The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from their ever-expanding datasets. The market, currently estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several factors, including the rising adoption of big data analytics, the proliferation of cloud-based solutions offering enhanced accessibility and scalability, and the growing demand for data-driven decision-making across diverse industries like finance, healthcare, and retail. The market is segmented by application (large enterprises and SMEs) and type (graphical and non-graphical tools), with graphical tools currently holding a larger market share due to their user-friendly interfaces and ability to effectively communicate complex data patterns. Large enterprises are currently the dominant segment, but the SME segment is anticipated to experience faster growth due to increasing affordability and accessibility of EDA solutions. Geographic expansion is another key driver, with North America currently holding the largest market share due to early adoption and a strong technological ecosystem. However, regions like Asia-Pacific are exhibiting high growth potential, fueled by rapid digitalization and a burgeoning data science talent pool. Despite these opportunities, the market faces certain restraints, including the complexity of some EDA tools requiring specialized skills and the challenge of integrating EDA tools with existing business intelligence platforms. Nonetheless, the overall market outlook for EDA tools remains highly positive, driven by ongoing technological advancements and the increasing importance of data analytics across all sectors. The competition among established players like IBM Cognos Analytics and Altair RapidMiner, and emerging innovative companies like Polymer Search and KNIME, further fuels market dynamism and innovation.

Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

Single-Cell Data Analysis Software Market Outlook

According to our latest research, the global single-cell data analysis software market size reached USD 424.5 million in 2024. The market is demonstrating a robust upward trajectory, driven by technological advancements and expanding applications across life sciences. The market is projected to grow at a CAGR of 15.9% from 2025 to 2033, reaching an estimated USD 1,483.4 million by 2033. This impressive growth is primarily fueled by the increasing adoption of single-cell sequencing technologies in genomics, transcriptomics, and proteomics research, as well as the expanding demand from pharmaceutical and biotechnology companies for advanced data analytics solutions.

One of the primary growth factors for the single-cell data analysis software market is the rapid evolution and adoption of high-throughput single-cell sequencing technologies. Over the past decade, there has been a significant shift from bulk cell analysis to single-cell approaches, allowing researchers to unravel cellular heterogeneity with unprecedented resolution. This transition has generated massive volumes of complex data, necessitating sophisticated software tools for effective analysis, visualization, and interpretation. The need to extract actionable insights from these intricate datasets is compelling both academic and commercial entities to invest in advanced single-cell data analysis software, thus propelling market expansion.

Another major driver is the expanding application scope of single-cell data analysis across various omics fields, including genomics, transcriptomics, proteomics, and epigenomics. The integration of these multi-omics datasets is enabling deeper insights into disease mechanisms, biomarker discovery, and personalized medicine. Pharmaceutical and biotechnology companies are increasingly leveraging single-cell data analysis software to accelerate drug discovery and development processes, optimize clinical trials, and identify novel therapeutic targets. The continuous innovation in algorithms, machine learning, and artificial intelligence is further enhancing the capabilities of these software solutions, making them indispensable tools in modern biomedical research.

Single-cell Analysis is revolutionizing the field of life sciences by providing unprecedented insights into cellular diversity and function. This cutting-edge approach allows researchers to study individual cells in isolation, revealing intricate details about their genetic, transcriptomic, and proteomic profiles. By focusing on single cells, scientists can uncover rare cell types and understand complex biological processes that were previously masked in bulk analyses. The ability to perform Single-cell Analysis is transforming our understanding of diseases, enabling the identification of novel biomarkers and therapeutic targets, and paving the way for personalized medicine.

The surge in government and private funding for single-cell research, coupled with the rising prevalence of chronic and infectious diseases, is also contributing to market growth. Governments worldwide are launching initiatives to support precision medicine and genomics research, fostering collaborations between academic institutions and industry players. This supportive ecosystem is not only stimulating the development of new single-cell technologies but also driving the adoption of specialized data analysis software. Moreover, the increasing awareness of the importance of data reproducibility and standardization is prompting the adoption of advanced software platforms that ensure robust, scalable, and reproducible analysis workflows.

From a regional perspective, North America continues to dominate the single-cell data analysis software market, attributed to its strong research infrastructure, presence of leading biotechnology and pharmaceutical companies, and substantial funding for genomics research. However, the Asia Pacific region is emerging as a significant growth engine, driven by increasing investments in life sciences, growing collaborations between academia and industry, and the rapid adoption of advanced sequencing technologies. Europe also holds a considerable share, supported by robust research activities and supportive regulatory frameworks. The market landscape in Latin America and the Middle East & Africa r

The global Cloud-Based Data Analytics Platform market is poised for significant expansion, projected to reach a substantial market size of $150 billion by 2025, exhibiting a robust Compound Annual Growth Rate (CAGR) of 18% throughout the forecast period of 2025-2033. This impressive growth trajectory is fueled by an increasing reliance on data-driven decision-making across all industries. Key drivers include the escalating volume and complexity of data, the growing demand for real-time insights to gain a competitive edge, and the inherent scalability and cost-effectiveness offered by cloud platforms compared to on-premise solutions. Businesses are increasingly leveraging these platforms to extract actionable intelligence from their data, enabling them to optimize operations, enhance customer experiences, and identify new revenue streams. The democratization of data analytics tools, with user-friendly interfaces and advanced AI/ML capabilities, is further accelerating adoption among small and medium-sized enterprises, broadening the market's reach and impact. The market landscape is characterized by a dynamic interplay of technological advancements and evolving business needs. Major trends include the proliferation of hybrid and multi-cloud strategies, offering organizations greater flexibility and control over their data. Advancements in AI and machine learning are deeply integrated into these platforms, enabling more sophisticated predictive analytics, natural language processing for query simplification, and automated insights. The emphasis on data governance, security, and compliance in cloud environments is also a critical consideration, with vendors investing heavily in robust security features. While the market experiences immense growth, potential restraints such as data privacy concerns, vendor lock-in anxieties, and the need for skilled personnel to manage and interpret complex data sets present challenges. However, the overwhelming benefits of enhanced agility, improved collaboration, and reduced IT infrastructure costs continue to drive strong market momentum, with platforms like those offered by industry leaders such as Amazon, Google, Microsoft, and Snowflake dominating the competitive arena. This comprehensive report provides an in-depth analysis of the global Cloud-Based Data Analytics Platform market, forecasting its trajectory from 2019 to 2033, with a base year of 2025. The study delves into the market's intricate dynamics, exploring its growth drivers, challenges, and emerging trends, while also providing valuable insights into its competitive landscape and key regional contributions. The estimated market size is expected to reach $XX million by 2025, with significant growth projected during the forecast period.

Proteomics Data Analysis AI Market Outlook

According to our latest research, the global Proteomics Data Analysis AI market size reached USD 1.48 billion in 2024, reflecting robust adoption across the life sciences sector. The market is poised for exceptional expansion, with a projected CAGR of 31.7% from 2025 to 2033. By 2033, the market is forecasted to attain a valuation of USD 15.42 billion. This extraordinary growth is primarily driven by the increasing integration of artificial intelligence in proteomics research, enabling faster, more accurate, and cost-effective analysis of complex biological data. As per our latest findings, the demand for advanced AI-driven proteomics data analysis solutions is surging, propelled by the need for personalized medicine, accelerated drug discovery, and the growing prevalence of chronic diseases globally.

One of the most significant growth factors for the Proteomics Data Analysis AI market is the exponential rise in proteomics research, which is generating vast and intricate datasets that traditional analysis methods struggle to interpret efficiently. AI-powered analytical platforms are revolutionizing the way researchers extract meaningful insights from proteomic data, enabling the identification of novel biomarkers, elucidation of disease pathways, and optimization of therapeutic targets. The increasing adoption of next-generation sequencing and mass spectrometry technologies in both academic and commercial settings is further amplifying the demand for AI-driven analytics. These technological advancements are not only improving the sensitivity and specificity of proteomics data analysis but are also reducing turnaround times, making high-throughput analysis more accessible and scalable for a wider range of applications.

Another crucial driver for the Proteomics Data Analysis AI market is the rapid growth of personalized medicine and precision healthcare initiatives. As healthcare systems worldwide shift towards individualized treatment approaches, the need for comprehensive proteomic profiling and data interpretation has never been greater. AI algorithms are uniquely positioned to integrate multi-omics data, including genomics, transcriptomics, and proteomics, to generate actionable insights for patient stratification, prognosis, and therapy selection. Pharmaceutical and biotechnology companies are increasingly leveraging these advanced analytics to accelerate drug discovery, optimize clinical trial design, and enhance the safety and efficacy of novel therapeutics. The synergy between AI and proteomics is thus playing a pivotal role in transforming the landscape of modern medicine and driving sustained market growth.

The Proteomics Data Analysis AI market is also benefiting from substantial investments by governments, research institutions, and private sector players in life sciences and digital health infrastructure. Increased funding for large-scale proteomics projects, coupled with the proliferation of cloud computing and high-performance computing resources, is lowering the barriers to entry for AI-driven analytics. Collaborations between technology providers, academic institutions, and healthcare organizations are fostering innovation and accelerating the development of next-generation proteomics platforms. However, the market also faces challenges related to data standardization, interoperability, and regulatory compliance, which must be addressed to fully realize the potential of AI in proteomics. Nevertheless, the overall outlook remains highly positive, with continuous advancements in machine learning algorithms and data integration techniques expected to further propel market expansion.

Mass Spectrometry Data Analysis AI is becoming a cornerstone in the field of proteomics, offering unparalleled precision and depth in the analysis of complex biological samples. With the integration of AI, mass spectrometry data is transformed into actionable insights, enabling researchers to decode intricate protein structures and interactions with unprecedented accuracy. This technological synergy is not only enhancing the sensitivity and specificity of proteomics studies but also facilitating the discovery of novel biomarkers and therapeutic targets. As AI algorithms continue to evolve, they are increasingly capable of handling the vast datasets generated by mass spectrometry, streamlining workflows and reducing the time requi

Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

Mass Spectrometry Data Analysis AI Market Outlook

According to our latest research, the global Mass Spectrometry Data Analysis AI market size reached USD 1.45 billion in 2024, reflecting an impressive surge in adoption across life sciences, healthcare, and environmental sectors. The market is projected to grow at a robust CAGR of 21.6% from 2025 to 2033, reaching a forecasted value of USD 10.23 billion by 2033. This significant growth is primarily driven by the increasing demand for high-throughput, accurate, and automated data analysis solutions in mass spectrometry, coupled with the rapid integration of artificial intelligence (AI) technologies to enhance data interpretation, reduce turnaround time, and improve reproducibility.

One of the primary growth factors for the Mass Spectrometry Data Analysis AI market is the exponential increase in the volume and complexity of data generated by advanced mass spectrometry instruments. Traditional data analysis methods often struggle to keep pace with the high throughput and intricate datasets produced, especially in fields such as proteomics and metabolomics. The integration of AI-powered algorithms enables researchers and clinicians to automate data processing, identify complex patterns, and extract actionable insights with unprecedented speed and accuracy. This technological leap is particularly beneficial in drug discovery and clinical diagnostics, where timely and reliable data interpretation can accelerate research timelines and improve patient outcomes. The ability of AI to handle multi-dimensional datasets and provide real-time analysis is revolutionizing how mass spectrometry data is utilized across various industries.

Another critical driver fueling the growth of the Mass Spectrometry Data Analysis AI market is the increasing investment by pharmaceutical and biotechnology companies in precision medicine and personalized healthcare. As these industries strive for more targeted therapies and diagnostics, the need for sophisticated data analysis solutions that can interpret complex biological data has become paramount. AI-driven platforms are being deployed to streamline workflows, reduce manual errors, and enhance the reproducibility of results. Additionally, the rise of cloud-based solutions is making AI-powered mass spectrometry data analysis more accessible to organizations of all sizes, eliminating the need for extensive on-premises infrastructure and enabling collaborative research across global teams. This democratization of advanced analytical capabilities is expected to further propel market expansion in the coming years.

Regulatory compliance and quality assurance requirements also play a pivotal role in shaping the Mass Spectrometry Data Analysis AI market. With stringent guidelines governing data integrity, traceability, and reproducibility, especially in clinical and environmental testing laboratories, AI-based solutions offer robust frameworks for automated quality control and audit trails. These capabilities are crucial for ensuring that analytical results meet regulatory standards and can withstand scrutiny during regulatory submissions. Furthermore, the integration of AI with mass spectrometry data analysis is enabling laboratories to optimize resource allocation, minimize operational costs, and enhance overall productivity. As regulatory agencies continue to emphasize data transparency and reliability, the adoption of AI-driven data analysis tools is expected to become a standard practice across the industry.

From a regional perspective, North America currently dominates the Mass Spectrometry Data Analysis AI market, accounting for more than 38% of the global market share in 2024. This leadership is attributed to the presence of major pharmaceutical companies, advanced research infrastructure, and a strong focus on technological innovation. Europe follows closely, driven by significant investments in life sciences and environmental monitoring. The Asia Pacific region is emerging as a high-growth market, with increasing adoption of AI technologies in research and clinical settings, particularly in countries such as China, Japan, and India. The rapid expansion of healthcare infrastructure, coupled with government initiatives to promote digital transformation, is expected to drive substantial growth in this region over the forecast period.

Component Analysis

The Component segment of the Mass Spectrometry Data Analysis AI ma

In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing and modeling such data. The approach of FDA is to assume that data are continuous functions and that each continuous function is considered as a single observation. Thus, FDA deals with large-scale and complex data. However, visualization and exploratory data analysis, which are very important in practice, can be challenging due to the complexity of the continuous functions. Here we introduce a type of record concept for functional data, and we propose some nonparametric tools based on the record concept for functional data observed over time (functional time series). We study the properties of the trajectory of the number of record curves under different scenarios. Also, we propose a unit root test based on the number of records. The trajectory of the number of records over time and the unit root test can be used for visualization and exploratory data analysis. We illustrate the advantages of our proposal through a Monte Carlo simulation study. We also illustrate our method on two different datasets: Daily wind speed curves at Yanbu, Saudi Arabia and annual mortality rates in France. Overall, we can identify the type of functional time series being studied based on the number of record curves observed. Supplementary materials for this article are available online.

· Financial expenses1 dataset: This dataset consists of simulated event logs generated from the financial expense data analysis process model. Each trace provides a detailed description of the process of analyzing office expense data. · Financial expenses2 dataset: This dataset consists of simulated event logs generated from the travel expense data analysis process model. Each trace provides a detailed description of the process of analyzing travel expense data. · Financial expenses3 dataset: This dataset consists of simulated event logs generated from the sales expense data analysis process model. Each trace provides a detailed description of the process of analyzing sales expense data. · Financial expenses4 dataset: This dataset consists of simulated event logs generated from the management expense data analysis process model. Each trace provides a detailed description of the process of analyzing management expense data. · Financial expenses5 dataset: This dataset consists of simulated event logs generated from the manufacturing expense data analysis process model. Each trace provides a detailed description of the process of analyzing manufacturing expense data. · Financial expenses6 dataset: This dataset consists of simulated event logs generated from the financial statement data analysis process model. Each trace provides a detailed description of the process of analyzing financial statement data.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Learning Data Visualization Tools Market Outlook

According to our latest research, the global Learning Data Visualization Tools market size reached USD 1.98 billion in 2024, reflecting the rapid adoption of innovative visualization platforms in education and corporate sectors. The market is expected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching an anticipated value of USD 8.45 billion by 2033. This growth is fueled by increasing demand for data-driven decision-making, the proliferation of e-learning, and the integration of artificial intelligence into visualization tools, which together are transforming how learners and professionals interact with complex data.

The primary growth driver for the Learning Data Visualization Tools market is the exponential rise in data generation across industries and academia. As organizations and educational institutions generate massive datasets, the need for intuitive, interactive visualization tools becomes paramount for extracting actionable insights. These tools not only simplify data interpretation but also enhance learning outcomes by enabling users to visually explore patterns and trends. The shift towards remote learning and digital classrooms, especially post-pandemic, has accelerated the adoption of these platforms, making them indispensable for both educators and learners striving for enhanced engagement and comprehension.

Another significant factor propelling market expansion is the increasing integration of advanced technologies such as artificial intelligence, machine learning, and natural language processing into data visualization tools. These innovations are making visualization platforms smarter and more adaptable, offering personalized learning experiences and automating complex data analyses. Additionally, the growing emphasis on upskilling and reskilling in the workforce is driving professionals and enterprises to adopt learning data visualization tools for corporate training and self-learning. The ability to customize dashboards, automate reporting, and provide real-time feedback further increases the value proposition for both individual and enterprise users.

Visual Analytics is playing a pivotal role in the evolution of learning data visualization tools. By combining data analysis with interactive visual representations, visual analytics enhances the ability of educators and learners to interpret complex datasets. This approach not only simplifies the understanding of intricate data patterns but also facilitates more informed decision-making processes in educational and corporate settings. As the demand for data-driven insights grows, visual analytics is increasingly being integrated into learning platforms, offering users a more intuitive and engaging experience. This integration is particularly beneficial in environments where quick, actionable insights are crucial for adapting to dynamic learning needs.

The market is also benefiting from the democratization of data literacy, as more organizations recognize the strategic importance of empowering employees and students with data interpretation skills. Educational institutions are increasingly embedding data visualization modules into their curricula, while enterprises are investing in platforms that facilitate collaborative learning and cross-functional data sharing. The proliferation of cloud-based solutions has further reduced barriers to entry, enabling small and medium-sized enterprises (SMEs) and educational startups to access powerful visualization tools without substantial upfront investments. This widespread accessibility is fostering a vibrant ecosystem of users and developers, driving continuous innovation and market growth.

From a regional perspective, North America currently dominates the Learning Data Visualization Tools market owing to its advanced educational infrastructure, high digital adoption rates, and strong presence of leading technology vendors. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing investments in digital education, rapid urbanization, and a burgeoning population of tech-savvy students and professionals. Europe remains a significant market, characterized by robust government initiatives to promote digital literacy and lifelong learning. Meanwhile, Latin America and the Mi

Graph Analytics Platform Market Outlook

According to our latest research, the global Graph Analytics Platform market size reached USD 2.3 billion in 2024, reflecting robust momentum in enterprise adoption of advanced analytics solutions. The market is expected to grow at a remarkable CAGR of 27.1% from 2025 to 2033, reaching an estimated USD 19.6 billion by 2033. This surge is driven by the increasing need for real-time insights, complex data relationship analysis, and the growing integration of artificial intelligence and machine learning with graph analytics platforms. The rapid digital transformation across industries and the proliferation of data-intensive applications are key drivers shaping the trajectory of the Graph Analytics Platform market worldwide.

A primary growth factor for the Graph Analytics Platform market is the rising complexity and volume of connected data being generated by organizations. As digital ecosystems expand, the relationships between data points become more intricate, requiring sophisticated tools to uncover patterns, anomalies, and insights. Graph analytics platforms enable enterprises to visualize and analyze these relationships efficiently, facilitating advanced use cases such as fraud detection, network optimization, and recommendation engines. The demand for these platforms is further propelled by the increasing need to derive actionable intelligence from unstructured and semi-structured data sources, such as social networks, IoT devices, and transaction logs. This fundamental shift toward connected data analysis is expected to sustain the market’s upward trajectory over the coming years.

Another significant growth catalyst is the integration of graph analytics with artificial intelligence and machine learning technologies. By leveraging AI/ML algorithms, graph analytics platforms can automate the detection of hidden patterns, predict future outcomes, and enhance decision-making processes. Industries such as BFSI, healthcare, and retail are rapidly adopting these solutions to gain a competitive edge, improve operational efficiency, and personalize customer experiences. The convergence of graph analytics and AI/ML is also driving innovation in areas like cybersecurity, where real-time threat detection and response are crucial. As organizations continue to invest in digital transformation initiatives, the adoption of graph analytics platforms is expected to accelerate, further expanding the market.

The growing emphasis on risk management, regulatory compliance, and fraud prevention is also fueling demand for Graph Analytics Platforms. Organizations, particularly in highly regulated sectors like BFSI and healthcare, are leveraging these platforms to monitor transactions, identify suspicious behavior, and ensure compliance with evolving regulations. The ability to trace data lineage and relationships across complex networks is invaluable for mitigating risks and maintaining data integrity. Additionally, the rise of cloud computing has made graph analytics solutions more accessible, scalable, and cost-effective, enabling businesses of all sizes to harness their benefits. These trends collectively contribute to the sustained growth of the global Graph Analytics Platform market.

From a regional perspective, North America continues to dominate the Graph Analytics Platform market, driven by early technology adoption, a mature IT infrastructure, and significant investments in advanced analytics. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing adoption of cloud-based solutions, and a burgeoning startup ecosystem. Europe also holds a substantial market share, supported by stringent data privacy regulations and the growing demand for innovative analytics tools across various industries. As enterprises worldwide recognize the strategic value of graph analytics, the market is poised for robust expansion across all major regions.

Component Analysis

The Component segment of the Graph Analytics Platform market is primarily divided into Software and Services, each playing a pivotal role in market growth. The software component, which includes graph databases, visualization tools, and analytics engines, constitutes the backbone of the market. Enterprises increasingly rely on advanced software solutions to manage, analyze, and visualize complex data relationships, driving significant investments in this segment. The software segme

The size of the Data Analytics Market market was valued at USD 57.76 billion in 2023 and is projected to reach USD 302.74 billion by 2032, with an expected CAGR of 26.7 % during the forecast period. The data analytics market encompasses tools and technologies that analyze and interpret complex data sets to derive actionable insights. It involves techniques such as data mining, predictive analytics, and statistical analysis, enabling organizations to make informed decisions. Key uses include improving operational efficiency, enhancing customer experiences, and driving strategic planning across industries like healthcare, finance, and retail. Applications range from fraud detection and risk management to marketing optimization and supply chain management. Current trends highlight the growing adoption of artificial intelligence and machine learning for advanced analytics, the rise of real-time data processing, and an increasing focus on data privacy and security. As businesses seek to leverage data for competitive advantage, the demand for analytics solutions continues to grow.

Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

The global Multivariate Analysis Software market is poised for significant expansion, projected to reach an estimated market size of USD 4,250 million in 2025, with a robust Compound Annual Growth Rate (CAGR) of 12.5% anticipated through 2033. This growth is primarily fueled by the increasing adoption of advanced statistical techniques across a wide spectrum of industries, including the burgeoning pharmaceutical sector, sophisticated chemical research, and complex manufacturing processes. The demand for data-driven decision-making, coupled with the ever-growing volume of complex datasets, is compelling organizations to invest in powerful analytical tools. Key drivers include the rising need for predictive modeling in drug discovery and development, quality control in manufacturing, and risk assessment in financial applications. Emerging economies, particularly in the Asia Pacific region, are also contributing to this upward trajectory as they invest heavily in technological advancements and R&D, further amplifying the need for sophisticated analytical solutions. The market is segmented by application into Medical, Pharmacy, Chemical, Manufacturing, and Marketing. The Pharmacy and Medical applications are expected to witness the highest growth owing to the critical need for accurate data analysis in drug efficacy studies, clinical trials, and personalized medicine. In terms of types, the market encompasses a variety of analytical methods, including Multiple Linear Regression Analysis, Multiple Logistic Regression Analysis, Multivariate Analysis of Variance (MANOVA), Factor Analysis, and Cluster Analysis. While advanced techniques like MANOVA and Factor Analysis are gaining traction for their ability to uncover intricate relationships within data, the foundational Multiple Linear and Logistic Regression analyses remain widely adopted. Restraints, such as the high cost of specialized software and the need for skilled personnel to effectively utilize these tools, are being addressed by the emergence of more user-friendly interfaces and cloud-based solutions. Leading companies like Hitachi High-Tech America, OriginLab Corporation, and Minitab are at the forefront, offering comprehensive suites that cater to diverse analytical needs. This report provides an in-depth analysis of the global Multivariate Analysis Software market, encompassing a study period from 2019 to 2033, with a base and estimated year of 2025 and a forecast period from 2025 to 2033, building upon historical data from 2019-2024. The market is projected to witness significant expansion, driven by increasing data complexity and the growing need for advanced analytical capabilities across various industries. The estimated market size for Multivariate Analysis Software is expected to reach $2.5 billion by 2025, with projections indicating a substantial growth to $5.8 billion by 2033, demonstrating a robust compound annual growth rate (CAGR) of approximately 11.5% during the forecast period.

Market Size and Growth: The global Data Visualization and Analysis Platform market is projected to reach $6240.6 million by 2033, exhibiting a CAGR of 8.1% during the forecast period 2023-2033. The increasing adoption of big data and analytics in various industries, the growing need for data visualization for effective decision-making, and government initiatives to promote digital transformation are driving the market growth. Key Trends and Drivers: The market is witnessing key trends such as the shift towards cloud-based platforms, the integration of artificial intelligence (AI) and machine learning (ML) for advanced data analysis capabilities, and the increasing use of visual storytelling to communicate complex data effectively. These advancements enable businesses to gain deeper insights, improve operational efficiency, and drive growth. Additionally, government regulations and standards for data privacy and security are also influencing the adoption of data visualization and analysis platforms.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027. What is Big data? Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big data analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.