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

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

Methodology

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

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

Description of the data in this data set

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

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

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

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

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

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

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

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

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

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

Licenses or restrictionsCC-BY

For more info, see README.txt

The North America Machine Learning (ML) Market size was valued at USD 19.20 USD billion in 2023 and is projected to reach USD 172.15 USD billion by 2032, exhibiting a CAGR of 36.8 % during the forecast period. The increase in demand for efficient data analytics solutions, the growth of cloud computing, and the proliferation of IoT devices are driving the market's growth. Machine learning (ML) is a discipline of artificial intelligence that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention. Machine learning methods enable computers to operate autonomously without explicit programming. ML applications are fed with new data, and they can independently learn, grow, develop, and adapt. Machine learning derives insightful information from large volumes of data by leveraging algorithms to identify patterns and learn in an iterative process. ML algorithms use computation methods to learn directly from data instead of relying on any predetermined equation that may serve as a model. Machine learning is used today for a wide range of commercial purposes, including suggesting products to consumers based on their past purchases, predicting stock market fluctuations, and translating text from one language to another. The North America Machine Learning (ML) Market is primarily driven by the increasing adoption of essential services like security information and cloud applications. Key drivers for this market are: Growing Adoption of Mobile Commerce to Augment the Demand for Virtual Fitting Room Tool . Potential restraints include: Lack of Privacy and Privacy Violations in AI and ML Applications to Restrain Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

This data set was generated in accordance with the semiconductor industry and contains simulated electrical test data of manufactured devices, like resistances, voltages, etc. Devices are manufactured on round wafers, i.e. slices of semiconductor material. If the test values of devices form an interesting spatial pattern on the wafer, this might result from deviations during processing and hence, must be traced.

A bunch of wafers is aggregated to a so-called lot. The dataset contains 4 training lots and 1 test lot à 200 wafers. In this dataset, each wafer contains approx. 17000 devices. Each device is assigned 10 different tests, which show one out of 5 patterns each, simulated with (ring, spot, trend, twospots and crescent) and without ('_pure' suffix) a randomized amount of Gaussian white noise.

For pattern recognition, each test column on the wafer is regarded as a spatial image, the so-called wafermap. In particular, a wafermap is an image, where the position of each pixel is uniquely identified through the x- and y-coordinates and the coloring is described by a value of one of the test columns.

Each wafermap in a lot is uniquely identified by its wafer number. Classes or clusters should not be assigned to each data row separately, but rather to wafermaps. The data represents wafermaps as spatial objects, with x- and y- coordinates. The values of such a map are characterized by all values in the corresponding test column for given lot and wafer numbers.

The five classes (patterns) are characterized as follows:

1. ring: a ring pattern along the border of the wafer
2. spot: a single circular or elliptic spot, placed randomly on the wafer
3. trend: a constant gradient over the wafer (with changes w.r.t. direction)
4. twospots: two spots at the left and right edge of the wafer
5. crescent: a crescent-shaped area on the right edge of the wafer

1. Source Information: (a) Authors: Martin Pleschberger: martin.pleschberger@k-ai.at Michael Scheiber: michael.scheiber@k-ai.at Stefan Schrunner: stefan.schrunner@k-ai.at KAI - Kompetenzzentrum Automobil- und Industrie- elektronik GmbH, Villach, Austria (b) Date: November, 2018 (c) Acknowledgement: The work has been performed in the project Power Semi- conductor and Electronics Manufacturing 4.0 (SemI40), under grant agreement No 692466. The project is co-funded by grants from Austria (BMVIT-IKT der Zukunft, FFG project no. 853338), Germany, Italy, France, Portugal and - Electronic Component Systems for European Leadership Joint Undertaking (ECSEL JU).

2. Past Usage: () M. Pleschberger: "Runtime Optimization for Automated Pattern Analysis", Master Thesis, Alpen-Adria Universit�t, Klagenfurt, 2018. (*) S. Schrunner, O. Bluder, A. Zernig, A. Kaestner, R. Kern: "A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data", IEEE International Conference on Machine Learning and Applications, Florida, USA, 2018.

3. Relevant Information:

   This data set was generated in accordance with the semiconductor industry and contains simulated electrical test data of manufactured devices, like resistances, voltages, etc. Devices are manufactured on round wafers, i.e. slices of semiconductor material. If the test values of devices form an interesting spatial pattern on the wafer, this might result from deviations during processing and hence, must be traced.

   A bunch of wafers is aggregated to a so-called lot. The dataset contains 4 training lots and 1 test lot � 200 wafers. In this dataset, each wafer contains approx. 17000 devices. Each device is assigned 10 different tests, which show one out of 5 patterns each, simulated with (ring, spot, trend, twospots and crescent) and without ('_pure' suffix) a randomized amount of Gaussian white noise.

   For pattern recognition, each test column on the wafer is regarded as a spatial image, the so-called wafermap. In particular, a wafermap is an image, where the position of each pixel is uniquely identified through the x- and y-coordinates and the coloring is described by a value of one of the test columns.

   Each wafermap in a lot is uniquely identified by its wafer number. Classes or clusters should not be assigned to each data row separately, but rather to wafermaps. The data represents wafermaps as spatial objects, with x- and y- coordinates. The values of such a map are characterized by all values in the corresponding test column for given lot and wafer numbers.

   The five classes (patterns) are characterized as follows: 1.) ring: a ring pattern along the border of the wafer 2.) spot: a single circular or elliptic spot, placed randomly on the wafer 3.) trend: a constant gradient over the wafer (with changes w.r.t. direction) 4.) twospots: two spots at the left and right edge of the wafer 5.) crescent: a crescent-shaped area on the right edge of the wafer

   Additional information on electrical wafer test data, their background and structure, can be found in the sources () and (*) (referred in 'Past Usage').

   The data is provided in 4 training and 1 test CSV file. Each dataset contains data of one lot.

The global Data Application Solution market size was valued at USD 70,530 million in 2025 and is expected to expand at a compound annual growth rate (CAGR) of 12.8% from 2025 to 2033. The market growth is attributed to the increasing adoption of data-driven decision-making, rising demand for real-time data insights, and growing need for data security and privacy. The increasing adoption of cloud-based data application solutions is a major trend in the market. Cloud-based solutions offer flexibility, scalability, and cost-effectiveness, making them attractive to businesses of all sizes. The growing popularity of artificial intelligence (AI) and machine learning (ML) is also driving the market growth. AI and ML algorithms can analyze large volumes of data and identify patterns and insights that would be difficult or impossible to find manually. This information can be used to improve decision-making, optimize processes, and identify new opportunities. The major players in the market include IBM, Microsoft, Google, Amazon Web Services, SAP, Oracle, Cloudera, Salesforce, Snowflake, Domo, Alteryx, and Palantir. These companies offer a wide range of data application solutions, including data management solutions, data analysis solutions, and application solutions.

The market for Data Analysis Application Solutions is projected to reach $345.8 million by 2033, exhibiting a CAGR of 12.3% during the forecast period (2023-2033). The increasing adoption of cloud-based data analysis solutions, the growing need for data-driven decision-making, and the rising adoption of big data analytics are the key factors propelling market growth. The increasing adoption of cloud-based data analysis solutions is one of the major drivers of market growth. Cloud-based solutions provide several benefits, such as reduced IT costs, increased flexibility, and accessibility. The growing need for data-driven decision-making is also contributing to market growth. Businesses are increasingly recognizing the importance of data in making informed decisions. Data analysis solutions provide businesses with the ability to analyze data and gain insights, helping them make better decisions. The rising adoption of big data analytics is another factor driving market growth. Big data analytics allows businesses to analyze large volumes of data, identifying patterns and trends that would not be possible to identify with traditional data analysis methods.

Images have always played a vital role in human life because vision is the most crucial sense for humans. As a result, image processing has a wide range of applications. Photographs are everywhere nowadays, more than ever, and it is quite easy for anyone to make a large number of photographs utilizing a smart phone. Given the complexities of vision, machine learning has emerged as a critical component of intelligent computer vision programmed when adaptability is required. Deep learning is a subfield of artificial intelligence that combines a number of statistical, probabilistic, and optimisation techniques to enable computers to "learn" from previous examples and find difficult-to-detect patterns in big, noisy, or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex promote and genomic measurements. An innovative integration of machine learning in image processing is very likely to have a great benefit to the field, which will contribute to a better understanding of complex images. This capability is especially well-suited to medical applications that rely on complicated promote and genomic measurements. A novel application of deep learning in image processing is extremely likely to benefit the field and lead to a better understanding of complicated images. A country’s economy is dependent on agricultural productivity. The identification of plant diseases is critical for reducing production losses and enhancing agricultural product quality. Traditional methods are dependable, but they necessitate the use of a human resource to visually observe plant leaf patterns and identify disease. Traditional methods take more time and need more labour. Early identification of plant disease utilising automated procedures will reduce productivity loss in large farm fields. We propose a vision-based automatic detection of plant disease detection utilising Image Processing Technique in this research. By recognising the colour feature of the leaf region, image processing algorithms are developed to detect plant illness or disease. The K mean algorithm is utilised for colour segmentation, whereas the GLCM algorithm is employed for disease classification. Plant infection based on vision yielded efficient results and Promising performance.

Manipulation Pattern Detection Market Outlook

According to our latest research, the global Manipulation Pattern Detection market size reached USD 2.41 billion in 2024, driven by the escalating sophistication of fraudulent activities and the increasing adoption of digital technologies across industries. The market is expected to grow at a robust CAGR of 16.7% from 2025 to 2033, reaching an estimated USD 10.61 billion by 2033. This remarkable growth is propelled by the rising demand for advanced analytics, machine learning, and artificial intelligence (AI) solutions to proactively identify and mitigate manipulation patterns in real-time, ensuring the integrity of financial transactions and digital operations across diverse sectors.

The primary growth driver for the Manipulation Pattern Detection market is the surge in cyber threats and fraudulent activities targeting organizations worldwide. As digital transformation accelerates, businesses are increasingly exposed to sophisticated manipulation tactics, including market abuse, insider trading, and cyber-attacks. The proliferation of online transactions, digital banking, and e-commerce platforms has created new vulnerabilities, necessitating the deployment of advanced manipulation pattern detection solutions. These technologies leverage machine learning algorithms, behavioral analytics, and big data to detect anomalies, unusual patterns, and suspicious activities, enabling organizations to respond swiftly and minimize financial losses. Additionally, regulatory mandates and compliance requirements in sectors such as BFSI, healthcare, and government are compelling organizations to invest in robust detection systems, further fueling market expansion.

Another significant factor contributing to market growth is the rapid evolution of artificial intelligence and machine learning technologies. These advancements have transformed manipulation pattern detection from traditional rule-based systems to dynamic, self-learning platforms capable of adapting to emerging threats. AI-powered solutions can analyze vast volumes of structured and unstructured data in real-time, uncover hidden patterns, and enhance the accuracy of threat detection. The integration of AI with cloud computing has further democratized access to sophisticated detection tools, enabling small and medium enterprises (SMEs) to benefit from enterprise-grade security. As organizations increasingly recognize the value of predictive analytics and real-time monitoring, the adoption of manipulation pattern detection solutions is expected to surge across various applications, including fraud detection, financial analysis, and behavioral analytics.

The Manipulation Pattern Detection market is also witnessing substantial growth due to the increasing emphasis on data privacy and security. With the implementation of stringent data protection regulations such as GDPR, CCPA, and other regional frameworks, organizations are under pressure to safeguard sensitive information and maintain customer trust. Manipulation pattern detection solutions play a critical role in ensuring compliance by providing comprehensive monitoring, reporting, and audit capabilities. Moreover, the rise of remote work, cloud adoption, and digital collaboration tools has expanded the attack surface, making it imperative for organizations to invest in advanced detection technologies. The growing awareness of the financial and reputational risks associated with manipulation and cyber threats is driving continuous innovation and investment in this market.

Regionally, North America dominates the Manipulation Pattern Detection market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading technology providers, a mature digital infrastructure, and high regulatory compliance standards contribute to North America's leadership. Europe is witnessing rapid adoption due to stringent data protection laws and increased cybercrime incidents, while Asia Pacific is emerging as a lucrative market driven by the digitalization of financial services, expanding e-commerce, and government initiatives to enhance cybersecurity. Latin America and the Middle East & Africa are also experiencing steady growth, supported by rising investments in digital transformation and security solutions.

Component Analysis

The Manipulation Pattern Detection market is segmented by component into Software and Services&

Background: Chemicals in consumer products are a major contributor to human chemical co-exposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical co-exposures in order to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this has been a major challenge. Objectives: We aim to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. Methods: We applied frequent itemset mining on an integrated dataset linking consumer product chemical ingredient data with product purchasing data from sixty thousand households to identify chemical combinations resulting from co-use of consumer products. Results: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Lastly, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. Discussion: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. This dataset is associated with the following publication: Stanfield, Z., C. Addington, K. Dionisio, D. Lyons, R. Tornero-Velez, K. Phillips, T. Buckley, and K. Isaacs. Mining of consumer product and purchasing data to identify potential chemical co-exposures.. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 129(6): N/A, (2021).

Machine Learning Platform Market Outlook

According to our latest research, the global Machine Learning Platform market size reached USD 20.1 billion in 2024, reflecting a robust surge in adoption across diverse industries. The market is expected to expand at a strong CAGR of 34.5% from 2025 to 2033, positioning the sector to achieve a remarkable USD 264.3 billion by 2033. This exceptional growth trajectory is driven by increasing enterprise investments in artificial intelligence, the proliferation of big data, and the ongoing digital transformation initiatives worldwide.

One of the primary growth factors for the Machine Learning Platform market is the exponential rise in data generation across industries. Organizations are collecting vast volumes of structured and unstructured data from multiple sources, including IoT devices, social media, enterprise applications, and more. This abundance of data necessitates advanced analytics tools capable of extracting actionable insights, and machine learning platforms have emerged as the preferred solution. The ability of these platforms to automate complex data analysis, identify patterns, and make accurate predictions is transforming decision-making processes and driving operational efficiency. Furthermore, the integration of machine learning with cloud computing has democratized access to powerful analytics tools, enabling even small and medium enterprises to leverage advanced AI capabilities without substantial upfront investments.

Another significant driver propelling the Machine Learning Platform market is the rapid advancement in algorithmic models and computing power. Innovations in deep learning, reinforcement learning, and natural language processing have expanded the scope of machine learning applications beyond traditional use cases. Enterprises are now deploying machine learning platforms for fraud detection, predictive maintenance, image recognition, and personalized marketing, among other functions. The growing sophistication of machine learning algorithms, combined with the scalability of cloud-based infrastructure, is enabling organizations to solve complex business challenges more efficiently. Additionally, the emergence of open-source machine learning frameworks and libraries is lowering the barriers to entry, fostering a vibrant ecosystem of developers and data scientists.

The surge in regulatory compliance requirements and the increasing focus on risk management are also fueling the demand for machine learning platforms. In highly regulated sectors such as banking, financial services, and healthcare, organizations are leveraging machine learning to enhance fraud detection, ensure compliance, and mitigate operational risks. Machine learning models can analyze vast datasets in real time, flag anomalies, and generate alerts, thereby improving the accuracy and speed of compliance-related processes. Moreover, as data privacy regulations become more stringent, machine learning platforms are incorporating advanced security features and explainable AI capabilities to ensure transparency and accountability in automated decision-making.

From a regional perspective, North America currently dominates the global Machine Learning Platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, a mature digital infrastructure, and significant investments in AI research are key factors underpinning North America's leadership. However, the Asia Pacific region is expected to register the fastest CAGR during the forecast period, driven by rapid digitalization, rising adoption of cloud technologies, and supportive government policies promoting AI innovation. Europe, meanwhile, is witnessing steady growth, supported by initiatives such as the European Union's AI strategy and increasing enterprise adoption across sectors including manufacturing, healthcare, and finance.

Component Analysis

The Machine Learning Platform market is segmented by component into Software and Services, each playing a critical role in driving the market’s overall growth. The software segment encompasses a wide array of machine learning tools, frameworks, and libraries that facilitate model development, training, deployment, and monitoring. These software solutions offer user-friendly interfaces, automated workflows, and integration capabilities with popular data

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Predictive AI In Education Market Size 2025-2029

The predictive AI in education market size is valued to increase by USD 7.6 billion, at a CAGR of 20.8% from 2024 to 2029. Increasing demand for personalized learning at scale will drive the predictive AI in education market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Technology - Deep learning and ML segment was valued at USD 2.11 billion in 2023
By Component - Solutions segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 279.90 million
Market Future Opportunities: USD 7601.70 million
CAGR from 2024 to 2029 : 20.8%

Market Summary

The market is experiencing significant growth due to the increasing demand for personalized learning at scale. This trend is driven by the integration of generative AI with predictive analytics, enabling educators to identify students' learning patterns and provide tailored instruction. However, the adoption of predictive AI in education also raises concerns regarding data privacy and security. One real-world business scenario illustrates the benefits of predictive AI in education. A large university implemented a predictive analytics system to identify students at risk of dropping out. By analyzing student data, the system identified patterns and flagged students who were struggling academically.
The university then intervened with targeted interventions, such as academic counseling and tutoring, which led to a 15% increase in student retention. Despite these benefits, the implementation of predictive AI in education comes with challenges. Data privacy and security are major concerns, as student data is sensitive and must be protected. Additionally, there is a need for transparency and accountability in the use of predictive AI, as well as ensuring that the technology does not perpetuate biases or discrimination. In conclusion, the market is poised for growth, driven by the demand for personalized learning and the integration of AI with predictive analytics.
However, concerns regarding data privacy and security must be addressed to ensure the ethical and effective use of this technology.

What will be the Size of the Predictive AI In Education Market during the forecast period?

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How is the Predictive AI In Education Market Segmented ?

The predictive AI in education 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.

Technology

  Deep learning and ML
  Natural language processing (NLP)


Component

  Solutions
  Services


End-user

  K-12 education
  Higher education
  Corporate training and learning


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Technology Insights

The deep learning and ml segment is estimated to witness significant growth during the forecast period.

Predictive AI technologies are revolutionizing the education sector, with the deep learning and machine learning segments driving significant growth. Deep learning, a sophisticated form of machine learning, is a key technology in The market. It uses intricate neural networks with numerous layers to analyze extensive and intricate datasets, yielding precise predictions and valuable insights that are typically unobtainable through conventional analytical methods. In education, deep learning models play a crucial role in processing a diverse range of student data, such as academic records, learning management system interaction logs, and even unstructured data like video recordings of classroom sessions or text from online forums.

These models employ natural language processing, cognitive load assessment, and other advanced techniques to deliver personalized feedback, adaptive learning pathways, and automated essay scoring. According to a recent study, the market is projected to grow by 25% annually, with applications ranging from progress tracking systems and early warning systems to intelligent tutoring systems and resource allocation models. Additionally, these technologies offer benefits like curriculum optimization, teacher workload reduction, and explainable AI, ensuring a more effective and engaging learning experience for students.

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The Deep learning and ML segment was valued at USD 2.11 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during t

Customer Expansion Analytics AI Market Outlook

According to our latest research, the global Customer Expansion Analytics AI market size reached USD 2.72 billion in 2024, reflecting the rapid adoption of AI-driven analytics across customer-centric industries. The market is expected to grow at a robust CAGR of 18.5% from 2025 to 2033, with the total market size forecasted to reach USD 14.82 billion by 2033. This exceptional growth is primarily driven by enterprises' increasing focus on leveraging artificial intelligence to optimize customer retention, upselling, and cross-selling strategies, thus maximizing customer lifetime value and profitability.

The proliferation of digital channels and the exponential growth in customer data have significantly contributed to the expansion of the Customer Expansion Analytics AI market. Organizations are increasingly recognizing the importance of harnessing advanced analytics to gain actionable insights into customer behavior, preferences, and purchasing patterns. This shift is fueled by the growing demand for personalized customer experiences, which is only possible through the real-time processing and analysis of massive datasets. The integration of AI technologies enables businesses to predict customer needs, proactively address pain points, and tailor their offerings, resulting in improved customer satisfaction and higher retention rates.

Another major growth factor is the rising adoption of cloud-based analytics platforms, which provide scalability, flexibility, and cost-effectiveness for enterprises of all sizes. Cloud deployment models have democratized access to sophisticated AI tools, enabling even small and medium-sized enterprises (SMEs) to leverage advanced customer expansion analytics without the need for significant upfront investments in hardware or infrastructure. The emergence of AI-powered self-service analytics and no-code/low-code platforms has further lowered the barriers to entry, empowering business users across departments to extract value from customer data and make data-driven decisions that drive expansion.

Furthermore, the increasing emphasis on customer-centric business strategies across industries such as BFSI, retail & e-commerce, healthcare, and telecommunications is accelerating the deployment of AI-based customer analytics solutions. Companies are leveraging these solutions not only to reduce customer churn but also to identify upselling and cross-selling opportunities, segment customers more effectively, and optimize marketing campaigns. The rapid advancements in machine learning algorithms, natural language processing, and predictive analytics are enhancing the accuracy and effectiveness of these solutions, providing businesses with a competitive edge in today’s dynamic market landscape.

From a regional perspective, North America continues to dominate the Customer Expansion Analytics AI market due to its early adoption of advanced technologies, robust digital infrastructure, and strong presence of leading AI vendors. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, increasing investments in AI, and the expanding e-commerce sector. Europe also holds a significant share, driven by stringent data protection regulations and the growing focus on customer experience management. Latin America and the Middle East & Africa are witnessing steady growth as enterprises in these regions increasingly embrace AI-driven analytics to enhance customer engagement and drive business expansion.

Component Analysis

The Customer Expansion Analytics AI market by component is segmented into software, hardware, and services. The software segment is the largest contributor to market revenue, accounting for a significant share in 2024. This dominance is attributed to the widespread adoption of AI-powered analytics platforms, customer data platforms (CDPs), and customer relationship management (CRM) solutions that integrate machine learning and predictive analytics capabilities. These software solutions enable organizations to analyze vast amounts of customer data, identify patterns, and derive actionable insights that drive customer expansion strategies. Continuous advancements in AI algorithms, data visualization tools, and automation features are further propelling the growth of the software segment.

The hardware segment, although smaller compared

In this project, I will analyze large publicly available datasets using machine learning to reveal new associations that can help refine existing theories or develop new theories in the social and management sciences. In the first project, I discuss some of the limitations of traditional statistical approaches and demonstrate how we can solve them using machine learning. In the second project, I demonstrate how machine learning can sieve through a large amount of data to identify patterns. In the third project, I document that machine learning models can be used to generate hypotheses that are subsequently validated by traditional methods (e.g., correlational and experimental studies). Machine learning models take a long time to build, requiring considerable software writing. However, these models are reusable. In the fourth project, I demonstrate how a machine learning model built in the third project can be reused for a different topic.

Vector Database for Anomaly Patterns Market Outlook

According to our latest research, the global Vector Database for Anomaly Patterns market size reached USD 1.46 billion in 2024, demonstrating robust demand across industries. The market is expected to expand at a CAGR of 18.2% from 2025 to 2033, projecting a value of USD 6.45 billion by 2033. This growth is primarily driven by the increasing adoption of AI-powered anomaly detection solutions, the need for real-time data analytics, and the proliferation of complex data environments across sectors such as BFSI, healthcare, and IT & telecommunications.

The accelerating digital transformation across industries is a significant growth factor for the Vector Database for Anomaly Patterns market. Organizations are increasingly relying on real-time data analysis to identify abnormal patterns, detect fraud, and mitigate security threats. The adoption of vector databases, which are optimized for high-dimensional data and AI-driven anomaly detection, is rapidly increasing as enterprises seek scalable solutions to manage and analyze vast, complex datasets. The rise in sophisticated cyber-attacks, financial fraud, and the need for predictive maintenance in manufacturing further fuel the demand for advanced anomaly detection platforms. As a result, enterprises are investing heavily in vector database technologies to enhance operational efficiency, reduce risk, and ensure data integrity. Additionally, the integration of machine learning and artificial intelligence with vector databases is enabling organizations to detect subtle anomalies more accurately, which was previously challenging with traditional database systems.

Another key growth driver is the increasing deployment of cloud-based vector database solutions. The cloud offers scalability, flexibility, and cost-efficiency, making it an attractive option for organizations of all sizes. Cloud deployments enable real-time anomaly detection across distributed environments, supporting remote workforces and decentralized operations. This shift is particularly pronounced in sectors such as IT & telecommunications, retail & e-commerce, and BFSI, where the volume, velocity, and variety of data are exceptionally high. The growing ecosystem of cloud-native vector database providers and the availability of managed services further accelerate market growth. Moreover, the integration of vector databases with cloud-based AI and analytics platforms is helping enterprises derive actionable insights from their data, optimize processes, and enhance customer experiences.

The regulatory landscape is also contributing to market expansion. Stringent compliance requirements in sectors like BFSI and healthcare are compelling organizations to adopt advanced anomaly detection systems that can ensure data privacy and security. Vector databases, with their ability to process and analyze high-dimensional data in real-time, are becoming essential tools for regulatory compliance and risk management. Furthermore, the increasing awareness of data-driven decision-making and the need to safeguard critical infrastructure are prompting governments and enterprises to invest in state-of-the-art anomaly detection solutions. As digital ecosystems become more interconnected and complex, the demand for robust, scalable, and intelligent vector database solutions is expected to rise substantially.

Regionally, North America dominates the Vector Database for Anomaly Patterns market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading technology providers, high adoption of AI and machine learning, and a strong focus on cybersecurity in the United States and Canada underpin North America's leadership. Europe is witnessing significant growth due to stringent data protection regulations and the rapid adoption of digital technologies in sectors such as finance and healthcare. Meanwhile, the Asia Pacific region is emerging as a high-growth market, driven by the digitalization of enterprises, increasing investments in AI, and the expansion of e-commerce and financial services. Latin America and the Middle East & Africa are also experiencing steady growth, supported by ongoing digital transformation initiatives and the rising need for advanced anomaly detection solutions.

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.

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

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

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

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

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

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

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

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

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

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

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AI In Genomics Market Size 2025-2029

The ai in genomics market size is valued to increase by USD 1.73 billion, at a CAGR of 32.6% from 2024 to 2029. Precipitous decline in sequencing costs and subsequent genomic data will drive the ai in genomics market.

Market Insights

Europe dominated the market and accounted for a 32% growth during the 2025-2029.
By Component - Software segment was valued at USD 87.00 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million 
Market Future Opportunities 2024: USD 1729.20 million
CAGR from 2024 to 2029 : 32.6%

Market Summary

The market is experiencing significant growth due to the precipitous decline in sequencing costs and subsequent genomic data proliferation. This data deluge is driving the need for advanced analytical tools to make sense of the complex genetic information. Enter generative AI and foundation models, which are increasingly being adopted in the biological domain to analyze and interpret genomic data. These models can identify patterns, make predictions, and even generate new sequences, revolutionizing research and development in genomics. However, the implementation of AI in genomics is not without challenges. The labyrinth of data privacy, security, and complex regulatory frameworks presents significant hurdles. For instance, in a pharmaceutical company, AI is used to optimize the supply chain by predicting demand for specific genetic therapies. This involves analyzing vast amounts of patient data, raising concerns around data security and privacy. Additionally, regulatory compliance adds another layer of complexity, requiring stringent data handling protocols. Despite these challenges, the potential benefits of AI in genomics are immense, from accelerating drug discovery to improving patient outcomes. The future of genomics lies in harnessing the power of AI to unlock the secrets of the human genome.

What will be the size of the AI In Genomics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, revolutionizing various sectors such as comparative genomics, population genetics studies, and infectious disease genomics. Big data analytics plays a pivotal role in processing vast genomic data, enabling faster and more accurate discoveries. Microbial genomics, cancer genomics, and structural genomics are among the fields benefiting from advanced algorithm optimization and high-performance computing. In the realm of human genomics, data mining methods and statistical genetics methods uncover hidden patterns and correlations, while explainable AI methods ensure transparency and interpretability. Parallel computing and predictive modeling enable real-time analysis and model validation techniques ensure accuracy. Variant annotation databases facilitate quicker identification of genetic mutations, contributing to personalized medicine and diagnostics. Cloud computing platforms provide scalable and cost-effective genomic data storage solutions, ensuring easy access to data for researchers and clinicians. Synthetic biology and plant genomics also gain from AI, with applications ranging from gene editing to crop improvement. Data sharing initiatives foster collaboration and accelerate research progress. In the boardroom, AI in Genomics translates to significant improvements in research efficiency and accuracy. For instance, companies have reported a substantial reduction in processing time, enabling them to bring products to market faster and stay competitive. The integration of AI in genomics is a strategic investment, offering potential cost savings, increased productivity, and improved patient outcomes.

Unpacking the AI In Genomics Market Landscape

In the dynamic realm of genomics, Artificial Intelligence (AI) is revolutionizing various applications, including genotype-phenotype association and therapeutic target validation. AI-driven solutions enable a 30% increase in efficiency compared to traditional methods, resulting in accelerated research and development. CRISPR gene editing benefits from AI integration, achieving a 25% improvement in precision and accuracy. Data security measures are reinforced through AI's ability to monitor and analyze access patterns, reducing potential breaches by 40%. Bioinformatics pipelines, diagnostics test development, and machine learning algorithms leverage AI for enhanced performance and accuracy. Protein-protein interactions, epigenetic modifications, and systems biology modeling gain new insights through AI-powered analysis. Personalized medicine approaches, gene expression profiling, and protein structure prediction are transformed by AI, leading to improved ROI and compliance alignment with data privacy regulatio

Data Detection and Response Market Outlook

According to our latest research, the global Data Detection and Response market size reached USD 3.8 billion in 2024. The market is poised for rapid expansion, with an anticipated CAGR of 18.7% from 2025 to 2033. By the end of the forecast period, the Data Detection and Response market is expected to attain a value of USD 17.2 billion. This robust growth trajectory is primarily driven by the escalating sophistication of cyber threats, stringent regulatory requirements, and the growing need for real-time data visibility and threat mitigation across diverse industries.

The primary growth factor propelling the Data Detection and Response market is the ever-evolving landscape of cyber threats. Organizations worldwide are grappling with increasingly complex and targeted attacks, such as ransomware, phishing, and advanced persistent threats (APTs), which require sophisticated detection and rapid response solutions. The proliferation of digital transformation initiatives, cloud adoption, and the expansion of remote work environments have significantly broadened the attack surface, making traditional security frameworks insufficient. As a result, enterprises are investing heavily in advanced Data Detection and Response platforms that leverage artificial intelligence, machine learning, and behavioral analytics to detect anomalies, minimize dwell time, and orchestrate swift incident responses. This shift towards proactive security postures, rather than reactive approaches, is a crucial driver in the market’s sustained growth.

Another significant factor fueling the expansion of the Data Detection and Response market is the tightening global regulatory environment. Regulatory bodies across regions, such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks in Asia Pacific, mandate rigorous data protection, breach notification, and compliance management practices. Organizations are compelled to implement robust detection and response mechanisms to avoid severe penalties and reputational damage resulting from non-compliance. This regulatory pressure is particularly prominent in data-sensitive sectors like BFSI, healthcare, and government, where the stakes of data breaches are exceptionally high. Consequently, compliance management and risk mitigation have become central to security strategies, further accelerating market adoption.

The integration of advanced technologies such as artificial intelligence, machine learning, and automation into Data Detection and Response solutions is also a pivotal growth catalyst. These technologies enable real-time threat intelligence, predictive analytics, and automated incident response, significantly reducing the time to detect and remediate security incidents. The ability to correlate vast volumes of data from disparate sources, identify patterns, and prioritize threats based on risk levels empowers security teams to operate more efficiently and effectively. Moreover, the rise of managed security services and cloud-based deployment models is democratizing access to cutting-edge Data Detection and Response capabilities, making them accessible to small and medium enterprises (SMEs) alongside large enterprises. This technological democratization is expected to drive widespread adoption and fuel market growth across industry verticals.

From a regional perspective, North America continues to dominate the Data Detection and Response market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced cybersecurity infrastructure, high digitalization rates, and the presence of major industry players. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid digital transformation, increasing cybersecurity investments, and heightened awareness of data protection regulations. Europe remains a significant market, driven by stringent GDPR compliance requirements and a strong focus on data privacy. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions ramp up their cybersecurity capabilities in response to rising threat levels.

Background and aimsPattern identification (PI) provides a basis for understanding disease symptoms and signs. The aims of this study are to extract features for identifying conventional PI types from the questionnaire data of patients with functional dyspepsia (FD) through supervised learning methods, and to compare them with another set of features for novel PI types identified with unsupervised learning.MethodsIn total, 153 patients with FD were invited to complete the Standardized Tool for Pattern Identification of Functional Dyspepsia (STPI-FD) questionnaire. Supervised analysis using support vector machine (SVM) was conducted to extract the most discriminative features. For unsupervised analysis, t-distributed stochastic neighbor embedding (t-SNE) and k-means clustering were applied to detect novel subgroups, and independent-samples t-tests were performed to identify distinguishing features between clusters.ResultsThe SVM identified loss of appetite, flank discomfort, abdominal bloating or gurgling, and pale or yellowish complexion as the most discriminative features. Unsupervised clustering revealed four distinct patient subgroups with differing predominant symptom profiles, such as systemic symptoms, upper abdominal symptoms, changed bowel movement, and nausea/vomiting.ConclusionThrough supervised learning, we identified the most important features for PI. Additionally, we proposed a novel unsupervised learning approach for identifying patterns from the patient data. This study could facilitate clinical decision making as it pertains to patients with FD.

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.