editions)

4) Open Data Barometer (ODB) (5 editions)

5) Open, Useful and Re-usable data (OURdata) Index (3 editions)

6) Open Government Development Index (OGDI) (2 editions)

These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.

Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.

Format of the file.xls, .csv (for the first spreadsheet only)

Licenses or restrictionsCC-BY

For more info, see README.txt

Integrity Data Visualization for Oil and Gas Market Outlook

According to our latest research, the global market size for Integrity Data Visualization for Oil and Gas reached USD 1.98 billion in 2024, advancing at a robust CAGR of 10.6% during the forecast period. The market is projected to reach USD 5.04 billion by 2033. This impressive growth is primarily driven by increasing digital transformation initiatives, stringent regulatory requirements, and the urgent need for real-time decision-making across the oil and gas sector. The adoption of advanced data visualization tools is enabling organizations to enhance operational efficiency, proactively manage asset integrity, and minimize risks associated with complex oil and gas infrastructures.

The Integrity Data Visualization for Oil and Gas Market is experiencing significant traction due to the rising complexity of oil and gas operations and the critical need for proactive asset management. As oil and gas infrastructure ages, the risk of failures and accidents escalates, compelling companies to invest in sophisticated visualization solutions that provide actionable insights from vast and disparate data sources. These solutions enable operators to monitor the health and performance of pipelines, refineries, and production assets in real time, facilitating predictive maintenance and reducing unplanned downtime. The integration of IoT devices and sensors further amplifies the volume of data generated, necessitating robust visualization platforms that can synthesize and present information in an intuitive, actionable format. This trend is particularly pronounced in regions with mature oil and gas assets, where the cost of failure can be catastrophic both financially and environmentally.

Another key growth driver for the Integrity Data Visualization for Oil and Gas Market is the increasing regulatory scrutiny and compliance requirements imposed by governments and industry bodies worldwide. Regulations governing pipeline integrity, environmental protection, and occupational safety are becoming more stringent, compelling oil and gas companies to adopt advanced monitoring and reporting tools. Data visualization platforms are instrumental in helping organizations track compliance metrics, document inspection and maintenance activities, and generate audit-ready reports. By automating these processes, companies can not only ensure compliance but also streamline operations and reduce administrative overhead. The ability to demonstrate transparency and accountability through clear, visual data representations is becoming a competitive differentiator in the industry.

Technological advancements such as artificial intelligence, machine learning, and cloud computing are further propelling the Integrity Data Visualization for Oil and Gas Market. These technologies enhance the capability of visualization tools to analyze large datasets, identify patterns, and predict potential failures before they occur. Cloud-based solutions, in particular, offer scalability, flexibility, and cost-effectiveness, making advanced data visualization accessible to organizations of all sizes. The convergence of these technologies is enabling oil and gas companies to move beyond reactive maintenance to a predictive and prescriptive approach, ultimately improving asset reliability and reducing operational costs. This shift is fostering a culture of data-driven decision-making across the industry, positioning data visualization as a cornerstone of digital transformation strategies.

The concept of the Digital Oilfield is revolutionizing the oil and gas industry by integrating advanced technologies to enhance operational efficiency and productivity. By leveraging digital tools, companies can optimize exploration and production processes, reduce costs, and improve safety. The Digital Oilfield encompasses a range of technologies, including data analytics, IoT, and automation, which work together to provide real-time insights into operations. This integration allows for better decision-making, predictive maintenance, and streamlined workflows. As the industry continues to embrace digital transformation, the Digital Oilfield is becoming a critical component in achieving sustainable growth and competitive advantage.

From a regional perspective, North America currently leads the Integrity Data Visualization for Oil and Gas Marke

The global Data Lake Visualization market is poised for significant expansion, projected to reach an estimated value of $5,200 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 19.5% anticipated throughout the forecast period from 2025 to 2033. This substantial growth is fueled by the escalating volume of data generated across industries and the increasing need for organizations to derive actionable insights from these vast datasets. Enterprises, particularly large corporations and Small and Medium-sized Enterprises (SMEs), are actively adopting data lake visualization solutions to gain a comprehensive understanding of their data, identify patterns, predict trends, and ultimately make data-driven decisions. The shift towards cloud-based solutions is a prominent trend, offering scalability, flexibility, and cost-efficiency, further accelerating market adoption. On-premises solutions will continue to hold relevance for organizations with stringent data governance and security requirements, but the momentum clearly favors cloud deployments. Key drivers underpinning this market surge include the burgeoning demand for advanced analytics, the rise of big data technologies, and the continuous innovation in visualization tools and platforms. Companies like Huawei, Amazon, Google, Tencent, Alibaba, IBM, Baidu, Microsoft, Databricks, Tableau, and Datamatics are at the forefront, offering a diverse range of solutions that cater to varied business needs. The market is characterized by intense competition, pushing vendors to innovate and enhance their offerings with features like real-time analytics, AI-powered insights, and seamless integration with existing data infrastructure. Geographically, North America and Asia Pacific are expected to lead the market, driven by early adoption of advanced technologies and a strong presence of key market players. Europe also represents a significant market, with a growing emphasis on data analytics for business optimization and regulatory compliance. While the market is on an upward trajectory, challenges such as data governance complexities, the need for skilled personnel, and integration issues with legacy systems may pose some restraints, although these are being actively addressed by technological advancements and strategic partnerships. This report offers an in-depth analysis of the global Data Lake Visualization market, spanning the Study Period from 2019 to 2033, with a Base Year and Estimated Year of 2025, and a Forecast Period from 2025 to 2033. The Historical Period covers 2019-2024. We delve into the intricate dynamics, market segmentation, and future trajectory of this rapidly evolving sector. The report aims to provide stakeholders with actionable insights, critical trends, and a comprehensive understanding of the forces shaping the data lake visualization landscape, projected to reach over $700 million in market value by 2033.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

The PatentsView API is intended to inspire the exploration and enhanced understanding of US intellectual property (IP) and innovation systems. The database driving the API is regularly updated and integrates the best available tools for inventor disambiguation and data quality control. We hope researchers and developers alike will explore the API to discover people and companies and to visualize trends and patterns across the US innovation landscape.

Adequate sleep is crucial during childhood for metabolic health, and physical and cognitive development. Inadequate sleep can disrupt metabolic homeostasis and alter sleeping energy expenditure (SEE). Functional data analysis methods were applied to SEE data to elucidate the population structure of SEE and to discriminate SEE between obese and non-obese children. Minute-by-minute SEE in 109 children, ages 5–18, was measured in room respiration calorimeters. A smoothing spline method was applied to the calorimetric data to extract the true smoothing function for each subject. Functional principal component analysis was used to capture the important modes of variation of the functional data and to identify differences in SEE patterns. Combinations of functional principal component analysis and classifier algorithm were used to classify SEE. Smoothing effectively removed instrumentation noise inherent in the room calorimeter data, providing more accurate data for analysis of the dynamics of SEE. SEE exhibited declining but subtly undulating patterns throughout the night. Mean SEE was markedly higher in obese than non-obese children, as expected due to their greater body mass. SEE was higher among the obese than non-obese children (p0.1, after post hoc testing). Functional principal component scores for the first two components explained 77.8% of the variance in SEE and also differed between groups (p = 0.037). Logistic regression, support vector machine or random forest classification methods were able to distinguish weight-adjusted SEE between obese and non-obese participants with good classification rates (62–64%). Our results implicate other factors, yet to be uncovered, that affect the weight-adjusted SEE of obese and non-obese children. Functional data analysis revealed differences in the structure of SEE between obese and non-obese children that may contribute to disruption of metabolic homeostasis.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.