The synthetic data generation market is experiencing explosive growth, driven by the increasing need for high-quality data in various applications, including AI/ML model training, data privacy compliance, and software testing. The market, currently estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $10 billion by 2033. This significant expansion is fueled by several key factors. Firstly, the rising adoption of artificial intelligence and machine learning across industries demands large, high-quality datasets, often unavailable due to privacy concerns or data scarcity. Synthetic data provides a solution by generating realistic, privacy-preserving datasets that mirror real-world data without compromising sensitive information. Secondly, stringent data privacy regulations like GDPR and CCPA are compelling organizations to explore alternative data solutions, making synthetic data a crucial tool for compliance. Finally, the advancements in generative AI models and algorithms are improving the quality and realism of synthetic data, expanding its applicability in various domains. Major players like Microsoft, Google, and AWS are actively investing in this space, driving further market expansion. The market segmentation reveals a diverse landscape with numerous specialized solutions. While large technology firms dominate the broader market, smaller, more agile companies are making significant inroads with specialized offerings focused on specific industry needs or data types. The geographical distribution is expected to be skewed towards North America and Europe initially, given the high concentration of technology companies and early adoption of advanced data technologies. However, growing awareness and increasing data needs in other regions are expected to drive substantial market growth in Asia-Pacific and other emerging markets in the coming years. The competitive landscape is characterized by a mix of established players and innovative startups, leading to continuous innovation and expansion of market applications. This dynamic environment indicates sustained growth in the foreseeable future, driven by an increasing recognition of synthetic data's potential to address critical data challenges across industries.

The Artificial Intelligence (AI) Basic Software market is experiencing robust growth, projected to reach $14,480 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.8% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of cloud-based AI solutions across diverse sectors, including personal use and enterprise applications, is a major catalyst. Furthermore, continuous advancements in machine learning algorithms and deep learning techniques are fueling innovation and expanding the capabilities of AI basic software. The rise of big data and the need for efficient data processing and analysis are also creating significant demand. While data security concerns and the high initial investment costs associated with implementing AI solutions represent potential restraints, the long-term benefits of increased efficiency, automation, and improved decision-making far outweigh these challenges. The market is segmented by application (personal and enterprise) and type (cloud-based and on-premises), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. Leading players like OpenAI, Google, and Microsoft are actively driving innovation and market competition, fostering a dynamic and rapidly evolving landscape. The geographical distribution of the market reveals significant opportunities across various regions. North America currently holds a dominant market share due to early adoption and strong technological infrastructure. However, Asia-Pacific, particularly China and India, is expected to witness substantial growth fueled by increasing digitalization and government initiatives promoting AI adoption. Europe is also a key market, with substantial investment in AI research and development. While exact regional market share figures aren't provided, a reasonable estimation, given the market dynamics, would show North America maintaining a leading position, followed by Asia-Pacific and Europe, with other regions contributing progressively. The continued expansion of the AI basic software market is expected, driven by the ongoing technological advancements and increasing demand across various sectors, signifying significant investment potential for businesses involved in this domain.

Geoparsing with Large Language Models

The .zip file included in this repository contains all the code and data required to reproduce the results from our paper. Note, however, that in order to run the OpenAI models, users will required an OpenAI API key and sufficient API credits.

Data

The data used for the paper are in the datasetst and results folders.

**Datasets: **This contains the XML files (LGL and Geovirus) and Json files (News2024) used to benchmark the models. It also contains all the data used to fine-tune the gpt-3.5 model, the prompt templates sent to the LLMs, and other data used for mapping and data creation.

**Results: **This contains the results for the models on the three datastes. The folder is separated by dataset, with a single .csv file giving the results for each model on each dataset separately. The .csv file is structured so that each row contains either a predicted toponym and an associated true toponym (along with assigned spatial coordinates), if the model correctly identified a toponym; otherwise the true toponym columns are empty for false positives and the predicted columns are empty for false negatives.

Code

The code is split into two seperate folders gpt_geoparser and notebooks.

**GPT_Geoparser: **this contains the classes and methods used process the XML and JSON articles (data.py), interact with the Nominatim API for geocoding (gazetteer.py), interact with the OpenAI API (gpt_handler.py), process the outputs from the GPT models (geoparser.py) and analyse the results (analysis.py).

Notebooks: This series of notebooks can be used to reproduce the results given in the paper. The file names a reasonably descriptive of what they do within the context of the paper.

Code/software

Requirements

Numpy

Pandas

Geopy

Scitkit-learn

lxml

openai

matplotlib

Contextily

Shapely

Geopandas

tqdm

huggingface_hub

Gnews

Access information

Other publicly accessible locations of the data:

The LGL and GeoVirus datasets can also be obtained here (opens in new window).

Abstract

Geoparsing- the process of associating textual data with geographic locations - is a key challenge in natural language processing. The often ambiguous and complex nature of geospatial language make geoparsing a difficult task, requiring sophisticated language modelling techniques. Recent developments in Large Language Models (LLMs) have demonstrated their impressive capability in natural language modelling, suggesting suitability to a wide range of complex linguistic tasks. In this paper, we evaluate the performance of four LLMs - GPT-3.5, GPT-4o, Llama-3.1-8b and Gemma-2-9b - in geographic information extraction by testing them on three geoparsing benchmark datasets: GeoVirus, LGL, and a novel dataset, News2024, composed of geotagged news articles published outside the models' training window. We demonstrate that, through techniques such as fine-tuning and retrieval-augmented generation, LLMs significantly outperform existing geoparsing models. The best performing models achieve a toponym extraction F1 score of 0.985 and toponym resolution accuracy within 161 km of 0.921. Additionally, we show that the spatial information encoded within the embedding space of these models may explain their strong performance in geographic information extraction. Finally, we discuss the spatial biases inherent in the models' predictions and emphasize the need for caution when applying these techniques in certain contexts.

Methods

This contains the data and codes required to reproduce the results from our paper. The LGL and GeoVirus datasets are pre-existing datasets, with references given in the manuscript. The News2024 dataset was constructed specifically for the paper.

To construct the News2024 dataset, we first created a list of 50 cities from around the world which have population greater than 1000000. We then used the GNews python package https://pypi.org/project/gnews/ (opens in new window) to find a news article for each location, published between 2024-05-01 and 2024-06-30 (inclusive). Of these articles, 47 were found to contain toponyms, with the three rejected articles referring to businesses which share a name with a city, and which did not otherwise mention any place names.

We used a semi autonmous approach to geotagging the articles. The articles were first processed using a Distil-BERT model, fine tuned for named entity recognicion. This provided a first estimate of the toponyms within the text. A human reviewer then read the articles, and accepted or rejected the machine tags, and added any tags missing from the machine tagging process. We then used OpenStreetMap to obtain geographic coordinates for the location, and to identify the toponym type (e.g. city, town, village, river etc). We also flagged if the toponym was acting as a geo-political entity, as these were reomved from the analysis process. In total, 534 toponyms were identified in the 47 news articles.

Dataset Card for GSM8K

  Dataset Summary

GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

The multi-modal generation market, encompassing technologies that integrate and process information from various data sources like text, images, audio, and video, is experiencing explosive growth. With a 2025 market size estimated at $1.893 billion (based on the provided 2025 value unit of "million") and a Compound Annual Growth Rate (CAGR) of 25.4%, the market is projected to reach significant scale by 2033. This rapid expansion is fueled by several key drivers, including the increasing availability of large datasets, advancements in artificial intelligence (AI) algorithms, particularly deep learning models adept at handling multi-modal data, and a growing demand for more sophisticated and human-like AI interactions across various industries. The market's expansion is further bolstered by the rising adoption of multi-modal AI in diverse applications, such as customer service chatbots, advanced medical diagnostics, immersive virtual and augmented reality experiences, and next-generation content creation tools. Major technology companies like Google, Microsoft, and OpenAI are heavily invested in this space, driving innovation and competition. Despite its significant potential, the market faces certain restraints. High computational costs associated with training complex multi-modal models and the need for substantial amounts of high-quality annotated data present challenges for smaller players. Furthermore, ethical considerations surrounding bias in AI algorithms and data privacy remain crucial hurdles that need to be addressed for sustainable growth. The segmentation of the market is likely driven by application type (e.g., customer service, healthcare, entertainment), technology used (e.g., deep learning, natural language processing, computer vision), and deployment method (e.g., cloud-based, on-premise). Ongoing technological innovation, combined with proactive efforts to mitigate challenges and address ethical concerns, will define the trajectory of this rapidly evolving market over the coming decade.

The Intelligent Semantic Data Service market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from rapidly expanding data volumes. The market's complexity necessitates sophisticated solutions that go beyond traditional data analytics, focusing on understanding the meaning and context of data. This demand is fueled by advancements in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), which power semantic analysis engines. Key players like Google, IBM, Microsoft, Amazon, and others are heavily investing in this space, developing and deploying powerful solutions that cater to various industries, from finance and healthcare to retail and manufacturing. The market's projected Compound Annual Growth Rate (CAGR) suggests a significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be approximately $15 billion, based on industry reports and observed growth trajectories in related AI segments. This figure is expected to reach approximately $35 billion by 2033. Several factors contribute to this growth, including the rising adoption of cloud-based solutions, the need for improved data governance, and a growing emphasis on data-driven decision-making. However, the market also faces certain restraints. High implementation costs, the need for specialized expertise, and data security concerns can hinder widespread adoption. Furthermore, the market is characterized by a relatively high barrier to entry, favoring established players with significant R&D capabilities. Nevertheless, the potential benefits of unlocking the true value of unstructured data through intelligent semantic analysis are compelling enough to drive continued investment and innovation in this rapidly evolving market. Segmentation within the market is likely based on deployment type (cloud, on-premise), service type (data enrichment, knowledge graph creation, semantic search), and industry vertical. The geographic distribution shows a strong concentration in North America and Europe, followed by a steady growth in the Asia-Pacific region, driven by increasing digitalization efforts.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

What’s Included

The Polish Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Polish language, advancing the field of artificial intelligence.

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in Polish. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Polish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

This fully labeled Polish Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in Polish are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Polish Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

A prebuilt dataset for OpenAI's task for image-2-latex system. Includes total of ~100k formulas and images splitted into train, validation and test sets. Formulas were parsed from LaTeX sources provided here: http://www.cs.cornell.edu/projects/kddcup/datasets.html(originally from arXiv)

Each image is a PNG image of fixed size. Formula is in black and rest of the image is transparent.

For related tools (eg. tokenizer) check out this repository: https://github.com/Miffyli/im2latex-dataset
For pre-made evaluation scripts and built im2latex system check this repository: https://github.com/harvardnlp/im2markup

Newlines used in formulas_im2latex.lst are UNIX-style newlines ( ). Reading file with other type of newlines results to slightly wrong amount of lines (104563 instead of 103558), and thus breaks the structure used by this dataset. Python 3.x reads files using newlines of the running system by default, and to avoid this file must be opened with newlines=" " (eg. open("formulas_im2latex.lst", newline=" ")).

Database Engines Market Outlook

The global database engines market size was valued at USD 40 billion in 2023 and is projected to reach USD 68 billion by 2032, growing at a CAGR of 6.2% during the forecast period. This growth is driven by the increasing demand for efficient data management solutions, the rapid proliferation of data, and advancements in cloud computing technologies.

The growth of the database engines market is primarily fueled by the exponential increase in data generated across various industry verticals. Organizations are seeking robust and scalable solutions to manage, store, and analyze massive volumes of data. The surge in digital transformation initiatives and the growing adoption of big data analytics are accelerating the demand for advanced database engines. Additionally, the rise in cloud-based services has paved the way for more flexible and cost-effective solutions, further bolstering market growth.

Another significant growth factor is the increasing adoption of Internet of Things (IoT) technologies. The IoT ecosystem generates vast amounts of real-time data that require sophisticated database engines for processing and analysis. This surge in data from IoT devices necessitates the deployment of efficient database management systems. Furthermore, the growing emphasis on artificial intelligence (AI) and machine learning (ML) technologies, which rely heavily on data, is also propelling the adoption of database engines.

The regulatory landscape is also playing a crucial role in shaping the database engines market. Compliance with data protection and privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), is driving the need for secure and reliable database solutions. Organizations are prioritizing data security and integrity, thereby increasing investments in advanced database engines that offer robust security features and regulatory compliance capabilities.

The rise of Open-Source Database Software is becoming a transformative force in the database engines market. These solutions offer organizations the flexibility to customize and optimize their database environments without the constraints of proprietary software. Open-source databases, such as PostgreSQL and MySQL, have gained popularity due to their cost-effectiveness and community-driven development models. They provide robust performance and scalability, making them suitable for a wide range of applications, from small startups to large enterprises. As more organizations seek to reduce costs and increase control over their data infrastructure, the adoption of open-source database software is expected to grow, further driving innovation and competition in the market.

Regionally, North America holds a dominant position in the database engines market, owing to the presence of major technology players and the early adoption of advanced technologies. The Asia Pacific region is expected to witness significant growth during the forecast period, driven by the rapid digitalization initiatives, increasing investments in IT infrastructure, and the growing number of small and medium enterprises (SMEs) adopting database solutions. Europe is also a notable market, with a strong emphasis on data protection and privacy regulations driving the demand for secure database engines.

Type Analysis

The database engines market is categorized into several types, including Relational, NoSQL, NewSQL, In-Memory, and Others. Relational database engines, based on the traditional table structure, remain the most widely used type due to their reliability, robustness, and ability to handle complex queries. These engines are integral to various enterprise applications, offering a structured way to manage data through predefined schemas. The maturity and extensive support of relational databases make them a preferred choice for many organizations, especially in industries like finance and healthcare where data integrity is paramount.

NoSQL database engines have gained significant traction in recent years, primarily due to their flexibility and scalability. Unlike traditional relational databases, NoSQL databases do not rely on a fixed schema, making them suitable for handling unstructured and semi-structured data. This characteristic is particularly advantageous for applications involving big data and real-time web applications. The rise of e-commerce, social media, and IoT app

What’s Included

The Kannada Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Kannada language, advancing the field of artificial intelligence.

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in Kannada. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Kannada people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

This fully labeled Kannada Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in Kannada are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Kannada Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

The Artificial Intelligence (AI) Basic Software market is experiencing robust growth, driven by the increasing adoption of AI across various industries and the continuous advancement of AI technologies. While precise market size figures for 2019-2024 are unavailable, a reasonable estimation, considering the involvement of major tech players like Google, OpenAI, and Microsoft, and a projected Compound Annual Growth Rate (CAGR), suggests a market size exceeding $50 billion in 2025. This substantial value reflects the market's maturity and its crucial role in powering AI applications. Key drivers include the growing need for efficient data processing and algorithm development, the rising demand for automation across sectors like healthcare, finance, and manufacturing, and the increasing availability of cloud-based AI infrastructure. Trends shaping this market include the expansion of open-source AI tools, the development of more sophisticated machine learning algorithms, and a focus on explainable AI (XAI) to improve transparency and trust. Despite these positive trends, market restraints include the high cost of implementation, the scarcity of skilled AI professionals, and ongoing ethical concerns surrounding AI bias and data privacy. The market is segmented based on software type (machine learning, deep learning, natural language processing, computer vision, etc.), deployment mode (cloud, on-premise), and industry vertical (healthcare, finance, retail, etc.). Leading companies are actively investing in research and development to maintain a competitive edge and capitalize on market opportunities. The forecast period (2025-2033) anticipates continued expansion, with the CAGR likely exceeding 20%. This projection is supported by the ongoing technological advancements, increasing digital transformation initiatives across industries, and the emergence of new AI applications in areas like robotics and autonomous vehicles. Despite potential challenges related to regulation and economic factors, the long-term growth outlook remains positive, with the market expected to reach a valuation significantly exceeding $200 billion by 2033. Strategic partnerships, acquisitions, and innovative product development will be critical for companies seeking market leadership in this dynamic and rapidly evolving landscape.

The AI Age Detector Software Market is anticipated to exhibit significant growth over the forecast period due to rising demand for age verification and estimation solutions across various industries. The increasing use of online platforms for sensitive activities such as e-commerce transactions and social media interactions has created a need for accurate and reliable methods to verify and estimate user ages. Key market drivers include the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies, as well as the increasing awareness of data privacy and regulatory compliance. The market is segmented into application, end-use, deployment type, technology, company, and region. In terms of application, age verification holds the largest market share due to its critical role in preventing minors from accessing age-restricted content and services. Cloud-based and hybrid deployment types are gaining popularity due to their scalability, cost-effectiveness, and ease of access. ML and deep learning are the dominant technologies used in age detector software, offering high accuracy and performance. The market is highly competitive with major players including DataRobot, Oracle, SAP, Palantir, Microsoft, Amazon, C3.ai, Facebook, IBM, Clarifai, OpenAI, Salesforce, Adobe, NVIDIA, and Google. North America is the largest regional market, followed by Europe and Asia Pacific. Ongoing advancements in AI and ML, coupled with increasing government regulations on data privacy, are expected to drive further market growth in the coming years. Key drivers for this market are: Enhanced personalization in digital marketing, Growing demand in e-commerce platforms; Integration with social media applications; Expansion in the healthcare industry; Increased use in security systems . Potential restraints include: Growing demand for verification tools, Increasing e-commerce and online services; Rise in social media usage; Advancements in AI technologies; Regulatory compliance and privacy concerns .

Operational Database Management Market Outlook

The global operational database management market size was valued at approximately USD 39.1 billion in 2023 and is projected to reach around USD 82.6 billion by 2032, growing at a CAGR of 8.7% during the forecast period. This market is driven by the increasing need for real-time data analytics, enhanced data security, and the rising adoption of cloud-based solutions. As businesses continue to digitize their operations, the demand for robust database management systems that can handle large volumes of data in real time has surged, positioning this market for significant growth.

One of the primary growth factors for this market is the proliferation of data across various industries. With the advent of IoT, social media, and other digital platforms, organizations are generating an unprecedented amount of data that needs to be managed efficiently. This has led to the adoption of advanced database management systems that can handle diverse data types and provide real-time insights. Additionally, advancements in AI and machine learning have further fueled the demand for operational databases that can support predictive analytics and automated decision-making processes.

Another major driver is the increasing necessity for enhanced data security and compliance. As data breaches and cyber threats become more sophisticated, organizations are under immense pressure to ensure the security and integrity of their data. Modern operational database management systems offer advanced security features such as encryption, access controls, and regular audits, which help organizations comply with stringent regulatory requirements and protect their sensitive information from unauthorized access and attacks.

The growing adoption of cloud-based solutions is also a significant contributor to market growth. Cloud-based operational databases offer numerous advantages, including reduced infrastructure costs, scalability, and accessibility from anywhere with an internet connection. This has made them particularly appealing to small and medium enterprises (SMEs) that may lack the resources to invest in on-premises solutions. Moreover, the integration of cloud services with AI and machine learning capabilities allows organizations to leverage their data for more strategic decision-making, further driving the demand for cloud-based database management systems.

The rise of Open Source Database solutions has been a game-changer in the operational database management market. These databases offer a cost-effective alternative to traditional proprietary systems, making them particularly attractive to small and medium enterprises (SMEs) and startups. Open source databases are not only budget-friendly but also provide the flexibility to customize and adapt the software to meet specific business needs. The robust community support and continuous innovation associated with open-source projects ensure that these databases remain at the forefront of technological advancements. As a result, many organizations are increasingly adopting open-source databases to leverage their scalability, reliability, and comprehensive feature sets, which are comparable to those of their proprietary counterparts.

From a regional perspective, North America remains a dominant player in the operational database management market, thanks to its advanced IT infrastructure and the presence of major technology companies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in IT infrastructure, and the rising adoption of cloud services in countries like China and India. Europe and Latin America are also anticipated to experience steady growth due to the increasing focus on data security and compliance with regulations such as GDPR.

Component Analysis

The operational database management market can be segmented into software and services. The software segment is anticipated to hold the larger market share during the forecast period. This is primarily due to the continuous advancements in database technologies that offer enhanced performance, scalability, and security. Companies are increasingly investing in sophisticated database management software that can support their growing data requirements and provide real-time analytics. Moreover, the integration of AI and machine learning capabilities into database software is enabling predictive analytic

Analysis of ‘Open Data 500 Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/govlab/open-data-500-companies on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The Open Data 500, funded by the John S. and James L. Knight Foundation (http://www.knightfoundation.org/) and conducted by the GovLab, is the first comprehensive study of U.S. companies that use open government data to generate new business and develop new products and services.

Study Goals

• Provide a basis for assessing the economic value of government open data

• Encourage the development of new open data companies

• Foster a dialogue between government and business on how government data can be made more useful

The Govlab's Approach

The Open Data 500 study is conducted by the GovLab at New York University with funding from the John S. and James L. Knight Foundation. The GovLab works to improve people’s lives by changing how we govern, using technology-enabled solutions and a collaborative, networked approach. As part of its mission, the GovLab studies how institutions can publish the data they collect as open data so that businesses, organizations, and citizens can analyze and use this information.

Company Identification

The Open Data 500 team has compiled our list of companies through (1) outreach campaigns, (2) advice from experts and professional organizations, and (3) additional research.

Outreach Campaign

• Mass email to over 3,000 contacts in the GovLab network

• Mass email to over 2,000 contacts OpenDataNow.com

• Blog posts on TheGovLab.org and OpenDataNow.com

• Social media recommendations

• Media coverage of the Open Data 500

• Attending presentations and conferences

Expert Advice

• Recommendations from government and non-governmental organizations

• Guidance and feedback from Open Data 500 advisors

Research

• Companies identified for the book, Open Data Now

• Companies using datasets from Data.gov

• Directory of open data companies developed by Deloitte

• Online Open Data Userbase created by Socrata

• General research from publicly available sources

What The Study Is Not

The Open Data 500 is not a rating or ranking of companies. It covers companies of different sizes and categories, using various kinds of data.

The Open Data 500 is not a competition, but an attempt to give a broad, inclusive view of the field.

The Open Data 500 study also does not provide a random sample for definitive statistical analysis. Since this is the first thorough scan of companies in the field, it is not yet possible to determine the exact landscape of open data companies.

--- Original source retains full ownership of the source dataset ---

Data Code Repository This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects. ## Features - Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes - Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content. - Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions. ## Installation To use the data code, you can down or clone the required code. Notably, before using the code, make sure the necessary environment configuration is done. ## Dependencies The data code has the following dependencies: Python (version 3.6 or higher) NumPy Pandas Seaborn Scikit-learn Openai Android Studio (version 4.0) Install the required dependencies using pip: pip install numpy.. ##License This data code is distributed under the MIT License. See LICENSE for more information. ##Copyright All copyright of the tool is owned by the author of the paper.

The open-source big data tools market is experiencing robust growth, driven by the increasing need for scalable, cost-effective data management and analysis solutions across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume and velocity of data generated across industries, from banking and finance to manufacturing and government, necessitate powerful and adaptable tools. Secondly, the cost-effectiveness and flexibility of open-source solutions compared to proprietary alternatives are major drawcards, especially for smaller organizations and startups. The ease of customization and community support further enhance their appeal. Growth is also being propelled by technological advancements such as the development of more sophisticated data analytics tools, improved cloud integration, and increased adoption of containerization technologies like Docker and Kubernetes for deployment and management. The market's segmentation across application (banking, manufacturing, etc.) and tool type (data collection, storage, analysis) reflects the diverse range of uses and specialized tools available. Key restraints to market growth include the complexity associated with implementing and managing open-source solutions, requiring skilled personnel and ongoing maintenance. Security concerns and the need for robust data governance frameworks also pose challenges. However, the growing maturity of the open-source ecosystem, coupled with the emergence of managed services providers offering support and expertise, is mitigating these limitations. The continued advancements in artificial intelligence (AI) and machine learning (ML) are further integrating with open-source big data tools, creating synergistic opportunities for growth in predictive analytics and advanced data processing. This integration, alongside the ever-increasing volume of data needing analysis, will undoubtedly drive continued market expansion over the forecast period.

The Multimodal AI market is experiencing explosive growth, driven by the increasing convergence of various data modalities—text, images, audio, and video—to create more comprehensive and nuanced AI systems. The market's expansion is fueled by several key factors. Firstly, the proliferation of data from diverse sources provides the rich fuel for training these sophisticated algorithms. Secondly, advancements in deep learning techniques allow for more effective processing and integration of these heterogeneous data types, leading to more accurate and insightful predictions. Thirdly, the growing adoption of cloud computing offers scalable infrastructure crucial for training and deploying resource-intensive multimodal AI models. This is particularly evident in sectors like BFSI (banking, financial services, and insurance), where fraud detection and risk assessment benefit greatly from analyzing multiple data points simultaneously; and Retail and eCommerce, where personalized experiences and efficient supply chain management are enhanced by multimodal analysis of customer data and product information. Finally, the emergence of specialized AI companies, alongside tech giants like AWS, Google, and Microsoft, is driving innovation and fostering competition, further accelerating market growth. The market is segmented by application (BFSI, Retail & eCommerce, Telecommunications, Healthcare, Manufacturing, Automotive, Others) and type (Cloud, On-Premises). While the Cloud segment currently dominates due to its scalability and accessibility, the On-Premise segment is expected to see growth driven by specific industry needs for data security and control. Geographically, North America and Europe currently hold significant market share, but the Asia-Pacific region is poised for rapid expansion, fueled by increasing digitalization and technological advancements in countries like China and India. Despite the significant growth potential, challenges remain, including the complexity of integrating diverse data sources, the need for robust data annotation, and concerns around data privacy and ethical implications. Overcoming these challenges will be crucial for continued market expansion in the coming years. We project a substantial increase in market value over the forecast period (2025-2033), with the CAGR significantly exceeding the average growth rates of related AI sub-markets.