Data Mining and Modeling Market Outlook

The global data mining and modeling market size was valued at approximately $28.5 billion in 2023 and is projected to reach $70.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This remarkable growth can be attributed to the increasing complexity and volume of data generated across various industries, necessitating robust tools and techniques for effective data analysis and decision-making processes.

One of the primary growth factors driving the data mining and modeling market is the exponential increase in data generation owing to advancements in digital technology. Modern enterprises generate extensive data from numerous sources such as social media platforms, IoT devices, and transactional databases. The need to make sense of this vast information trove has led to a surge in the adoption of data mining and modeling tools. These tools help organizations uncover hidden patterns, correlations, and insights, thereby enabling more informed decision-making and strategic planning.

Another significant growth driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. Data mining and modeling are critical components of AI and ML algorithms, which rely on large datasets to learn and make predictions. As businesses strive to stay competitive, they are increasingly investing in AI-driven analytics solutions. This trend is particularly prevalent in sectors such as healthcare, finance, and retail, where predictive analytics can provide a substantial competitive edge. Moreover, advancements in big data technologies are further bolstering the capabilities of data mining and modeling solutions, making them more effective and efficient.

The burgeoning demand for business intelligence (BI) and analytics solutions is also a major factor propelling the market. Organizations are increasingly recognizing the value of data-driven insights in identifying market trends, customer preferences, and operational inefficiencies. Data mining and modeling tools form the backbone of sophisticated BI platforms, enabling companies to transform raw data into actionable intelligence. This demand is further amplified by the growing importance of regulatory compliance and risk management, particularly in highly regulated industries such as banking, financial services, and healthcare.

From a regional perspective, North America currently dominates the data mining and modeling market, owing to the early adoption of advanced technologies and the presence of major market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation initiatives and increasing investments in AI and big data technologies. Europe also holds a significant market share, supported by stringent data protection regulations and a strong focus on innovation.

Component Analysis

The data mining and modeling market by component is broadly segmented into software and services. The software segment encompasses various tools and platforms that facilitate data mining and modeling processes. These software solutions range from basic data analysis tools to advanced platforms integrated with AI and ML capabilities. The increasing complexity of data and the need for real-time analytics are driving the demand for sophisticated software solutions. Companies are investing in custom and off-the-shelf software to enhance their data handling and analytical capabilities, thereby gaining a competitive edge.

The services segment includes consulting, implementation, training, and support services. As organizations strive to leverage data mining and modeling tools effectively, the demand for professional services is on the rise. Consulting services help businesses identify the right tools and strategies for their specific needs, while implementation services ensure the seamless integration of these tools into existing systems. Training services are crucial for building in-house expertise, enabling teams to maximize the benefits of data mining and modeling solutions. Support services ensure the ongoing maintenance and optimization of these tools, addressing any technical issues that may arise.

The software segment is expected to dominate the market throughout the forecast period, driven by continuous advancements in te

Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring systems. For example, consider an airline manufacturer [tex]$\mathcal{C}$[/tex] manufacturing an aircraft model [tex]$A$[/tex] and selling it to five different airline operating companies [tex]$\mathcal{V}_1 \dots \mathcal{V}_5$[/tex]. These aircrafts, during their operation, generate huge amount of data. Mining this data can reveal useful information regarding the health and operability of the aircraft which can be useful for disaster management and prediction of efficient operating regimes. Now if the manufacturer [tex]$\mathcal{C}$[/tex] wants to analyze the performance data collected from different aircrafts of model-type [tex]$A$[/tex] belonging to different airlines then central collection of data for subsequent analysis may not be an option. It should be noted that the result of this analysis may be statistically more significant if the data for aircraft model [tex]$A$[/tex] across all companies were available to [tex]$\mathcal{C}$[/tex]. The potential problems arising out of such a data mining scenario are:

Data Mining Software Market Outlook

The global data mining software market size was valued at USD 7.2 billion in 2023 and is projected to reach USD 15.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. This growth is driven primarily by the increasing adoption of big data analytics and the rising demand for business intelligence across various industries. As businesses increasingly recognize the value of data-driven decision-making, the market is expected to witness substantial growth.

One of the significant growth factors for the data mining software market is the exponential increase in data generation. With the proliferation of internet-enabled devices and the rapid advancement of technologies such as the Internet of Things (IoT), there is a massive influx of data. Organizations are now more focused than ever on harnessing this data to gain insights, improve operations, and create a competitive advantage. This has led to a surge in demand for advanced data mining tools that can process and analyze large datasets efficiently.

Another driving force is the growing need for personalized customer experiences. In industries such as retail, healthcare, and BFSI, understanding customer behavior and preferences is crucial. Data mining software enables organizations to analyze customer data, segment their audience, and deliver personalized offerings, ultimately enhancing customer satisfaction and loyalty. This drive towards personalization is further fueling the adoption of data mining solutions, contributing significantly to market growth.

The integration of artificial intelligence (AI) and machine learning (ML) technologies with data mining software is also a key growth factor. These advanced technologies enhance the capabilities of data mining tools by enabling them to learn from data patterns and make more accurate predictions. The convergence of AI and data mining is opening new avenues for businesses, allowing them to automate complex tasks, predict market trends, and make informed decisions more swiftly. The continuous advancements in AI and ML are expected to propel the data mining software market over the forecast period.

Regionally, North America holds a significant share of the data mining software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. The Asia Pacific region is also expected to witness substantial growth due to the rapid digital transformation across various industries and the increasing investments in data infrastructure. Additionally, the growing awareness and implementation of data-driven strategies in emerging economies are contributing to the market expansion in this region.

Text Mining Software is becoming an integral part of the data mining landscape, offering unique capabilities to analyze unstructured data. As organizations generate vast amounts of textual data from various sources such as social media, emails, and customer feedback, the need for specialized tools to extract meaningful insights is growing. Text Mining Software enables businesses to process and analyze this data, uncovering patterns and trends that were previously hidden. This capability is particularly valuable in industries like marketing, customer service, and research, where understanding the nuances of language can lead to more informed decision-making. The integration of text mining with traditional data mining processes is enhancing the overall analytical capabilities of organizations, allowing them to derive comprehensive insights from both structured and unstructured data.

Component Analysis

The data mining software market is segmented by components, which primarily include software and services. The software segment encompasses various types of data mining tools that are used for analyzing and extracting valuable insights from raw data. These tools are designed to handle large volumes of data and provide advanced functionalities such as predictive analytics, data visualization, and pattern recognition. The increasing demand for sophisticated data analysis tools is driving the growth of the software segment. Enterprises are investing in these tools to enhance their data processing capabilities and derive actionable insights.

Within the software segment, the emergence of cloud-based data mining solutions is a notable trend. Cloud-based solutions offer several advantages, including s

Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

Data Mining Tools Market Outlook

According to our latest research, the global Data Mining Tools market size reached USD 1.93 billion in 2024, reflecting robust industry momentum. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 5.69 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics across diverse industries, rapid digital transformation, and the necessity for actionable insights from massive data volumes.

One of the pivotal growth factors propelling the Data Mining Tools market is the exponential rise in data generation, particularly through digital channels, IoT devices, and enterprise applications. Organizations across sectors are leveraging data mining tools to extract meaningful patterns, trends, and correlations from structured and unstructured data. The need for improved decision-making, operational efficiency, and competitive advantage has made data mining an essential component of modern business strategies. Furthermore, advancements in artificial intelligence and machine learning are enhancing the capabilities of these tools, enabling predictive analytics, anomaly detection, and automation of complex analytical tasks, which further fuels market expansion.

Another significant driver is the growing demand for customer-centric solutions in industries such as retail, BFSI, and healthcare. Data mining tools are increasingly being used for customer relationship management, targeted marketing, fraud detection, and risk management. By analyzing customer behavior and preferences, organizations can personalize their offerings, optimize marketing campaigns, and mitigate risks. The integration of data mining tools with cloud platforms and big data technologies has also simplified deployment and scalability, making these solutions accessible to small and medium-sized enterprises (SMEs) as well as large organizations. This democratization of advanced analytics is creating new growth avenues for vendors and service providers.

The regulatory landscape and the increasing emphasis on data privacy and security are also shaping the development and adoption of Data Mining Tools. Compliance with frameworks such as GDPR, HIPAA, and CCPA necessitates robust data governance and transparent analytics processes. Vendors are responding by incorporating features like data masking, encryption, and audit trails into their solutions, thereby enhancing trust and adoption among regulated industries. Additionally, the emergence of industry-specific data mining applications, such as fraud detection in BFSI and predictive diagnostics in healthcare, is expanding the addressable market and fostering innovation.

From a regional perspective, North America currently dominates the Data Mining Tools market owing to the early adoption of advanced analytics, strong presence of leading technology vendors, and high investments in digital transformation. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization, expansion of IT infrastructure, and growing awareness of data-driven decision-making in countries like China, India, and Japan. Europe, with its focus on data privacy and digital innovation, also represents a significant market share, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions modernize their operations and adopt cloud-based analytics solutions.

Component Analysis

The Component segment of the Data Mining Tools market is bifurcated into Software and Services. Software remains the dominant segment, accounting for the majority of the market share in 2024. This dominance is attributed to the continuous evolution of data mining algorithms, the proliferation of user-friendly graphical interfaces, and the integration of advanced analytics capabilities such as machine learning, artificial intelligence, and natural language pro

Big Data Tools Market Outlook

The global Big Data Tools market size is anticipated to grow from USD 31.5 billion in 2023 to USD 103.5 billion by 2032, at a compound annual growth rate (CAGR) of 14.5%. This robust growth can be attributed to several key factors, including the increasing volume of data generated across various industries, advancements in data analytics technologies, and the growing demand for data-driven decision-making. The proliferation of IoT devices, the rise of artificial intelligence, and the emphasis on enhancing customer experience further drive the expansion of the Big Data Tools market worldwide.

The exponential increase in data generation is one of the foremost drivers of the Big Data Tools market. With the rise of digital transformation initiatives, industries are generating massive amounts of data every second. From social media interactions to transactional data and from IoT sensors to operational data, the volume, variety, and velocity of data have escalated to unprecedented levels. Organizations are increasingly recognizing the potential of leveraging this data to gain actionable insights, optimize operations, and drive business growth, thus fueling the demand for advanced Big Data tools and technologies.

Another significant growth factor is the technological advancements in data analytics and machine learning. Big Data tools have evolved from traditional data warehousing and analytics platforms to sophisticated solutions incorporating artificial intelligence and machine learning. These advancements enable organizations to perform predictive and prescriptive analytics, uncover hidden patterns, and make data-driven decisions with greater accuracy and speed. The continuous innovation and integration of advanced technologies into Big Data tools are propelling their adoption across various sectors.

The increasing emphasis on enhancing customer experience is also driving the Big Data Tools market. Businesses are leveraging Big Data analytics to gain deeper insights into customer behavior, preferences, and sentiment. By analyzing this data, organizations can personalize their offerings, improve customer engagement, and deliver superior experiences. In sectors such as retail, banking, and healthcare, the ability to understand and predict customer needs has become a competitive differentiator, leading to significant investments in Big Data tools to achieve these objectives.

Data Mining Tools play a pivotal role in the Big Data landscape by enabling organizations to extract valuable insights from vast datasets. These tools are designed to sift through large volumes of data, identify patterns, and uncover relationships that might not be immediately apparent. By leveraging advanced algorithms and statistical techniques, Data Mining Tools help businesses make informed decisions, optimize processes, and enhance strategic planning. As the volume of data continues to grow exponentially, the demand for robust and efficient Data Mining Tools is on the rise, driving innovation and competition in the market. Companies are increasingly investing in these tools to gain a competitive edge and unlock the full potential of their data assets.

From a regional perspective, North America is expected to dominate the Big Data Tools market, primarily due to the presence of leading technology companies, early adoption of advanced analytics solutions, and significant investments in data-driven initiatives. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitalization of economies, increasing internet penetration, and the burgeoning e-commerce sector are driving the demand for Big Data tools in this region. Additionally, governments in countries like China and India are promoting data analytics and AI, further boosting the market's growth prospects.

Component Analysis

The Big Data Tools market is segmented by component into software and services. The software segment includes various types of Big Data platforms and analytics tools. These software solutions are designed to handle, process, and analyze large volumes of structured and unstructured data. Key offerings within this segment include data storage solutions, data processing frameworks, data visualization tools, and advanced analytics software. The continuous innovation in software capabilities, such as real-time data analytics and AI integration, is driving the growth of this segment.

Data Warehouse Market size was valued at USD 27.68 Billion in 2024 and is projected to reach USD 63.9 Billion by 2032, growing at a CAGR of 11% from 2026 to 2032.

Key Market Drivers: Increasing Volume of Data Generated across Industries: The exponential expansion of data generation is increasing the demand for robust data warehouse solutions. According to the International Data Corporation (IDC), the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This tremendous rise in data volume demands sophisticated data warehousing capabilities to ensure efficient storage, administration, and analysis.

Growing Adoption of Cloud-based Data Warehousing: The shift to cloud-based solutions is a significant driver of the Data Warehouse Market.

T10I4D100K is a renowned synthetic database generated using the IBM Quest generator. This database is widely used to evaluate various frequent and correlated pattern mining algorithms.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Privacy‑Preserving Data Mining Tools Market Outlook

According to our latest research, the global Privacy‑Preserving Data Mining Tools market size reached USD 1.42 billion in 2024, reflecting robust adoption across diverse industries. The market is expected to exhibit a CAGR of 22.8% during the forecast period, propelling the market to USD 10.98 billion by 2033. This remarkable growth is driven by the increasing need for secure data analytics, stringent data protection regulations, and the rising frequency of data breaches, all of which are pushing organizations to adopt advanced privacy solutions.

One of the primary growth factors for the Privacy‑Preserving Data Mining Tools market is the exponential rise in data generation and the parallel escalation of privacy concerns. As organizations collect vast amounts of sensitive information, especially in sectors like healthcare and BFSI, the risk of data exposure and misuse grows. Governments worldwide are enacting stricter data protection laws, such as the GDPR in Europe and CCPA in California, compelling enterprises to integrate privacy‑preserving technologies into their analytics workflows. These regulations not only mandate compliance but also foster consumer trust, making privacy‑preserving data mining tools a strategic investment for businesses aiming to maintain a competitive edge while safeguarding user data.

Another significant driver is the rapid digital transformation across industries, which necessitates the extraction of actionable insights from large, distributed data sets without compromising privacy. Privacy‑preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, are gaining traction as they allow organizations to collaborate and analyze data securely. The advent of cloud computing and the proliferation of connected devices further amplify the demand for scalable and secure data mining solutions. As enterprises embrace cloud-based analytics, the need for robust privacy-preserving mechanisms becomes paramount, fueling the adoption of advanced tools that can operate seamlessly in both on-premises and cloud environments.

Moreover, the increasing sophistication of cyber threats and the growing awareness of the potential reputational and financial damage caused by data breaches are prompting organizations to prioritize data privacy. High-profile security incidents have underscored the vulnerabilities inherent in traditional data mining approaches, accelerating the shift towards privacy-preserving alternatives. The integration of artificial intelligence and machine learning with privacy-preserving technologies is also opening new avenues for innovation, enabling more granular and context-aware data analytics. This technological convergence is expected to further catalyze market growth, as organizations seek to harness the full potential of their data assets while maintaining stringent privacy standards.

From a regional perspective, North America currently commands the largest share of the Privacy‑Preserving Data Mining Tools market, driven by the presence of leading technology vendors, high awareness levels, and a robust regulatory framework. Europe follows closely, propelled by stringent data privacy laws and increasing investments in secure analytics infrastructure. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT ecosystems, and rising cybersecurity concerns in emerging economies such as China and India. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the importance of privacy in data-driven decision-making.

Component Analysis

The Privacy‑Preserving Data Mining Tools market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment dominates the market, accounting for the majority of revenue in 2024. Organizations are increasingly investing in advanced software so

The Life Sciences Data Mining and Visualization Software market is experiencing robust growth, driven by the increasing volume of biological data generated through genomics, proteomics, and clinical trials. The market's expansion is fueled by the urgent need for efficient tools to analyze this complex data, enabling faster drug discovery, personalized medicine initiatives, and improved patient outcomes. Companies are increasingly investing in advanced analytics solutions to gain actionable insights from their data, leading to improved operational efficiency and reduced research and development costs. The integration of artificial intelligence (AI) and machine learning (ML) capabilities within these software solutions is a significant trend, enhancing the ability to identify patterns and make predictions from large datasets. This market is segmented by software type (e.g., data mining, visualization, integrated solutions), deployment mode (cloud, on-premise), and end-user (pharmaceutical companies, biotechnology firms, research institutions). Competition is fierce, with established players like IBM, Microsoft, and SAS competing with specialized life sciences focused companies and emerging innovative startups. While the market faces challenges such as the high cost of implementation and the need for specialized expertise, the long-term prospects remain positive. The continuous advancements in data generation technologies and the growing demand for data-driven decision-making in the life sciences sector will continue to fuel market growth. Furthermore, the increasing adoption of cloud-based solutions is expected to lower the barrier to entry for smaller companies and research institutions, further expanding the market. This makes the Life Sciences Data Mining and Visualization Software market a particularly attractive investment opportunity with high potential for both established players and new entrants. The market's estimated size in 2025 is $10 Billion, with a projected Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033.

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

The Enterprise Data Warehouse (EDW) market is experiencing robust growth, projected to reach $14.40 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 30.08% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and variety of data generated by businesses necessitate robust solutions for storage, processing, and analysis. Cloud-based deployments are gaining significant traction, offering scalability, cost-effectiveness, and accessibility. Furthermore, the growing adoption of advanced analytics techniques like machine learning and AI is driving demand for sophisticated EDW solutions capable of handling complex data sets and delivering actionable insights. The market is segmented by product type (information and analytical processing, data mining) and deployment (cloud-based, on-premises). While on-premises solutions still hold a market share, the cloud segment is witnessing significantly faster growth due to its inherent advantages. Key players like Snowflake, Amazon, and Microsoft are leading the charge, leveraging their existing cloud infrastructure and expertise in data management to capture market share. Competitive strategies focus on innovation in areas like data virtualization, enhanced security features, and integration with other enterprise applications. Industry risks include data security breaches, the complexity of data integration, and the need for skilled professionals to manage and utilize EDW systems effectively. The North American market currently dominates, followed by Europe and APAC regions, each showing strong growth potential. The forecast period (2025-2033) anticipates continued market expansion driven by ongoing digital transformation initiatives across various industries. The increasing adoption of big data analytics and the growing need for real-time business intelligence will further fuel market growth. Companies are investing heavily in upgrading their EDW infrastructure and adopting advanced analytical capabilities to gain a competitive edge. The competitive landscape is dynamic, with both established players and emerging startups vying for market share. Strategic partnerships, mergers, and acquisitions are expected to reshape the market landscape over the forecast period. The continued development of innovative solutions addressing the evolving needs of businesses will be crucial for success in this rapidly growing market. Regions like APAC show immense growth potential due to increasing digitization and data generation across emerging economies.

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

The India data mining tools market attained a value of USD 202.40 Million in 2024 and is projected to expand at a CAGR of around 12.90% through 2034. The swift growth in digitization, cloud-based infrastructure, and generation of enterprise data is driving market growth. Specifically, the Indian IT-BPM sector, which reached a revenue of more than USD 245 billion in FY2024, continues to increase its analytics and data services offerings. The increasing demand for cloud-native platforms and the inclusion of AI and ML in business processes also sustains the positive outlook for the India data mining tools market during the forecast period. This thereby accelerates the industry to achieve a value of USD 681.00 Million by 2034.

The data mining software market in India is experiencing rapid growth, fueled by the exponential use of digital technologies, growing volumes of data, and the strategic focus on decision-making based on data across industries. Data mining software helps companies derive valuable insights from large amounts of data, improving customer engagement, operational effectiveness, and competitiveness. With the growing adoption of AI, ML, and advanced analytics across industries like BFSI, healthcare, retail, and manufacturing, demand for advanced data mining solutions is picking up, thus propelling the India data mining tools market expansion.

Government schemes such as Digital India and growing enterprise-level investment in big data infrastructure are also driving market growth. For instance, Indian IT companies like Infosys and TCS increased their analytics services in early 2025 to cater to global and domestic customers. Infosys secured its highest-ever quarterly deal wins in Q1 FY25, totaling USD 4.1 billion across 34 contracts, with 63% being net new deals. This surge reflects a strategic focus on AI, data analytics, and cloud services, positioning Infosys as a leader in next-generation digital solutions. TCS maintained its status as the world's second most valuable IT services brand in 2025, with a brand value increase of 11% to USD 21.3 billion. This growth is attributed to TCS's investments in AI and emerging technologies, reinforcing its global leadership in digital transformation services. The growing adoption of clouds and increasing penetration of SMEs in the technology ecosystem equally highlight the importance of data mining tools in India. With data at the core of decision-making and strategy development, the market will be a key component of the digital transformation journey of the country.

Data and model checkpoints for paper "Weakly Supervised Concept Map Generation through Task-Guided Graph Translation" by Jiaying Lu, Xiangjue Dong, and Carl Yang. The paper has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).

GT-D2G-*.tar.gz are model checkpoints for GT-D2G variants. These models are trained by seed=27. nyt/dblp/yelp.*.win5.pickle.gz are initial graphs generated by NLP pipelines. glove.840B.restaurant.400d.vec.gz is the pre-trained embedding for the Yelp dataset.

For more instructions, please refer to our GitHub repo.

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.

We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.

We provide links to the PTM Dataset and PTM Torrent Source Code.

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.

Data Extraction Software Market Outlook

The global data extraction software market size is projected to grow from USD 2.5 billion in 2023 to USD 6.7 billion by 2032, at a compound annual growth rate (CAGR) of 11.4% during the forecast period. The increasing demand for efficient data management solutions and the exponential growth in data generation across industries are key growth factors driving this market.

One of the primary growth drivers for the data extraction software market is the escalating volume of data being generated daily. With the proliferation of digitalization, IoT devices, and social media platforms, organizations are inundated with massive amounts of data. Leveraging this data for actionable insights necessitates advanced data extraction tools, thereby boosting market demand. Additionally, the growing need for organizations to make data-driven decisions is further propelling the adoption of data extraction software. Companies aim to extract valuable information from disparate data sources to enhance operational efficiency and gain competitive advantages.

Another pivotal growth factor is the increasing integration of artificial intelligence (AI) and machine learning (ML) technologies in data extraction software. AI and ML algorithms significantly enhance the capability of data extraction tools to understand, process, and analyze unstructured data. This development is particularly beneficial for sectors such as healthcare, BFSI, and retail, where large volumes of unstructured data are prevalent. The adoption of AI and ML in data extraction solutions not only improves accuracy and speed but also reduces manual intervention, thereby lowering operational costs.

The shift towards cloud-based solutions is also fueling the growth of the data extraction software market. Cloud deployment offers several advantages, including scalability, flexibility, and reduced infrastructure costs. Moreover, the COVID-19 pandemic has accelerated the digital transformation journey of many businesses, leading to an increased adoption of cloud-based data extraction solutions. The ability to access and analyze data remotely becomes crucial in a work-from-home environment, further driving demand for cloud-based data extraction software.

Regional growth prospects for the data extraction software market appear robust, with North America expected to hold the largest market share. The region's advanced IT infrastructure, coupled with the presence of major technology players, underpins its market dominance. Asia Pacific is anticipated to witness the highest growth rate during the forecast period, driven by rapid digital transformation initiatives, increasing investments in big data analytics, and the growing adoption of cloud services in countries such as China and India. Europe also presents significant growth opportunities, particularly in industries such as BFSI and healthcare, where data-driven decision-making is becoming increasingly critical.

Data Mining Software plays a crucial role in the broader landscape of data extraction and analysis. As organizations increasingly seek to derive actionable insights from vast amounts of data, data mining software provides the tools necessary to uncover patterns and relationships within datasets. This software utilizes sophisticated algorithms to process large volumes of data, making it indispensable for industries such as finance, healthcare, and retail. By integrating data mining capabilities with data extraction software, companies can enhance their decision-making processes, optimize operations, and identify new business opportunities. The synergy between data mining and extraction tools is driving innovation and efficiency across various sectors, underscoring the importance of these technologies in the modern data-driven world.

Component Analysis

In the data extraction software market, components are broadly segmented into software and services. The software segment includes various data extraction tools and platforms designed to collect, process, and analyze data from multiple sources. This segment is expected to hold the largest market share owing to the rising demand for advanced data extraction solutions that can handle large datasets and provide real-time insights. The adoption of AI and ML technologies within these software solutions is a significant trend, enabling more accurate and efficient data extraction processes.

The services segment includes consulting, i

LScD (Leicester Scientific Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScD (Leicester Scientific Dictionary) is created from the updated LSC (Leicester Scientific Corpus) - Version 2*. All pre-processing steps applied to build the new version of the dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. After pre-processing steps, the total number of unique words in the new version of the dictionary is 972,060. The files provided with this description are also same as described as for LScD Version 2 below.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2** Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v2[Version 2] Getting StartedThis document provides the pre-processing steps for creating an ordered list of words from the LSC (Leicester Scientific Corpus) [1] and the description of LScD (Leicester Scientific Dictionary). This dictionary is created to be used in future work on the quantification of the meaning of research texts. R code for producing the dictionary from LSC and instructions for usage of the code are available in [2]. The code can be also used for list of texts from other sources, amendments to the code may be required.LSC is a collection of abstracts of articles and proceeding papers published in 2014 and indexed by the Web of Science (WoS) database [3]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English. The corpus was collected in July 2018 and contains the number of citations from publication date to July 2018. The total number of documents in LSC is 1,673,824.LScD is an ordered list of words from texts of abstracts in LSC.The dictionary stores 974,238 unique words, is sorted by the number of documents containing the word in descending order. All words in the LScD are in stemmed form of words. The LScD contains the following information:1.Unique words in abstracts2.Number of documents containing each word3.Number of appearance of a word in the entire corpusProcessing the LSCStep 1.Downloading the LSC Online: Use of the LSC is subject to acceptance of request of the link by email. To access the LSC for research purposes, please email to ns433@le.ac.uk. The data are extracted from Web of Science [3]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.Step 2.Importing the Corpus to R: The full R code for processing the corpus can be found in the GitHub [2].All following steps can be applied for arbitrary list of texts from any source with changes of parameter. The structure of the corpus such as file format and names (also the position) of fields should be taken into account to apply our code. The organisation of CSV files of LSC is described in README file for LSC [1].Step 3.Extracting Abstracts and Saving Metadata: Metadata that include all fields in a document excluding abstracts and the field of abstracts are separated. Metadata are then saved as MetaData.R. Fields of metadata are: List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.Step 4.Text Pre-processing Steps on the Collection of Abstracts: In this section, we presented our approaches to pre-process abstracts of the LSC.1.Removing punctuations and special characters: This is the process of substitution of all non-alphanumeric characters by space. We did not substitute the character “-” in this step, because we need to keep words like “z-score”, “non-payment” and “pre-processing” in order not to lose the actual meaning of such words. A processing of uniting prefixes with words are performed in later steps of pre-processing.2.Lowercasing the text data: Lowercasing is performed to avoid considering same words like “Corpus”, “corpus” and “CORPUS” differently. Entire collection of texts are converted to lowercase.3.Uniting prefixes of words: Words containing prefixes joined with character “-” are united as a word. The list of prefixes united for this research are listed in the file “list_of_prefixes.csv”. The most of prefixes are extracted from [4]. We also added commonly used prefixes: ‘e’, ‘extra’, ‘per’, ‘self’ and ‘ultra’.4.Substitution of words: Some of words joined with “-” in the abstracts of the LSC require an additional process of substitution to avoid losing the meaning of the word before removing the character “-”. Some examples of such words are “z-test”, “well-known” and “chi-square”. These words have been substituted to “ztest”, “wellknown” and “chisquare”. Identification of such words is done by sampling of abstracts form LSC. The full list of such words and decision taken for substitution are presented in the file “list_of_substitution.csv”.5.Removing the character “-”: All remaining character “-” are replaced by space.6.Removing numbers: All digits which are not included in a word are replaced by space. All words that contain digits and letters are kept because alphanumeric characters such as chemical formula might be important for our analysis. Some examples are “co2”, “h2o” and “21st”.7.Stemming: Stemming is the process of converting inflected words into their word stem. This step results in uniting several forms of words with similar meaning into one form and also saving memory space and time [5]. All words in the LScD are stemmed to their word stem.8.Stop words removal: Stop words are words that are extreme common but provide little value in a language. Some common stop words in English are ‘I’, ‘the’, ‘a’ etc. We used ‘tm’ package in R to remove stop words [6]. There are 174 English stop words listed in the package.Step 5.Writing the LScD into CSV Format: There are 1,673,824 plain processed texts for further analysis. All unique words in the corpus are extracted and written in the file “LScD.csv”.The Organisation of the LScDThe total number of words in the file “LScD.csv” is 974,238. Each field is described below:Word: It contains unique words from the corpus. All words are in lowercase and their stem forms. The field is sorted by the number of documents that contain words in descending order.Number of Documents Containing the Word: In this content, binary calculation is used: if a word exists in an abstract then there is a count of 1. If the word exits more than once in a document, the count is still 1. Total number of document containing the word is counted as the sum of 1s in the entire corpus.Number of Appearance in Corpus: It contains how many times a word occurs in the corpus when the corpus is considered as one large document.Instructions for R CodeLScD_Creation.R is an R script for processing the LSC to create an ordered list of words from the corpus [2]. Outputs of the code are saved as RData file and in CSV format. Outputs of the code are:Metadata File: It includes all fields in a document excluding abstracts. Fields are List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.File of Abstracts: It contains all abstracts after pre-processing steps defined in the step 4.DTM: It is the Document Term Matrix constructed from the LSC[6]. Each entry of the matrix is the number of times the word occurs in the corresponding document.LScD: An ordered list of words from LSC as defined in the previous section.The code can be used by:1.Download the folder ‘LSC’, ‘list_of_prefixes.csv’ and ‘list_of_substitution.csv’2.Open LScD_Creation.R script3.Change parameters in the script: replace with the full path of the directory with source files and the full path of the directory to write output files4.Run the full code.References[1]N. Suzen. (2019). LSC (Leicester Scientific Corpus) [Dataset]. Available: https://doi.org/10.25392/leicester.data.9449639.v1[2]N. Suzen. (2019). LScD-LEICESTER SCIENTIFIC DICTIONARY CREATION. Available: https://github.com/neslihansuzen/LScD-LEICESTER-SCIENTIFIC-DICTIONARY-CREATION[3]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4]A. Thomas, "Common Prefixes, Suffixes and Roots," Center for Development and Learning, 2013.[5]C. Ramasubramanian and R. Ramya, "Effective pre-processing activities in text mining using improved porter’s stemming algorithm," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 12, pp. 4536-4538, 2013.[6]I. Feinerer, "Introduction to the tm Package Text Mining in R," Accessible en ligne: https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf, 2013.