The global Data Mining Software market is experiencing robust growth, driven by the increasing need for businesses to extract valuable insights from massive datasets. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and SMEs. Furthermore, advancements in machine learning and artificial intelligence algorithms are enhancing the accuracy and efficiency of data mining processes, leading to better decision-making across various sectors like finance, healthcare, and marketing. The rise of big data analytics and the increasing availability of affordable, high-powered computing resources are also significant contributors to market growth. However, the market faces certain challenges. Data security and privacy concerns remain paramount, especially with the increasing volume of sensitive information being processed. The complexity of data mining software and the need for skilled professionals to operate and interpret the results present a barrier to entry for some businesses. The high initial investment cost associated with implementing sophisticated data mining solutions can also deter smaller organizations. Nevertheless, the ongoing technological advancements and the growing recognition of the strategic value of data-driven decision-making are expected to overcome these restraints and propel the market toward continued expansion. The market segmentation reveals a strong preference for cloud-based solutions, reflecting the industry's trend toward flexible and scalable IT infrastructure. Large enterprises currently dominate the market share, but SMEs are rapidly adopting data mining software, indicating promising future growth in this segment. Geographic analysis shows that North America and Europe are currently leading the market, but the Asia-Pacific region is poised for significant growth due to increasing digitalization and economic expansion in countries like China and India.

Data Mining Tools Market Outlook 2032

The global data mining tools market size was USD 932 Million in 2023 and is projected to reach USD 2,584.7 Million by 2032, expanding at a CAGR of 12% during 2024–2032. The market is fueled by the rising demand for big data analytics across various industries and the increasing need for AI-integrated data mining tools for insightful decision-making.



Increasing adoption of cloud-based platforms in data mining tools fuels the market. This enhances scalability, flexibility, and cost-efficiency in data handling processes. Major tech companies are launching cloud-based data mining solutions, enabling businesses to analyze vast datasets effectively. This trend reflects the shift toward agile and scalable data analysis methods, meeting the dynamic needs of modern enterprises.

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  In July 2023, Microsoft launched Power Automate Process Mining. This tool, powered by advanced AI, allows companies to gain deep insights into their operations, streamline processes, and foster ongoing improvement through automation and low-code applications, marking a new era in business efficiency and process optimization.

  
  
  
  
  

Rising focus on predictive analytics propels the development of advanced data mining tools capable of forecasting future trends and behaviors. Industries such as finance, healthcare, and retail invest significantly in predictive analytics to gain a competitive edge, driving demand for sophisticated data mining technologies. This trend underscores the strategic importance of foresight in decision-making processes.



Visual data mining tools are gaining traction in the market, offering intuitive data exploration and interpretation capabilities. These tools enable users to uncover patterns and insights through graphical representations, making data analysis accessible to a broader audience. The launch of user-friendly visual data mining applications marks a significant step toward democratizing data analytics.

Impact of Artificial Intelligence (

The global Data Mining Tools market, valued at $612.4 million in 2025, is projected to experience robust growth, driven by the increasing volume and variety of data generated across industries and the rising need for extracting actionable insights. The Compound Annual Growth Rate (CAGR) of 6.7% from 2025 to 2033 signifies a substantial expansion, propelled by several key factors. The burgeoning adoption of cloud-based data mining tools offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of these tools, enabling more sophisticated analytics and predictive modeling. Specific application areas like BFSI (Banking, Financial Services, and Insurance), Healthcare and Life Sciences, and Telecom and IT are significant contributors to market growth, fueled by the need for risk management, personalized medicine, and customer relationship management respectively. While data security and privacy concerns represent a potential restraint, the overall market outlook remains positive, driven by continuous technological innovations and increasing digitalization across industries. The market segmentation reveals a preference for cloud-based solutions over on-premises deployments, reflecting the growing demand for flexible and scalable analytics infrastructure. Leading players like IBM, SAS Institute, and Oracle are consolidating their market share through strategic partnerships and continuous product development. However, the emergence of agile and specialized data mining startups is also intensifying competition. Geographic distribution shows strong growth in North America and Europe, driven by early adoption of advanced analytics techniques. However, the Asia-Pacific region is expected to emerge as a significant growth driver in the coming years due to increasing digitalization and government initiatives promoting data-driven decision-making. The historical period (2019-2024) likely saw a similar growth trajectory, setting the stage for the forecasted expansion during 2025-2033. The continued integration of data mining tools with other business intelligence platforms is expected to further fuel market expansion.

PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE, KIRK BORNE, SANDIPAN DEY, SUGANDHA ARORA, AND HILLOL KARGUPTA** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms, including the mixture of experts model, classification and regression trees (CART), and others have been developed, motivated by the fact that a variability in the local distribution of inputs may be reflective of a significant change in the target variable. While these methods can handle the non-stationarity in the relationships to varying degrees, they are often not scalable and, therefore, not used in large scale data mining applications. In this paper we develop Block-GP, a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution. Theoretical analysis and empirical studies on various synthetic and real data sets show high accuracy and scalability of Block-GP compared to existing nonlinear regression techniques.

The Text and Data Mining (TDM) market is experiencing robust growth, driven by the increasing volume of unstructured data generated across various sectors and the need for businesses to extract actionable insights. The market's expansion is fueled by advancements in Natural Language Processing (NLP), machine learning algorithms, and cloud computing capabilities, which enable efficient processing and analysis of large datasets. Organizations across diverse industries, including finance, healthcare, and marketing, are adopting TDM solutions to improve customer experience, enhance operational efficiency, and gain a competitive edge through data-driven decision-making. The rising demand for sentiment analysis, topic modeling, and text summarization is further contributing to the market's expansion. While data privacy and security concerns represent a significant restraint, the development of robust and compliant solutions is mitigating these risks, fostering market growth. Considering a plausible CAGR of 15% and a 2025 market size of $5 billion (a reasonable estimation based on industry reports showing similar growth in related AI segments), we can project substantial growth throughout the forecast period. Leading players like IBM, SAS, and others are continuously innovating and expanding their offerings, solidifying their market positions. The competitive landscape is characterized by a mix of established vendors and emerging players offering diverse solutions ranging from enterprise-level platforms to specialized APIs. The market is witnessing a shift towards cloud-based TDM solutions due to their scalability, cost-effectiveness, and ease of deployment. Furthermore, the increasing adoption of open-source tools like Apache Mahout is making TDM technology more accessible to a wider range of users. However, challenges remain in terms of data standardization, integrating TDM with existing business processes, and ensuring the accuracy and reliability of insights generated. Future growth will be significantly influenced by the adoption of advanced analytics techniques such as deep learning and the development of more sophisticated NLP models capable of understanding context and nuances in human language. Continued investment in research and development, coupled with industry collaborations, will be crucial in overcoming these challenges and unlocking the full potential of TDM.

The Enterprise Data Warehouse (EDW) market is experiencing robust growth, projected to reach a market size of $3455.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.6% from 2025 to 2033. This expansion is driven by the increasing need for organizations to consolidate data from disparate sources for improved business intelligence, enhanced decision-making, and streamlined operational efficiency. The rising adoption of cloud-based EDW solutions, fueled by scalability, cost-effectiveness, and accessibility, is a significant factor contributing to this growth. Furthermore, the expanding use of advanced analytics techniques, such as data mining and predictive modeling, within EDWs is further boosting market demand across diverse sectors including healthcare, finance, and retail. The market is segmented by deployment type (web-based and server-based) and application (information processing, data mining, and analytical processing), reflecting the diverse functionalities and deployment models available. Key players, including industry giants like Amazon Web Services, Microsoft, and Google, alongside specialized vendors like Teradata and Snowflake, are aggressively innovating to meet the evolving needs of enterprises. The competitive landscape is characterized by both established players and emerging technology providers. The ongoing trend towards data democratization, where access to data and analytics is broadened within organizations, is fostering demand for user-friendly EDW interfaces and tools. While regulatory compliance and data security remain key restraints, the overall market outlook for EDWs remains positive, with substantial growth potential driven by the continuous rise in data volumes, the growing need for real-time analytics, and increasing investments in digital transformation initiatives across industries globally. The North American market currently holds a significant share due to early adoption and technological advancements, but the Asia-Pacific region is projected to witness rapid growth in the coming years due to increased digitalization and technological infrastructure development.

The Data Processing and Hosting Services market, exhibiting a Compound Annual Growth Rate (CAGR) of 4.20%, presents a significant opportunity for growth. While the exact market size in millions is not specified, considering the substantial involvement of major players like Amazon Web Services, IBM, and Salesforce, coupled with the pervasive adoption of cloud computing and big data analytics across diverse sectors, a 2025 market size exceeding $500 billion is a reasonable estimate. This robust growth is driven by several key factors. The increasing reliance on cloud-based solutions by both large enterprises and SMEs reflects a shift towards greater scalability, flexibility, and cost-effectiveness. Furthermore, the exponential growth of data necessitates advanced data processing capabilities, fueling demand for data mining, cleansing, and management services. The burgeoning adoption of AI and machine learning further enhances this need, as these technologies require robust data infrastructure and sophisticated processing techniques. Specific industry segments like IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), and Retail are major consumers, demanding reliable and secure hosting solutions and data processing capabilities to manage their critical operations and customer data. However, challenges remain, including the ongoing threat of cyberattacks and data breaches, necessitating robust security measures and compliance with evolving data privacy regulations. Competition among existing players is intense, driving innovation and price wars, which can impact profitability for some market participants. The forecast period of 2025-2033 indicates a continued upward trajectory for the market, largely fueled by expanding digitalization efforts globally. The Asia Pacific region is projected to be a significant contributor to this growth, driven by increasing internet penetration and a burgeoning technological landscape. While North America and Europe maintain substantial market share, the faster growth rate anticipated in Asia Pacific and other emerging markets signifies an evolving global market dynamic. Continued advancements in technologies such as edge computing, serverless architecture, and improved data analytics techniques will further drive market expansion and shape the competitive landscape. The segmentation within the market (by organization size, service offering, and end-user industry) presents diverse investment opportunities for businesses catering to specific needs and technological advancements within these niches. Recent developments include: December 2022 - TetraScience, the Scientific Data Cloud company, announced that Gubbs, a lab optimization, and validation software leader, joined the Tetra Partner Network to increase and enhance data processing throughput with the Tetra Scientific Data Cloud., November 2022 - Kinsta, a hosting provider that provides managed WordPress hosting powered by Google Cloud Platform, announced the launch of Application Hosting and Database Hosting. It is adding these two hosting services to its Managed WordPress product ushers in a new era for Kinsta as a Cloud Platform, enabling developers and businesses to run powerful applications, databases, websites, and services more flexibly than ever.. Key drivers for this market are: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Potential restraints include: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Notable trends are: Web Hosting is Gaining Traction Due to Emergence of Cloud-based Platform.

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.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.

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

The Lifesciences Data Mining and Visualization market is experiencing robust growth, driven by the increasing volume of biological data generated through genomics, proteomics, and clinical trials. The need for efficient analysis and interpretation of this complex data to accelerate drug discovery, personalize medicine, and improve patient outcomes is fueling market expansion. A Compound Annual Growth Rate (CAGR) of approximately 15% is projected for the period 2025-2033, indicating significant market potential. The pharmaceutical and biotech sectors are major contributors, with a strong demand for advanced analytical tools to manage large datasets and extract actionable insights. Contract Research Organizations (CROs) are also actively adopting these solutions to improve efficiency and reduce costs in their research and development processes. The market is segmented by deployment type (on-premise, on-demand, both) and application (academia, biotech, government, pharmaceuticals, CROs, others). On-demand solutions are witnessing greater adoption due to their scalability and cost-effectiveness, particularly among smaller organizations. Geographic growth is expected across regions, with North America and Europe maintaining a significant market share due to the presence of established players and extensive research infrastructure. However, Asia Pacific is poised for rapid expansion driven by increasing government investments in healthcare and growing adoption of advanced technologies. Competitive landscape includes established players like Tableau, SAP, IBM, and SAS, along with several specialized data visualization providers. The market's future growth is dependent on factors such as advancements in data analytics techniques, increasing data volumes, and the growing focus on data security and regulatory compliance within the life sciences industry. The market's future hinges on several factors. The continuous evolution of data analytics techniques, including artificial intelligence and machine learning, will create more sophisticated tools for life sciences data analysis. The exponential growth of biological data, driven by next-generation sequencing and other high-throughput technologies, will sustain demand for efficient data mining and visualization solutions. Additionally, regulations regarding data privacy and security will influence the development and adoption of these tools, with robust security features becoming paramount. The increasing emphasis on personalized medicine and precision therapies will further bolster the market, as researchers require advanced analytics to understand individual patient responses and tailor treatments accordingly. Finally, the integration of data mining and visualization tools with other life science software and platforms will drive greater adoption and efficiency within the industry.

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Saas-Based Business Analytics Market Size 2024-2028

The saas-based business analytics market size is forecast to increase by USD 10.2 billion, at a CAGR of 13.63% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing demand for data integration and visual analytics solutions. Companies are recognizing the value of leveraging real-time data to make informed business decisions, leading to increased adoption of cloud-based analytics platforms. However, challenges persist in the form of bandwidth and connectivity issues, which can hinder the seamless implementation and usage of these solutions. As businesses continue to generate vast amounts of data, the ability to effectively manage and analyze it in a timely and cost-efficient manner is becoming a critical success factor. To capitalize on this opportunity, companies must focus on addressing connectivity challenges through investments in robust infrastructure and partnerships with reliable service providers. Additionally, offering user-friendly, customizable solutions that cater to various industries and business sizes will be essential for market differentiation and customer retention. Overall, the market presents significant growth potential for companies that can effectively navigate these challenges and meet the evolving needs of data-driven organizations.

What will be the Size of the Saas-Based Business Analytics Market during the forecast period?

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Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Performance benchmarking and access control lists enable organizations to measure and improve their operational efficiency, while alerting systems and audit trails ensure data security and compliance. Collaborative analytics and predictive modeling, fueled by machine learning algorithms, offer new opportunities for identifying trends and making informed decisions. Advanced analytics techniques such as data mining and statistical modeling provide deeper insights into complex data sets. Interactive data exploration and custom reporting features allow users to gain valuable insights through self-service analytics. Data integration methods, user role management, and data governance frameworks ensure data accuracy and consistency. Workflow automation and real-time dashboards offer actionable insights in a timely manner, while cloud-based platforms provide scalability and flexibility. Data transformation processes and data validation rules ensure data quality, and reporting frequency options cater to diverse business needs. Big data processing and data visualization techniques offer new possibilities for gaining insights from vast amounts of data. For instance, a retail company was able to increase sales by 15% through predictive analytics, which helped them optimize inventory levels and pricing strategies. According to industry reports, the market is expected to grow by over 12% annually in the coming years.

How is this Saas-Based Business Analytics Industry segmented?

The saas-based business analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. End-userRetailBFSITelecomHealthcareOthersGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.In the retail industry, supply chain management (SCM) has become increasingly complex with the rise of e-commerce and the need for real-time data analysis. Retailers are turning to business analytics solutions to optimize their operations and make informed decisions. These solutions offer features such as performance benchmarking, access control lists, alerting systems, audit trails, collaborative analytics, predictive modeling, and machine learning algorithms. Data encryption, advanced analytics techniques, interactive data exploration, data integration methods, user role management, custom reporting, data governance framework, workflow automation, data mining techniques, and dashboard customization are also essential components. According to recent industry reports, the retail analytics market is expected to grow by over 15% annually, as retailers seek to improve their competitive edge. For instance, Walmart, a leading retailer, has implemented a cloud-based retail analytics platform to streamline its SCM process and increase operational efficiency. This solution allows for real-time data integration, data transformation processes, and data validation rules, enabling the company to respond qu

This dataset contains a variety of publicly available real-life event logs. We derived two types of Petri nets for each event log with two state-of-the-art process miners : Inductive Miner (IM) and Split Miner (SM). Each event log-Petri net pair is intended for evaluating the scalability of existing conformance checking techniques.We used this data-set to evaluate the scalability of the S-Component approach for measuring fitness.

This repository contains supplemental results to the paper "On Strong Scaling and Open Source Tools
for Analyzing Atom Probe Tomography Data".

Specifically, the input parameter and result files from all synthetic benchmark studies.
The results file for all synthetic specimen benchmark studies are available from the
authors upon serious request.

The repository content is as follows:

Real world data:
Three triplets of *.tar.gz archive files document the settings and analysis files for the real world case studies
3013902 is the incipient specimen,
2763207 the intermediate specimen, and
3345501 the mature specimen.

Scripts.zip
Contains all batch scripts we used for performing the analyses as a queue

Synthetic data:

APTCrystallography.zip
Contains all results from the benchmarking of the reimplemented Vicente Araullo-Peters et al. method

FullVolumeTessellation.zip
Contains all results from the benchmarking of the Voro++ Voronoi volume tessellation method except for the HDF5/XDMF volume tessellation files of the 200 and 2000 million ion datasets

TwoPointStatistics.zip
Contains all results from benchmarking the computing of 2-point spatial statistics

TipSurfaceDescrSpatialStatistics.zip
Contains all results from benchmarking the alpha shape computation, descriptive spatial statistics, and clustering analyses

PARAPROBE.Results.2Mio.tar.gz
PARAPROBE.Results.20Mio.tar.gz
PARAPROBE.Results.200Mio.tar.gz
Contains all settings files and some results from benchmarking the hybrid implementation.

Unpack the individual repositories using tar through Linux console as follows:
tar -xvf

We would kindly like to ask you to use the following repository
to access source code of PARAPROBE in the future:

https://gitlab.mpcdf.mpg.de/mpie-aptfim-toolbox/paraprobe
https://paraprobe-toolbox.readthedocs.io/en/latest/

Only above repository will be updated in the future!
Only there new code additions and bugfixes are posted!
The examples folder of this repository contains a folder examples/tasks/paper14 which shows how
to run a workflow of paraprobe tools from this above repository to run analyses akin as reported in this paper.

Recommendations and bug reports to M. Kühbach are appreciated! Happy APT analyzing!

------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The PARAPROBE source code version we used for analyzing the synthetic and real world APT
specimens in the paper used an earlier development version of the PARAPROBE tool, the source code
is here:

PARAPROBE_20190117_VersionUsedForPaper.zip

https://github.com/mkuehbach/PARAPROBE
https://paraprobe.readthedocs.io/en/latest/

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Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, m

The set of event logs included, are aimed to support the evaluation of the performance of process discovery algorithms. The largest event logs in this data set have millions of events. If you need even bigger datasets, you can generate these yourself using the CPN Tools sources files included (*.cpn). Each file has two parameters nofcases (i.e., the number of process instances) and nofdupl (i.e., the number of times a process is replicated with unique new names).

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Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is forecast to increase by USD 189.4 million at a CAGR of 12.5% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing demand for digitalization in government operations. This trend is leading to an increased adoption of advanced technologies, such as artificial intelligence (AI) and machine learning, in open data management platforms. These technologies enable more efficient data processing, analysis, and dissemination, making it easier for governments to provide accessible and actionable data to the public. However, the market faces challenges related to data privacy concerns.
Additionally, there is a need for clear guidelines and regulations regarding the collection, storage, and sharing of open data to maintain transparency and trust with the public. Companies operating in this market can capitalize on the growing demand for digitalization and advanced technologies while addressing data privacy concerns to gain a competitive edge. With the growing availability of open data, ensuring the security and confidentiality of sensitive information is a major concern. Governments must implement robust security measures to protect data from unauthorized access, misuse, or theft. Computer vision and image recognition are transforming industries like healthcare and education.

What will be the Size of the Government Open Data Management Platform Market during the forecast period?

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The market for government open data management platforms continues to evolve, driven by the increasing importance of public data infrastructure and the need for effective data governance policies. Data privacy regulations are shaping the landscape, with a growing emphasis on data reuse promotion and performance benchmarking. Data aggregation methods and data usage patterns are under constant review, as transparency and system scalability become essential. Data storytelling techniques and data usability assessments are gaining traction, while data platform architecture and data integration tools are being refined. A recent study revealed a 25% increase in data accessibility features adoption among government agencies.

Industry growth is expected to reach 15% annually, as open data licensing, role-based access control, and data modeling techniques become standard. Data quality monitoring, data consistency, and data reliability remain key concerns, with data audit procedures and data integrity measures being implemented to address these challenges. Data contextualization and data visualization dashboards are essential for making sense of the vast amounts of data being generated, while open government initiatives continue to drive innovation and collaboration. Data security remains a priority, with privacy concerns driving the need for data mining and edge computing.

How is this Government Open Data Management Platform Industry segmented?

The government open data management platform 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.

End-user

  Large enterprises
  SMEs


Deployment

  On-premises
  Cloud-based


Component

  Solutions
  Services


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By End-user Insights

The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's data-driven business landscape, large enterprises are increasingly turning to government open data management platforms to unlock valuable insights and fuel innovation. These platforms enable organizations to access, manage, and analyze vast amounts of data published by government agencies. By integrating government open data with their internal data, businesses can gain a deeper understanding of market trends, consumer behavior, and emerging opportunities. Data interoperability and version control ensure seamless integration of diverse data sources, while data migration strategies facilitate the transfer of data between systems. Data lineage tracking and metadata management provide transparency into the origin and evolution of data, enabling data provenance management and data discovery. Advanced process control and time series forecasting are integral to this evolution, with machine learning algorithms and deep learning frameworks powering predictive analytics tools.

Structured data management, data clea

The High Performance Computing (HPC) and High Performance Data Analytics (HPDA) Marketsize was valued at USD 46.01 USD billion in 2023 and is projected to reach USD 84.65 USD billion by 2032, exhibiting a CAGR of 9.1 % during the forecast period. High-Performance Computing (HPC) refers to the use of advanced computing systems and technologies to solve complex and large-scale computational problems at high speeds. HPC systems utilize powerful processors, large memory capacities, and high-speed interconnects to execute vast numbers of calculations rapidly. High-Performance Data Analytics (HPDA) extends this concept to handle and analyze big data with similar performance goals. HPDA encompasses techniques like data mining, machine learning, and statistical analysis to extract insights from massive datasets. Types of HPDA include batch processing, stream processing, and real-time analytics. Features include parallel processing, scalability, and high throughput. Applications span scientific research, financial modeling, and large-scale simulations, addressing challenges that require both intensive computing and sophisticated data analysis. Recent developments include: December 2023: Lenovo, a company offering computer hardware, software, and services, extended the HPC system “LISE” at the Zuse Institute Berlin (ZIB). This expansion would provide researchers at the institute with high computing power required to execute data-intensive applications. The major focus of this expansion is to enhance the energy efficiency of “LISE”. , August 2023: atNorth, a data center services company, announced the acquisition of Gompute, the HPC cloud platform offering Cloud HPC services, as well as on-premises and hybrid cloud solutions. Under the terms of the agreement, atNorth would add Gompute’s data center to its portfolio., July 2023: HCL Technologies Limited, a consulting and information technology services firm, extended its collaboration with Microsoft Corporation to provide HPC solutions, such as advanced analytics, ML, core infrastructure, and simulations, for clients across numerous sectors., June 2023: Leostream, a cloud-based desktop provider, launched new features designed to enhance HPC workloads on AWS EC2. The company develops zero-trust architecture around HPC workloads to deliver cost-effective and secure resources to users on virtual machines., November 2022: Intel Corporation, a global technology company, launched the latest advanced processors for HPC, artificial intelligence (AI), and supercomputing. These processors include data center version GPUs and 4th Gen Xeon Scalable CPUs.. Key drivers for this market are: Technological Advancements Coupled with Robust Government Investments to Fuel Market Growth. Potential restraints include: High Cost and Skill Gap to Restrain Industry Expansion. Notable trends are: Comprehensive Benefits Provided by Hybrid Cloud HPC Solutions to Aid Industry Expansion .

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US Deep Learning Market Size 2025-2029

The deep learning market size in US is forecast to increase by USD 5.02 billion at a CAGR of 30.1% between 2024 and 2029.

The deep learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) in various industries for advanced solutioning. This trend is fueled by the availability of vast amounts of data, which is a key requirement for deep learning algorithms to function effectively. Industry-specific solutions are gaining traction, as businesses seek to leverage deep learning for specific use cases such as image and speech recognition, fraud detection, and predictive maintenance. Alongside, intuitive data visualization tools are simplifying complex neural network outputs, helping stakeholders understand and validate insights. 


However, challenges remain, including the need for powerful computing resources, data privacy concerns, and the high cost of implementing and maintaining deep learning systems. Despite these hurdles, the market's potential for innovation and disruption is immense, making it an exciting space for businesses to explore further. Semi-supervised learning, data labeling, and data cleaning facilitate efficient training of deep learning models. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. 

What will be the Size of the market During the Forecast Period?

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Deep learning, a subset of machine learning, continues to shape industries by enabling advanced applications such as image and speech recognition, text generation, and pattern recognition. Reinforcement learning, a type of deep learning, gains traction, with deep reinforcement learning leading the charge. Anomaly detection, a crucial application of unsupervised learning, safeguards systems against security vulnerabilities. Ethical implications and fairness considerations are increasingly important in deep learning, with emphasis on explainable AI and model interpretability. Graph neural networks and attention mechanisms enhance data preprocessing for sequential data modeling and object detection. Time series forecasting and dataset creation further expand deep learning's reach, while privacy preservation and bias mitigation ensure responsible use.

In summary, deep learning's market dynamics reflect a constant pursuit of innovation, efficiency, and ethical considerations. The Deep Learning Market in the US is flourishing as organizations embrace intelligent systems powered by supervised learning and emerging self-supervised learning techniques. These methods refine predictive capabilities and reduce reliance on labeled data, boosting scalability. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. Sophisticated feature extraction algorithms now enable models to isolate patterns with high precision, particularly in applications such as image classification for healthcare, security, and retail.

How is this market segmented and which is the largest segment?

The market 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.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  North America

    US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. In the realm of artificial intelligence (AI) and machine learning, image recognition, a subset of computer vision, is gaining significant traction. This technology utilizes neural networks, deep learning models, and various machine learning algorithms to decipher visual data from images and videos. Image recognition is instrumental in numerous applications, including visual search, product recommendations, and inventory management. Consumers can take photographs of products to discover similar items, enhancing the online shopping experience. In the automotive sector, image recognition is indispensable for advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Furthermore, image recognition plays a pivotal role in augmented reality (AR) and virtual reality (VR) applications, where it tracks physical objects and overlays digital content onto real-world scenarios. The model training process involves the backpropagation algorithm, which calculates