The global SQL Query Builders market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period, 2025-2033, reaching USD XXX million in 2033. The market growth is attributed to the increasing adoption of cloud-based data platforms, the growing need for data analysis and visualization, and the rising demand for self-service BI tools. The cloud-based segment is expected to dominate the market due to its flexibility, scalability, and cost-effectiveness. The North America region accounted for the largest market share in 2025 and is expected to maintain its dominance during the forecast period. The high adoption of advanced technologies, presence of major vendors, and growing awareness about data-driven decision-making are the key factors driving the market growth in this region. The Asia Pacific region is expected to experience the fastest growth rate during the forecast period due to the increasing adoption of digital technologies and the growing number of small and medium-sized businesses in the region. Major vendors in the market include Chartio, Datapine, Syncfusion, Devart, Idera, Navicat, Toad, SQLyog, DbVisualizer, Skyvia, Aqua Data Studio, Valentina, IBExpert, EasyQueryBuilder, Active Database Software, DBHawk, Data Xtractor, and others.

OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

A database where EPA has compiled data on public drinking water systems and whether they have certain drinking water violations. This data is collected by the states and given to the EPA. This dataset is associated with the following publication: Pennino, M., J. Compton, and S. Leibowitz. Trends in Drinking Water Nitrate Violations Across the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 13450-13460, (2017).

Improve the use of land cover data by developing an advanced framework for robust classification using multi-source datasets:
Develop, validate and optimize a generalized multi-kernel, active learning (MKL-AL) pattern recognition framework for multi-source data fusion.
Develop both single- and ensemble-classifier versions (MKL-AL and Ensemble-MKL-AL) of the system.
Utilize multi-source remotely sensed and in situ data to create land-cover classification and perform accuracy assessment with available labeled data; utilize first results to query new samples that, if inducted into the training of the system, will significantly improve classification performance and accuracy.
 

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the Archer Access Request Form so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the Archer User Feedback Form. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this form to subscribe to it. Citation Guidelines: 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [codebook], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. doi:10.13012/B2IDB-5649852_V3 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [database], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V3

Nowadays web portals play an essential role in searching and retrieving information in the several fields of knowledge: they are ever more technologically advanced and designed for supporting the storage of a huge amount of information in natural language originating from the queries launched by users worldwide.
A good example is given by the WorldWideScience search engine:
The database is available at . It is based on a similar gateway, Science.gov, which is the major path to U.S. government science information, as it pulls together Web-based resources from various agencies. The information in the database is intended to be of high quality and authority, as well as the most current available from the participating countries in the Alliance, so users will find that the results will be more refined than those from a general search of Google. It covers the fields of medicine, agriculture, the environment, and energy, as well as basic sciences. Most of the information may be obtained free of charge (the database itself may be used free of charge) and is considered ‘‘open domain.’’ As of this writing, there are about 60 countries participating in WorldWideScience.org, providing access to 50+databases and information portals. Not all content is in English. (Bronson, 2009)
Given this scenario, we focused on building a corpus constituted by the query logs registered by the GreyGuide: Repository and Portal to Good Practices and Resources in Grey Literature and received by the WorldWideScience.org (The Global Science Gateway) portal: the aim is to retrieve information related to social media which as of today represent a considerable source of data more and more widely used for research ends.
This project includes eight months of query logs registered between July 2017 and February 2018 for a total of 445,827 queries. The analysis mainly concentrates on the semantics of the queries received from the portal clients: it is a process of information retrieval from a rich digital catalogue whose language is dynamic, is evolving and follows – as well as reflects – the cultural changes of our modern society.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

Enterprise Semantic Search Software Market Outlook

The global market size for enterprise semantic search software was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 7.9 billion by 2032, reflecting a CAGR of 13.5% over the forecast period. This robust growth is driven primarily by the increasing need for advanced data search capabilities within organizations, which enable more efficient and effective retrieval of relevant information.

The explosive growth of data generated by businesses is a primary factor propelling the enterprise semantic search software market. As organizations accumulate massive volumes of structured and unstructured data, the ability to efficiently search, categorize, and retrieve this data becomes critical. Semantic search software, which leverages natural language processing (NLP) and machine learning (ML) to understand the context and intent behind search queries, offers a solution to this challenge, driving its adoption across various industries.

Another significant growth factor is the increasing adoption of artificial intelligence (AI) and machine learning technologies. These advanced technologies enhance the capabilities of semantic search engines, allowing them to deliver more accurate and relevant search results. As enterprises continue to recognize the value of AI-driven insights for strategic decision-making, investment in semantic search software is expected to rise, contributing to market growth.

Moreover, the rising focus on improving customer experience is also fueling the demand for enterprise semantic search software. Businesses are increasingly seeking ways to provide personalized and seamless experiences to their customers, and semantic search technology plays a crucial role in achieving this goal. By enabling more intuitive and context-aware search functionalities, organizations can better meet customer needs and preferences, thereby enhancing satisfaction and loyalty.

Entity Resolution Software is becoming increasingly important in the realm of enterprise semantic search solutions. As organizations deal with vast amounts of data, the need to accurately identify and link related data entities across different datasets becomes crucial. This software helps in resolving ambiguities and ensuring that data is correctly matched and merged, which is essential for maintaining data integrity and reliability. By integrating entity resolution capabilities, semantic search software can provide more precise and comprehensive search results, enhancing the overall data management process. This is particularly beneficial for industries such as finance and healthcare, where accurate data linkage is critical for compliance and operational efficiency. As the demand for more sophisticated data management solutions grows, the role of entity resolution software in semantic search platforms is expected to expand, driving further innovation and adoption in the market.

In terms of regional outlook, North America currently holds the largest market share due to the high adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by increasing digital transformation initiatives, growing internet penetration, and rising investments in AI and ML technologies.

Component Analysis

The enterprise semantic search software market can be segmented by component into software and services. The software segment encompasses the actual search platforms and tools that utilize semantic technology to deliver enhanced search capabilities. This segment is anticipated to hold the largest market share due to the increasing demand for advanced search solutions that can handle large volumes of data. As organizations continue to digitize their operations and generate vast amounts of data, the need for robust semantic search software is expected to grow.

Within the software segment, there is a notable trend towards the integration of AI and machine learning functionalities. These technologies significantly enhance the performance of semantic search tools by enabling them to understand the context and intent behind user queries. This results in more accurate and relevant search results, thereby improving user satisfaction and productivity. The continuous advancements in AI and ML are expected to further drive the adoption of semantic

Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.

Non-Native Database Management Systems Market Outlook

The global market size for Non-Native Database Management Systems (DBMS) was valued at approximately USD 15 billion in 2023 and is projected to grow to USD 45 billion by 2032, driven by a robust CAGR of 12%. The rapid digital transformation across various industries, coupled with the increasing need for advanced data management solutions, are significant growth factors for this market. Enterprises are increasingly opting for non-native DBMS to manage their data more effectively, ensuring high performance, scalability, and flexibility.

The growth of the Non-Native DBMS market can be attributed to several factors. Firstly, the exponential increase in data generation from various sources such as social media, IoT devices, and enterprise applications has made traditional databases cumbersome and inefficient. Non-native DBMS provide unique advantages in handling large-scale data, offering better performance, scalability, and flexibility. Secondly, the rise of cloud computing is another significant driver. Cloud-based deployment models are rapidly being adopted due to their cost-effectiveness, scalability, and ease of integration with existing systems. Thirdly, advancements in machine learning and artificial intelligence are pushing the demand for advanced data management solutions that can support complex analytical queries, which non-native DBMS are well equipped to handle.

Another growth factor is the increasing adoption of non-native DBMS in various industry verticals such as healthcare, BFSI, retail, and IT & telecommunications. These sectors are heavily reliant on data for decision-making and operational efficiency. For instance, in the healthcare sector, the need for efficient data management is paramount for patient records, medical research, and compliance with regulatory requirements. Similarly, the BFSI sector requires robust data management solutions for risk management, fraud detection, and customer analytics. The retail industry benefits from non-native DBMS in managing customer data, inventory, and supply chain operations. As these industries continue to evolve, the demand for advanced database management solutions is expected to soar.

Regionally, North America is anticipated to hold the largest market share due to the early adoption of advanced technologies and the presence of major IT and cloud service providers. Europe is also expected to witness significant growth driven by stringent data protection regulations such as GDPR, which necessitate advanced data management solutions. The Asia Pacific region is projected to be the fastest-growing market due to rapid digitalization, increasing internet penetration, and the burgeoning IT sector. Latin America and the Middle East & Africa are also expected to show steady growth as businesses in these regions continue to modernize their IT infrastructure.

Type Analysis

The Non-Native Database Management Systems market can be segmented by type into Relational, NoSQL, NewSQL, and others. The relational database segment has traditionally dominated the market due to its robust data integrity, reliability, and ease of use. Relational databases use structured query language (SQL) for data manipulation and offer strong transactional consistency, making them ideal for applications requiring complex queries and relationships between data. Despite the rise of newer database technologies, relational databases continue to be widely used in various industries, including finance and healthcare, where data consistency and integrity are paramount.

The emergence of NEWSQL Database solutions represents a significant evolution in the database management landscape. These databases aim to bridge the gap between the traditional relational databases and the more modern NoSQL databases by offering the scalability and flexibility of NoSQL while maintaining the ACID properties of relational databases. This hybrid approach makes NEWSQL databases particularly appealing for applications that require high throughput and low latency without sacrificing data consistency. As businesses increasingly demand real-time analytics and transactional processing, NEWSQL databases are becoming an attractive option for industries such as finance, gaming, and e-commerce, where performance and reliability are critical.

NoSQL databases have gained significant traction in recent years due to their ability to handle large volumes of unstructured data. Unlike relational databases,

Adaptive Query Acceleration Market Outlook

According to our latest research, the global Adaptive Query Acceleration market size stood at USD 2.21 billion in 2024. The market is projected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching an estimated value of USD 8.79 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing demand for real-time data processing and analytics across a variety of industries, as organizations seek to derive actionable insights from vast and complex datasets. The proliferation of big data, advancements in artificial intelligence, and the widespread adoption of cloud-based solutions are further catalyzing the expansion of the Adaptive Query Acceleration market worldwide.

One of the most significant growth drivers for the Adaptive Query Acceleration market is the exponential rise in data generation across enterprises. Businesses today are inundated with massive volumes of structured and unstructured data from multiple sources, including IoT devices, social media, and transactional systems. Traditional query processing methods often struggle to deliver real-time insights due to latency and scalability challenges. Adaptive Query Acceleration technologies address these limitations by dynamically optimizing and expediting query execution, thereby enabling organizations to extract maximum value from their data assets. This capability is especially critical in sectors such as BFSI, healthcare, and retail, where timely decision-making can lead to substantial competitive advantages.

Another key factor fueling market growth is the increasing adoption of cloud computing and hybrid IT infrastructure. As enterprises migrate their data workloads to cloud environments, the need for scalable, flexible, and high-performance query acceleration solutions becomes paramount. Adaptive Query Acceleration platforms are designed to seamlessly integrate with both on-premises and cloud-based data repositories, offering organizations the agility to process queries efficiently regardless of the underlying infrastructure. This flexibility not only enhances operational efficiency but also reduces the total cost of ownership, making it an attractive proposition for businesses of all sizes, including small and medium enterprises (SMEs).

Furthermore, the rapid evolution of business intelligence (BI) and analytics applications is contributing to the growing demand for Adaptive Query Acceleration solutions. Modern BI tools require the ability to process complex queries and deliver real-time analytics to end-users, often across globally distributed datasets. Adaptive Query Acceleration technologies leverage advanced algorithms, hardware acceleration, and intelligent caching mechanisms to optimize query performance, enabling organizations to unlock deeper insights and drive data-driven innovation. This trend is expected to intensify as digital transformation initiatives gain momentum, particularly in industries such as manufacturing, IT and telecommunications, and government.

From a regional perspective, North America currently dominates the Adaptive Query Acceleration market, accounting for the largest share in 2024. This leadership position can be attributed to the early adoption of advanced analytics solutions, a strong presence of leading technology vendors, and significant investments in digital infrastructure. However, the Asia Pacific region is poised for the highest growth over the forecast period, driven by rapid digitalization, expanding cloud adoption, and increasing focus on data-driven business strategies in emerging economies such as China and India. Europe and Latin America are also witnessing steady growth, supported by regulatory mandates for data compliance and the rising need for efficient data processing solutions.

Component Analysis

The Adaptive Query Acceleration market is segmented by component into hardware, software, and services, each playing a crucial role in the overall ecosystem. The hardware segment comprises specialize

The King County Groundwater Protection Program maintains a database of groundwater wells, water quality and water level sampling data. Users may search the database using Quick or Advanced Search OR use King County Groundwater iMap map set. Search for King County well data by Well ID, Local Number, DoE Well Tag, or Parcel Number.

The Analytics Query Accelerator (AQA) market is experiencing robust growth, driven by the increasing demand for faster and more efficient data analysis across various industries. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. This growth is fueled by several key factors. Firstly, the exponential growth of data volume necessitates faster query processing times, making AQAs indispensable for businesses aiming to gain real-time insights. Secondly, the rising adoption of cloud-based analytics platforms and big data technologies creates a fertile ground for AQA solutions. Furthermore, the increasing need for advanced analytics capabilities in sectors such as finance, healthcare, and e-commerce is further driving market expansion. Finally, continuous technological advancements, including the development of more powerful processors and optimized algorithms, are improving AQA performance and expanding their application across various use cases. However, the market also faces certain challenges. High initial investment costs and the complexity of implementation can hinder adoption, particularly among smaller businesses. Furthermore, the need for skilled professionals to manage and maintain AQA systems poses another barrier. Despite these restraints, the long-term outlook for the AQA market remains extremely positive. The ongoing trend toward data-driven decision-making and the continuous evolution of data analytics technologies are expected to propel significant growth in the coming years. Market segmentation reveals strong growth in the cloud-based application segment and a rising demand for AI-powered AQAs. Geographically, North America and Europe currently dominate the market, but Asia-Pacific is anticipated to show rapid growth, driven by increased digitalization and technological advancements.

AI-Powered Cognitive Search Market Outlook

The global AI-powered cognitive search market size is projected to grow from $2.35 billion in 2023 to $10.45 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 18.1% over the forecast period. This impressive growth is driven by the increasing demand for advanced data analytics tools and the need for enhanced customer experience across various industries. The integration of artificial intelligence (AI) technology in search algorithms has significantly improved the ability to retrieve relevant information, thus fueling the market expansion.

One of the primary growth factors for the AI-powered cognitive search market is the exponential increase in data generation across industries. With the advent of big data, organizations are accumulating vast amounts of unstructured data, which traditional search methods struggle to manage effectively. AI-powered cognitive search leverages machine learning, natural language processing (NLP), and other AI technologies to analyze and index this data, allowing organizations to derive actionable insights and make data-driven decisions. This capability is particularly valuable in sectors such as healthcare, BFSI, and IT, where the rapid retrieval of relevant information can significantly impact operational efficiency and customer satisfaction.

Furthermore, the growing emphasis on personalized customer experiences is propelling the adoption of AI-powered cognitive search solutions. Modern consumers expect quick and accurate responses to their queries, and businesses are increasingly recognizing the need to enhance their search functionalities to meet these expectations. By implementing AI-powered cognitive search, companies can provide more relevant search results and recommendations, thereby improving customer engagement and loyalty. This trend is especially prominent in the retail and e-commerce sectors, where personalized interactions can drive higher conversion rates and revenues.

Additionally, advancements in AI technologies, such as deep learning and NLP, are continuously enhancing the capabilities of cognitive search solutions. These technologies enable search systems to understand the context and intent behind user queries, leading to more accurate and relevant search results. As a result, organizations are investing heavily in AI research and development to stay competitive in the market. The ongoing innovation in AI-powered cognitive search tools is expected to create new growth opportunities and drive market expansion over the forecast period.

The regional outlook for the AI-powered cognitive search market indicates significant growth across various geographies. North America currently holds the largest market share, primarily due to the presence of leading technology companies and high adoption rates of AI technologies. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by the increasing digital transformation initiatives and the rising demand for advanced analytics solutions in countries like China and India. Europe and Latin America are also anticipated to experience substantial growth, supported by the growing awareness of AI benefits and the increasing investments in AI infrastructure.

Component Analysis

The AI-powered cognitive search market can be segmented by components into software and services. The software segment is expected to hold the largest market share, driven by the increasing adoption of AI-based search solutions across various industries. These software solutions incorporate advanced algorithms and AI technologies such as machine learning and NLP to enhance search accuracy and relevance. The continuous advancements in AI technologies are further boosting the capabilities of cognitive search software, enabling them to provide more sophisticated and intuitive search experiences.

Within the software segment, several sub-segments can be identified, including enterprise search software, cognitive search platforms, and industry-specific search solutions. Enterprise search software is designed to cater to the needs of large organizations, providing comprehensive search capabilities across diverse data sources. Cognitive search platforms, on the other hand, offer more specialized functionalities, often tailored to specific use cases or industries. Industry-specific search solutions are customized to address the unique requirements of sectors such as healthcare, retail, and BFSI, enhancing their ability to retrieve relevant information quickly and accurately.

Market Analysis: Structured Query Language Server Transformation The global Structured Query Language (SQL) Server Transformation market, estimated at USD 14 million in 2025, is projected to expand at a compound annual growth rate (CAGR) of 9.0% during the forecast period from 2025 to 2033. Key market drivers include the growing adoption of cloud computing, increasing data volumes, and the need for real-time data insights. Additionally, the rise of big data analytics and machine learning is further driving demand for advanced SQL Server transformation capabilities. The market segmentation comprises applications such as large enterprises and small and medium enterprises, and types including data integration scripts, information retrieval, analysis queries, and others. North America holds the largest market share due to the presence of major technology companies and the early adoption of cloud-based solutions. However, Asia Pacific is expected to witness the highest growth rate, driven by rapid digitization and the increasing adoption of data analytics in developing countries. Major companies in the market include Oracle Corporation, IBM Corporation, NuoDB, Microsoft Corporation, Alphabet, SingleStore, Teradata Corporation, Actian Corporation, SAP, and Amazon Web Services.

description: This search allows users to enter complex boolean queries to access all but the most recent day's EDGAR filings on www.sec.gov. Filings are from 1994 to present.; abstract: This search allows users to enter complex boolean queries to access all but the most recent day's EDGAR filings on www.sec.gov. Filings are from 1994 to present.

This technical report aims to provide detailed information on the results of Stage I of the methodology used to find references that are potentially relevant to the topic “Classification of Key Competencies for Construction Project Management.” In Stage I, potentially relevant references were searched using the American Society of Civil Engineers (ASCE) Library. In the ASCE Library, “advanced search” was used to find applicable references via specific search terms, topics and publication dates. For topics, the term “construction” was used. The option “title” was checked to specify where to look for search terms. The search terms used included competencies, competence, skill, capability, knowledge, project manager, project management, construction management, and engineering management. For more representative results, the search was restricted to references inclusively published from 1988 to 2019. When more than one chapter of a book was found, instead of counting all the chapters found, the book was counted as one single reference. In such cases, the book title might exclude all the search terms used. If the same reference was found under different search terms, it was numbered only one time when counting the total number of references initially found. This process resulted in 2,102 references retrieved from the ASCE Library (Table 1 to Table 16). In the following Tables, “Selected: Yes” indicates that the initially-retrieved reference was ultimately selected for content analysis, and “Selected: No” means that the reference was not selected for content analysis.

This data includes information on Arsenic violations in the US, including time patterns and spatial patterns in Arsenic violations, and people served by systems in violation. Most of the data is from the Safe Drinking Water Information System. This dataset is associated with the following publication: Foster, S., M. Pennino, J. Compton, S. Leibowitz, and M. Kile. Arsenic Drinking Water Violations Decreased Across the United States Following Revision of the Maximum Contaminant Level.. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(19): 11478-11485, (2019).

Are you looking for data that tell if the companies or persons you look into own any patents? If they do, do you want to know how many patents they own?

The Assignee Query Data will provide you with a timely and comprehensive result of global patent ownership of the companies or individuals with the history of 50 years.

How do we do that?

We include decades’ worth of global full-text databases, such as the US, China, EM/EUIPO, Japan, Korea, WIPO and so on, and keep them updated on a timely basis—as frequently as every day or week, depending on the sources.

Furthermore, the data downloaded are cleansed to minimize data errors and thus search and analysis errors. For example, we standardize assignee names to enables individual patents to correspond to a single owner; logic-based corrections ensure that values are corrected based on rules.

In addition, we use advanced algorithms in analyzing, selecting, and presenting the most current and accurate information from multiple available data sources. For instance, a single patent’s legal status is triangulated across different patent data for accuracy. Moreover, proprietary Quality and Value rankings put patents in each key market under the equally evaluative process, offering subjective predictions for the patent's likelihood of validity and monetization.

This dataset is composed of the 28 SPARQL queries executed to generate the measurement tables which are included in the files belonging to dataset containing the data tables results of the queries execution. They have the same name. They only differ by their extension. By example, CWG_reception_fallingNumber_raw.sparql is the file including the SPARQL query executed to obtain the table included in the file CWG_reception_fallingNumber_raw.tsv.