This graph presents the results of a survey, conducted by BARC in 2014/15, into the current and planned use of technology for the analysis of big data. At the beginning of 2015, ** percent of respondents indicated that their company was already using a big data analytical appliance for big data.

The Data Analytics Market size was valued at USD 41.05 USD billion in 2023 and is projected to reach USD 222.39 USD billion by 2032, exhibiting a CAGR of 27.3 % during the forecast period. Data Analytics can be defined as the rigorous process of using tools and techniques within a computational framework to analyze various forms of data for the purpose of decision-making by the concerned organization. This is used in almost all fields such as health, money matters, product promotion, and transportation in order to manage businesses, foresee upcoming events, and improve customers’ satisfaction. Some of the principal forms of data analytics include descriptive, diagnostic, prognostic, as well as prescriptive analytics. Data gathering, data manipulation, analysis, and data representation are the major subtopics under this area. There are a lot of advantages of data analytics, and some of the most prominent include better decision making, productivity, and saving costs, as well as the identification of relationships and trends that people could be unaware of. The recent trends identified in the market include the use of AI and ML technologies and their applications, the use of big data, increased focus on real-time data processing, and concerns for data privacy. These developments are shaping and propelling the advancement and proliferation of data analysis functions and uses. Key drivers for this market are: Rising Demand for Edge Computing Likely to Boost Market Growth. Potential restraints include: Data Security Concerns to Impede the Market Progress . Notable trends are: Metadata-Driven Data Fabric Solutions to Expand Market Growth.

This graph shows the tools used by French companies to analyze Big Data in 2016. The results show that almost ** percent of the companies surveyed used Online Analytical Processing engines.

The statistical analysis software market is experiencing robust growth, driven by the increasing volume of data generated across various sectors and the rising need for data-driven decision-making. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors. The adoption of advanced analytics techniques like machine learning and artificial intelligence is significantly boosting demand for sophisticated statistical software capable of handling complex datasets and delivering actionable insights. Furthermore, the growing penetration of cloud-based solutions is enhancing accessibility and scalability, reducing upfront investment costs and facilitating collaboration among users. The expanding application across diverse industries, including healthcare, finance, and research, further contributes to market growth. Key players like The MathWorks, IBM, and SAS Institute are continuously innovating, incorporating advanced functionalities, and expanding their product portfolios to cater to evolving market needs. However, the market is not without its challenges. The high cost of advanced statistical software can be a barrier to entry for smaller organizations. Additionally, the need for specialized expertise in statistical analysis and data interpretation can limit the widespread adoption of these tools. Despite these restraints, the overall market outlook remains positive, driven by the unrelenting growth of data and the increasing recognition of the value of data-driven insights in optimizing business operations and making strategic decisions. The market is segmented by deployment (cloud, on-premise), application (business intelligence, research & development), and end-user (healthcare, finance, manufacturing). The competitive landscape is characterized by a mix of established players and niche providers, resulting in a dynamic market with continuous innovation and consolidation.

Big Data Analysis Platform Market Outlook

The global market size for Big Data Analysis Platforms is projected to grow from USD 35.5 billion in 2023 to an impressive USD 110.7 billion by 2032, reflecting a CAGR of 13.5%. This substantial growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, the rapid proliferation of IoT devices, and the ever-growing volumes of data generated globally.

One of the primary growth factors for the Big Data Analysis Platform market is the escalating need for businesses to derive actionable insights from complex and voluminous datasets. With the advent of technologies such as artificial intelligence and machine learning, organizations are increasingly leveraging big data analytics to enhance their operational efficiency, customer experience, and competitiveness. The ability to process vast amounts of data quickly and accurately is proving to be a game-changer, enabling businesses to make more informed decisions, predict market trends, and optimize their supply chains.

Another significant driver is the rise of digital transformation initiatives across various sectors. Companies are increasingly adopting digital technologies to improve their business processes and meet changing customer expectations. Big Data Analysis Platforms are central to these initiatives, providing the necessary tools to analyze and interpret data from diverse sources, including social media, customer transactions, and sensor data. This trend is particularly pronounced in sectors such as retail, healthcare, and BFSI (banking, financial services, and insurance), where data analytics is crucial for personalizing customer experiences, managing risks, and improving operational efficiencies.

Moreover, the growing adoption of cloud computing is significantly influencing the market. Cloud-based Big Data Analysis Platforms offer several advantages over traditional on-premises solutions, including scalability, flexibility, and cost-effectiveness. Businesses of all sizes are increasingly turning to cloud-based analytics solutions to handle their data processing needs. The ability to scale up or down based on demand, coupled with reduced infrastructure costs, makes cloud-based solutions particularly appealing to small and medium-sized enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure.

Data Science and Machine-Learning Platforms play a pivotal role in the evolution of Big Data Analysis Platforms. These platforms provide the necessary tools and frameworks for processing and analyzing vast datasets, enabling organizations to uncover hidden patterns and insights. By integrating data science techniques with machine learning algorithms, businesses can automate the analysis process, leading to more accurate predictions and efficient decision-making. This integration is particularly beneficial in sectors such as finance and healthcare, where the ability to quickly analyze complex data can lead to significant competitive advantages. As the demand for data-driven insights continues to grow, the role of data science and machine-learning platforms in enhancing big data analytics capabilities is becoming increasingly critical.

From a regional perspective, North America currently holds the largest market share, driven by the presence of major technology companies, high adoption rates of advanced technologies, and substantial investments in data analytics infrastructure. Europe and the Asia Pacific regions are also experiencing significant growth, fueled by increasing digitalization efforts and the rising importance of data analytics in business strategy. The Asia Pacific region, in particular, is expected to witness the highest CAGR during the forecast period, propelled by rapid economic growth, a burgeoning middle class, and increasing internet and smartphone penetration.

Component Analysis

The Big Data Analysis Platform market can be broadly categorized into three components: Software, Hardware, and Services. The software segment includes analytics software, data management software, and visualization tools, which are crucial for analyzing and interpreting large datasets. This segment is expected to dominate the market due to the continuous advancements in analytics software and the increasing need for sophisticated data analysis tools. Analytics software enables organizations to process and analyze data from multiple sources,

Data Analytics in Financial Market Outlook

The global data analytics in financial market size was valued at approximately USD 10.5 billion in 2023 and is projected to reach around USD 34.8 billion by 2032, growing at a robust CAGR of 14.4% during the forecast period. This remarkable growth is driven by the increasing adoption of advanced analytics technologies, the need for real-time data-driven decision-making, and the rising incidence of financial fraud.

One of the primary growth factors for the data analytics in the financial market is the burgeoning volume of data generated from diverse sources such as transactions, social media, and online banking. Financial institutions are increasingly leveraging data analytics to process and analyze this vast amount of data to gain actionable insights. Additionally, technological advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the capabilities of data analytics tools, enabling more accurate predictions and efficient risk management.

Another driving factor is the heightened focus on regulatory compliance and security management. In the wake of stringent regulations imposed by financial authorities globally, organizations are compelled to adopt robust analytics solutions to ensure compliance and mitigate risks. Moreover, with the growing threat of cyber-attacks and financial fraud, there is a heightened demand for sophisticated analytics tools capable of detecting and preventing fraudulent activities in real-time.

Furthermore, the increasing emphasis on customer-centric strategies in the financial sector is fueling the adoption of data analytics. Financial institutions are utilizing analytics to understand customer behavior, preferences, and needs more accurately. This enables them to offer personalized services, improve customer satisfaction, and drive revenue growth. The integration of advanced analytics in customer management processes helps in enhancing customer engagement and loyalty, which is crucial in the competitive financial landscape.

Regionally, North America has been the dominant player in the data analytics in financial market, owing to the presence of major market players, technological advancements, and a high adoption rate of analytics solutions. However, the Asia Pacific region is anticipated to witness the highest growth during the forecast period, driven by the rapid digitalization of financial services, increasing investments in analytics technologies, and the growing focus on enhancing customer experience in emerging economies like China and India.

Component Analysis

In the data analytics in financial market, the components segment is divided into software and services. The software segment encompasses various analytics tools and platforms designed to process and analyze financial data. This segment holds a significant share in the market owing to the continuous advancements in software capabilities and the growing need for real-time analytics. Financial institutions are increasingly investing in sophisticated software solutions to enhance their data processing and analytical capabilities. The software segment is also being propelled by the integration of AI and ML technologies, which offer enhanced predictive analytics and automation features.

On the other hand, the services segment includes consulting, implementation, and maintenance services provided by vendors to help financial institutions effectively deploy and manage analytics solutions. With the rising complexity of financial data and analytics tools, the demand for professional services is on the rise. Organizations are seeking expert guidance to seamlessly integrate analytics solutions into their existing systems and optimize their use. The services segment is expected to grow significantly as more institutions recognize the value of professional support in maximizing the benefits of their analytics investments.

The software segment is further categorized into various types of analytics tools such as descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics tools are used to summarize historical data to identify patterns and trends. Predictive analytics tools leverage historical data to forecast future outcomes, which is crucial for risk management and fraud detection. Prescriptive analytics tools provide actionable recommendations based on predictive analysis, aiding in decision-making processes. The growing need for advanced predictive and prescriptive analytics is driving the demand for specialized software solut

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,

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

Big Data and Business Analytics Market Outlook

In 2023, the global Big Data and Business Analytics market size is estimated to be valued at approximately $274 billion, and with a projected compound annual growth rate (CAGR) of 12.4%, it is anticipated to reach around $693 billion by 2032. This significant growth is driven by the escalating demand for data-driven decision-making processes across various industries, which leverage insights derived from vast data sets to enhance business efficiency, optimize operations, and drive innovation. The increasing adoption of Internet of Things (IoT) devices, coupled with the exponential growth of data generated daily, further propels the need for advanced analytics solutions to harness and interpret this information effectively.

A critical growth factor in the Big Data and Business Analytics market is the increasing reliance on data to gain a competitive edge. Organizations are now more than ever looking to uncover hidden patterns, correlations, and insights from the data they collect to make informed decisions. This trend is especially prominent in industries such as retail, where understanding consumer behavior can lead to personalized marketing strategies, and in healthcare, where data analytics can improve patient outcomes through precision medicine. Moreover, the integration of big data analytics with artificial intelligence and machine learning technologies is enabling more accurate predictions and real-time decision-making, further enhancing the value proposition of these analytics solutions.

Another key driver of market growth is the continuous technological advancements and innovations in data analytics tools and platforms. Companies are increasingly investing in advanced analytics capabilities, such as predictive analytics, prescriptive analytics, and real-time analytics, to gain deeper insights into their operations and market environments. The development of user-friendly and self-service analytics tools is also democratizing data access within organizations, empowering employees at all levels to leverage data in their daily decision-making processes. This democratization of data analytics is reducing the reliance on specialized data scientists, thereby accelerating the adoption of big data analytics across various business functions.

The increasing emphasis on regulatory compliance and data privacy is also driving growth in the Big Data and Business Analytics market. Strict regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, require organizations to manage and analyze data responsibly. This is prompting businesses to invest in robust analytics solutions that not only help them comply with these regulations but also ensure data integrity and security. Additionally, as data breaches and cybersecurity threats continue to rise, organizations are turning to analytics solutions to identify potential vulnerabilities and mitigate risks effectively.

Regionally, North America remains a dominant player in the Big Data and Business Analytics market, benefiting from the presence of major technology companies and a high rate of digital adoption. The Asia Pacific region, however, is emerging as a significant growth area, driven by rapid industrialization, urbanization, and increasing investments in digital transformation initiatives. Europe also showcases a robust market, fueled by stringent data protection regulations and a strong focus on innovation. Meanwhile, the markets in Latin America and the Middle East & Africa are gradually gaining momentum as organizations in these regions are increasingly recognizing the value of data analytics in enhancing business outcomes and driving economic growth.

Component Analysis

The Big Data and Business Analytics market is segmented by components into software, services, and hardware, each playing a crucial role in the ecosystem. Software components, which include data management and analytics tools, are at the forefront, offering solutions that facilitate the collection, analysis, and visualization of large data sets. The software segment is driven by a demand for scalable solutions that can handle the increasing volume, velocity, and variety of data. As organizations strive to become more data-centric, there is a growing need for advanced analytics software that can provide actionable insights from complex data sets, leading to enhanced decision-making capabilities.

In the services segment, businesses are increasingly seeking consultation, implementation, and support services to effective

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

This manual provides guidance on how to create a pair analysis file and on the appropriate weights and design variables needed to analyze pair data, and it provides example code in multiple software packages.

Market Analysis for Data Middle Platform The global data middle platform market size was valued at USD 24.9 billion in 2025 and is anticipated to reach USD 76.1 billion by 2033, exhibiting a CAGR of 15.3% during the forecast period (2025-2033). Key drivers fueling market growth include the increasing adoption of cloud-based solutions, the proliferation of data, and the need for efficient data management. The rising adoption of data analytics and machine learning is also contributing to the demand for data middle platforms. The market is segmented by application (enterprise, municipal, bank, other) and type (local, cloud-based). The cloud-based segment dominates the market due to its cost-effectiveness, scalability, and flexibility. Key players in the market include Guangzhou Guangdian Information Technology Co., Ltd., Shanghai Qianjiang Network Technology Co., Ltd., Tianmian Information Technology (Shenzhen) Co., Ltd., Guangzhou Yunmi Technology Co., Ltd., Spot Technology, Xiamen Meiya Pico Information Co., Ltd., Star Ring Technology, Beijing Jiuqi Software Co., Ltd., LnData, SIE, Yusys Technology, and Sunline. The market is expected to experience significant growth in the Asia Pacific region, particularly in China, India, and Japan, due to the increasing number of data-driven businesses and the government's focus on digital transformation. The data middle platform market is a rapidly growing market, with a global value of $10.5 billion in 2021. The market is projected to grow at a CAGR of 15.7% over the next five years, reaching $24.5 billion by 2026. The growth of the market is being driven by the increasing adoption of data-driven decision-making in enterprises. As businesses become more reliant on data to improve their operations, they are increasingly investing in data middle platforms to manage and analyze their data.

Big Data Platform Software Market Outlook

The global Big Data Platform Software market size was valued at approximately USD 70 billion in 2023 and is projected to reach around USD 250 billion by 2032, growing at a compound annual growth rate (CAGR) of 15%. The substantial growth in this market can be attributed to the increasing volume and complexity of data generated across various industries, along with the rising need for data analytics to drive business decision-making.

One of the key growth factors driving the Big Data Platform Software market is the explosive growth in data generation from various sources such as social media, IoT devices, and enterprise applications. The proliferation of digital devices has led to an unprecedented surge in data volumes, compelling businesses to adopt advanced Big Data solutions to manage and analyze this data effectively. Additionally, advancements in cloud computing have further amplified the capabilities of Big Data platforms, enabling organizations to store and process vast amounts of data in a cost-efficient manner.

Another significant driver of market growth is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. Big Data platforms equipped with AI and ML capabilities can provide valuable insights by analyzing patterns, trends, and anomalies within large datasets. This has been particularly beneficial for industries such as healthcare, finance, and retail, where data-driven decision-making can lead to improved operational efficiency, enhanced customer experiences, and better risk management.

Moreover, the rising demand for real-time data analytics is propelling the growth of the Big Data Platform Software market. Businesses are increasingly seeking solutions that can process and analyze data in real-time to gain immediate insights and respond swiftly to market changes. This demand is fueled by the need for agility and competitiveness, as organizations aim to stay ahead in a rapidly evolving business landscape. The ability to make data-driven decisions in real-time can provide a significant competitive edge, driving further investment in Big Data technologies.

From a regional perspective, North America holds the largest share of the Big Data Platform Software market, driven by the early adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing digital transformation initiatives and the rising awareness about the benefits of Big Data analytics across various industries. Europe also presents significant growth opportunities, driven by stringent data protection regulations and the growing emphasis on data privacy and security.

Component Analysis

The Big Data Platform Software market can be segmented by component into Software and Services. The software segment encompasses the various Big Data platforms and tools that enable data storage, processing, and analytics. This includes data management software, data analytics software, and visualization tools. The demand for Big Data software is driven by the need for organizations to handle large volumes of data efficiently and derive actionable insights from it. With the growing complexity of data, advanced software solutions that offer robust analytics capabilities are becoming increasingly essential.

The services segment includes consulting, implementation, and support services related to Big Data platforms. These services are crucial for the successful deployment and management of Big Data solutions. Consulting services help organizations to design and strategize their Big Data initiatives, while implementation services ensure the seamless integration of Big Data platforms into existing IT infrastructure. Support services provide ongoing maintenance and troubleshooting to ensure the smooth functioning of Big Data systems. The growing adoption of Big Data solutions is driving the demand for these ancillary services, as organizations seek expert guidance to maximize the value of their Big Data investments.

Within the software segment, data analytics software is witnessing significant demand due to its ability to process and analyze large datasets to uncover hidden patterns and insights. This is particularly important for industries such as healthcare, finance, and retail, where data-driven insights can lead to improved decision-making and operational efficiency. Additionally, data management software plays a critical role in ensuring the integrity, securit

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

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Big Data Security Market Size 2025-2029

The big data security market size is forecast to increase by USD 23.9 billion, at a CAGR of 15.7% between 2024 and 2029. Stringent regulations regarding data protection will drive the big data security market.

Major Market Trends & Insights

North America dominated the market and accounted for a 37% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 10.91 billion in 2023
By End-user - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 188.34 billion
Market Future Opportunities: USD USD 23.9 billion 
CAGR : 15.7%
North America: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving landscape, with stringent regulations driving the demand for advanced data protection solutions. As businesses increasingly rely on big data to gain insights and drive growth, the focus on securing this valuable information has become a top priority. The core technologies and applications underpinning big data security include encryption, access control, and threat detection, among others. These solutions are essential as the volume and complexity of data continue to grow, posing significant challenges for organizations. The service types and product categories within the market include managed security services, software, and hardware. Major companies, such as IBM, Microsoft, and Cisco, dominate the market with their comprehensive offerings. However, the market is not without challenges, including the high investments required for implementing big data security solutions and the need for continuous updates to keep up with evolving threats. Looking ahead, the forecast timeline indicates steady growth for the market, with adoption rates expected to increase significantly. According to recent estimates, The market is projected to reach a market share of over 50% by 2025. As the market continues to unfold, related markets such as the Cloud Security and Cybersecurity markets will also experience similar trends.

What will be the Size of the Big Data Security Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Big Data Security Market Segmented and what are the key trends of market segmentation?

The big data security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloud-basedEnd-userLarge enterprisesSMEsSolutionSoftwareServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalySpainUKAPACChinaIndiaJapanRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market trends encompass various advanced technologies and strategies that businesses employ to safeguard their valuable data. Threat intelligence platforms analyze potential risks and vulnerabilities, enabling proactive threat detection and response. Data encryption methods secure data at rest and in transit, ensuring confidentiality. Security automation tools streamline processes, reducing manual efforts and minimizing human error. Data masking techniques and tokenization processes protect sensitive information by obfuscating or replacing it with non-sensitive data. Vulnerability management tools identify and prioritize risks, enabling remediation. Federated learning security ensures data privacy in collaborative machine learning environments. Real-time threat detection and data breaches prevention employ anomaly detection algorithms and artificial intelligence security to identify and respond to threats. Access control mechanisms and security incident response systems manage and mitigate unauthorized access and data breaches. Security orchestration automation, machine learning security, and big data anonymization techniques enhance security capabilities. Risk assessment methodologies and differential privacy techniques maintain data privacy while enabling data usage. Homomorphic encryption schemes and blockchain security implementations provide advanced data security. Behavioral analytics security monitors user behavior and identifies anomalous activities. Compliance regulations and data privacy regulations mandate adherence to specific security standards. Zero trust architecture and network security monitoring ensure continuous security evaluation and response. Intrusion detection systems and data governance frameworks further strengthen security posture. According to recent studies, the market has experienced a significant 25.6% increase in adoption. Furthermore, industry experts anticipate a 31.8% expansion in the market's size ove

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

This course will introduce you to two of these tools: the Hot Spot Analysis (Getis-Ord Gi*) tool and the Cluster and Outlier Analysis (Anselin Local Moran's I) tool. These tools provide you with more control over your analysis. You can also use these tools to refine your analysis so that it better meets your needs.GoalsAnalyze data using the Hot Spot Analysis (Getis-Ord Gi*) tool.Analyze data using the Cluster and Outlier Analysis (Anselin Local Moran's I) tool.

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.