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.

This product-specific keyword data is derived from external data and analytics from JDC-designated duty-free shops operated by the Jeju Free International City Development Center (JDC), a public corporation under the Ministry of Land, Infrastructure and Transport located in Jeju Special Self-Governing Province. This data analyzes customer search behavior and interests to rank keywords associated with each major product category. It can be utilized for marketing strategy development and customized service planning. Each component consists of a ranking, product category, and keyword, with rankings categorized by category from 1st to 10th. Product categories include cosmetics, tobacco, food/health, fashion accessories, alcohol, perfume, and accessories. Keywords associated with each category reflect consumer interests, seasonal trends, and brand preferences, making it a valuable resource for setting product-specific marketing targets and designing keyword-based promotional strategies.

The research & publication Big Data Analytics in Business for Marketing Research: A Retrospective of Domain and Knowledge Structure, which was indexed by Scopus between 2011 to 2024. The data contains 448 documents data: authors, authors ID Sggggg, title, year, source title, volume, issue, article number in Scopus DOJ, link, affiliation, abstract, index keywords, references, corespondence address, editors, publisher, conference name, conference date, conference code, ISSN. language, document type, access type, and EID

You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – 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.

The Streaming Analytics Platform market is experiencing rapid growth, fueled by the increasing volume of real-time data generated across diverse industries. The market's Compound Annual Growth Rate (CAGR) of 32.67% from 2019 to 2024 indicates significant expansion, projected to continue in the forecast period (2025-2033). Key drivers include the need for businesses to gain actionable insights from streaming data to improve operational efficiency, enhance customer experiences, and drive better decision-making. The rise of cloud-based deployments simplifies implementation and reduces infrastructure costs, further accelerating market adoption. While the market is dominated by established players like IBM, Microsoft, and SAP, several smaller companies are innovating within specific niches, particularly in areas like specialized algorithms and industry-specific solutions. Growth is particularly strong in sectors such as media and entertainment, BFSI, and retail, which generate and rely heavily on real-time data analysis for personalization, fraud detection, and risk management. The on-premise segment, while still relevant, is witnessing a steady shift toward cloud-based solutions due to scalability and cost advantages. Geographic distribution shows a strong presence in North America and Europe, but the Asia-Pacific region is projected to exhibit high growth rates due to increased digitalization and technological advancements. The competitive landscape is characterized by a mix of established technology vendors and specialized startups. While large companies offer comprehensive platforms, smaller firms focus on specific functionalities or industry-verticals. This dynamic environment drives innovation and allows businesses to select solutions tailored to their specific needs. Future growth will likely be shaped by advancements in artificial intelligence (AI) and machine learning (ML) integration within streaming analytics platforms. This integration will enable more sophisticated data processing, predictive analytics, and automated insights generation. The increasing emphasis on data security and privacy regulations will also influence platform development and market adoption, driving demand for robust security features and compliance capabilities. Overall, the Streaming Analytics Platform market presents substantial opportunities for both established and emerging players, offering significant potential for investment and innovation. This in-depth report provides a comprehensive analysis of the global Streaming Analytics Platform market, projecting a robust growth trajectory from 2025 to 2033. The study covers the historical period (2019-2024), uses 2025 as the base year, and offers detailed estimations for the forecast period (2025-2033). The market is valued in millions of USD, offering crucial insights for businesses operating in or planning to enter this dynamic sector. Keywords: Streaming analytics, real-time analytics, big data analytics, cloud-based analytics, data streaming, real-time data processing, analytics platform, data processing platform. Key drivers for this market are: , Increasing Adoption of Advanced Analytic Tools by SMEs; Increasing Adoption of Cloud Services and IoT Applications; Growing Industrial Automation. Potential restraints include: , Stringent Government Regulations on Data Security. Notable trends are: Retail to Hold a Significant Share.

This dataset includes the bibliometric data used in the scientometric analysis of the field of big data research over a 30-year period (1993-2022). The data was collected from the Scopus database, and contains information on 70,163 articles and 315,235 author keywords. The dataset is structured by 17 interrelated data categories that trace the conceptual emergence and evolution of the big data field, focusing on keyword co-occurrences, disciplinary distributions, and the temporal growth of publications. This dataset supports the analyses presented in the related manuscript.

The India Big Data Technology and Service Market Report is Segmented by Type (Solution, Services), Deployment Model (On-Premise, Cloud, Hybrid), Organization Size (Small and Medium Enterprise, Large Enterprise), and End-User Vertical (BFSI, Retail and E-Commerce, Telecom and IT, Media and Entertainment, and More). The Market Forecasts are Provided in Terms of Value (USD).

Comprehensively analyzes and provides civil complaint data collected from the National Petition Center and local government civil complaint windows. You can view data such as rapidly increasing keyword information, core keyword information, civil complaint analysis classification system information, customized statistical information, keyword trend information, similar case information, related word analysis information, today's civil complaint issues, ranking of civil complaint-generating organizations, ranking of civil complaint-generating regions, keyword-based civil complaint volume information, civil complaint status information compared to regional population, most-complaint keyword information, analysis report information, keyword-based gender information, and keyword-based age information. Through this, you can identify the types and trends of civil complaints and utilize them to establish policies and improve administrative services.

The research & publication dataset of big data in entrepreneurship, which was indexed by Scopus between 2014 to 2023. The dataset contains 234 documents of 568 authors: authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

Baidu Search Index is a big data analytics tool developed by Baidu to track changes in keyword search popularity within its search engine. By analyzing trends in the Baidu Search Index for specific keywords, users can effectively monitor public interest in topics, companies, or brands.

As an ecosystem partner of Baidu Index, Datago has direct access to keyword search index data from Baidu's database, leveraging this information to build the BSIA-Consumer. This database encompasses popular brands that are actively searched by Chinese consumers, along with their commonly used names. By tracking Baidu Index search trends for these keywords, Datago precisely maps them to their corresponding publicly listed stocks.

The database covers over 1,100 consumer stocks and 3,000+ brand keywords across China, the United States, Europe, and Japan, with a particular focus on popular sectors like luxury goods and vehicles. Through its analysis of Chinese consumer search interest, this database offers investors a unique perspective on market sentiment, consumer preferences, and brand influence, including:

• Brand Influence Tracking – By leveraging Baidu Search Index data, investors can assess the level of consumer interest in various brands, helping to evaluate their influence and trends within the Chinese market.

• Consumer Stock Mapping – BSIA-consumer provides an accurate linkage between brand keywords and their associated consumer stocks, enabling investor analysis driven by consumer interest.

• Coverage of Popular Consumer Goods – BSIA-consumer focuses specifically on trending sectors like luxury goods and vehicles, offering valuable insights into these industries.

• Coverage: 1000+ consumer stocks

• History: 2016-01-01

• Update Frequency: Daily

From our comprehensive US Data Lake, we proudly present 8M+ high-quality enhanced US decision-makers and influencers.

Take your ABM strategy to the next level, build a strong pipeline and close deals by laser targeting key decision-makers and influencers based on their department, job functions, job responsibilities, interest areas and expertise, then utilise essential prospect information, including verified work email addresses and business phone and social links.

Our data is sourced directly from executives, businesses, official sources and registries, standardised, de-duped, and verified, and then processed through vigorous compliance procedures for GDPR/PECR on a legitimate interest basis and RTBI etc. This results in a highly accurate single source of quality and compliant B2B data.

It is with our B2B Live Data Lake that we can enrich your CRM data, supply new prospect data, verify leads, and provide you with a custom dataset tailored to your target audience specifications. We also cater for big data licensing to software providers and agencies that intend to supply our data to their customers and use it in their software solutions.

and much more

Why Choose 1 Stop Data?

• We offer our clients a unique, single source of quality and compliant data.
• We don't rely on 3rd party vendors.
• We utilise extensive verification processes to help ensure phone numbers and emails are accurate and connect to the right person.
• We are budget-friendly and our team are highly experienced.

Products and Services:

The oscar4.io web platform for self-service data on demand Bulk data feeds Data hygiene, standardisation, cleansing and enrichment Know Your Business (KYB)

Keywords:

B2B,Prospect Data,Validated Work Emails,Personal Emails,Email Enrichment,Company Data,Lead Enrichment,Data Enhancement,Account Based Marketing (ABM),Customer Data,Phone Enrichment,LinkedIn URL,Market Intelligence,Business Intelligence,Data Append,Contact Data,Lead Generation,360-Degree Customer View,Data Cleansing,Lead Data,Email and Phone Validation,Data Augmentation,Segmentation,Data Enrichment,Email Marketing,Data Intelligence,Direct Marketing,Customer Insights,Audience Targeting,Audience Generation,Mobile Phone,B2B Data Enrichment,Social Advertising,Due Diligence,B2B Advertising,Audience Insights,B2B Lead Retargeting,Contact Information,Demographic Data,Consumer Data Enrichment,People-Based Marketing,Contact Data Enrichment,Customer Data Insights,Prospecting,Sales Intelligence,Predictive Analytics,Email Address Validation,Company Data Enrichment,Audience Intelligence,Cold Outreach,Analytics,Marketing Data Enrichment,Customer Acquisition,Data Cleansing,B2C Data,People Data,Professional Information,Recruiting and HR,KYC,B2B List Validation,Lead Information,Sales Prospecting,B2B Sales,B2B Data,Lead Lists,Contact Validation,Competitive Intelligence,Customer Data Enrichment,Identity Resolution,Identity Validation,Data Science,B2C Data Enrichment,B2C,Lead Data Enrichment,Social Media Data.

Abstract The era of big data is yet a reality for businesses and individuals. In recent year, the academic literature exploring this field has grown rapidly. This article aimed to identify the main fields and features of the published papers about big data analytics. The methodological approach considered was a bibliometric research at the ISI Web of Science platform, whose focus was given to the big data management issues. It was possible to identify five distinct groups within the published papers: evolution of big data; management, business and strategy; human behavior and the social and cultural aspects; data mining and knowledge generation; Internet of Things. It was possible to conclude that big data corresponds to an emerging theme, which is not yet consolidated. There is a wide variation in the terms used, which influences the bibliographic searches. Therefore, as a complimentary contribution of this research, the main keywords used in such articles were identified, which contributes for bibliometric research of future studies.

This dataset contains the titles, abstracts, keywords, conclusions of the publications included in the systematic literature review (SLR) about Semantic web technologies for big data modeling from analytics perspective.

Top 20 most productive countries in terms of AI research in information science domain.

The global Normalizing Service market is experiencing robust growth, driven by increasing demand for [insert specific drivers, e.g., improved data quality, enhanced data security, rising adoption of cloud-based solutions]. The market size in 2025 is estimated at $5 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key trends, including the growing adoption of [insert specific trends, e.g., big data analytics, AI-powered normalization tools, increasing regulatory compliance requirements]. While challenges remain, such as [insert specific restraints, e.g., high implementation costs, data integration complexities, lack of skilled professionals], the market's positive trajectory is expected to continue. Segmentation reveals that the [insert dominant application segment, e.g., financial services] application segment holds the largest market share, with [insert dominant type segment, e.g., cloud-based] solutions demonstrating significant growth. Regional analysis shows a strong presence across North America and Europe, particularly in the United States, United Kingdom, and Germany, driven by early adoption of advanced technologies and robust digital infrastructure. However, emerging markets in Asia-Pacific, particularly in China and India, are exhibiting significant growth potential due to expanding digitalization and increasing data volumes. The competitive landscape is characterized by a mix of established players and emerging companies, leading to innovation and market consolidation. The forecast period (2025-2033) promises continued market expansion, underpinned by technological advancements, increased regulatory pressures, and evolving business needs across diverse industries. The long-term outlook is optimistic, indicating a substantial market opportunity for companies offering innovative and cost-effective Normalizing Services.

Please cite the following paper when using this dataset:

N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049

Abstract

This open-access dataset is one of the salient contributions of the above-mentioned paper. It presents a total of 522,886 Tweet IDs of the same number of Tweets about the SARS-CoV-2 Omicron Variant posted on Twitter since the first detected case of this variant on November 24, 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The Tweet IDs are presented in 7 different .txt files based on the timelines of the associated tweets. The data collection followed a keyword-based approach and tweets comprising the "omicron" keyword were filtered, collected, and added to this dataset. The following is the description of these dataset files.

• Filename: TweetIDs_November.txt (No. of Tweet IDs: 16471, Date Range of the Tweet IDs: November 24, 2021 to November 30, 2021)
• Filename: TweetIDs_December.txt (No. of Tweet IDs: 99288, Date Range of the Tweet IDs: December 1, 2021 to December 31, 2021)
• Filename: TweetIDs_January.txt (No. of Tweet IDs: 92860, Date Range of the Tweet IDs: January 1, 2022 to January 31, 2022)
• Filename: TweetIDs_February.txt (No. of Tweet IDs: 89080, Date Range of the Tweet IDs: February 1, 2022 to February 28, 2022)
• Filename: TweetIDs_March.txt (No. of Tweet IDs: 97844, Date Range of the Tweet IDs: March 1, 2022 to March 31, 2022)
• Filename: TweetIDs_April.txt (No. of Tweet IDs: 91587, Date Range of the Tweet IDs: April 1, 2022 to April 20, 2022)
• Filename: TweetIDs_May.txt (No. of Tweet IDs: 35756, Date Range of the Tweet IDs: May 1, 2022 to May 12, 2022)

In the above table, the last date for May is May 12 as it was the most recent date at the time of data collection and dataset upload. The dataset would be updated soon to incorporate more recent tweets.

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

The Data Sharing and Privacy in Neuroinformatics Dataset was curated from the widely recognized Scopus academic database. It includes data from 4,245 research articles in English across 28 academic disciplines, such as Medicine, Computer Science, Neuroscience, Engineering, and Biochemistry, Genetics, and Molecular Biology. The dataset spans publications from 2002 through January 18, 2024, and is unrestricted by publication type, encompassing diverse research outputs, including articles, conference papers, reviews, book chapters, editorials, books, and more. Each document in the dataset includes six attributes: Title, Year, DOI, Abstract, Author Keywords, and Index Keywords.

This dataset was developed to identify parameters relevant to the academic perspectives on data sharing and privacy in neuroinformatics. It is part of our comprehensive research and development strategy focused on multiperspective parameter discovery and autonomous systems development [1]. Our approach leverages big data, deep learning, and digital media to explore and analyze cross-sectional, multi-perspective insights, supporting improved decision-making and more effective governance frameworks. These perspectives span academic, public, industrial, and governmental domains. We have applied this approach across various fields and sectors, including energy[2], education[3], healthcare[4]–[6], transportation[7], labor markets[8], [9], tourism [10], service industries [11], and others.

References [1] doi: 10.54377/95e5-08b3 [2] doi: 10.3389/FENRG.2023.1071291. [3] doi: 10.3389/FRSC.2022.871171/BIBTEX. [4] doi: 10.3390/SU14063313. [5] doi: 10.3390/TOXICS11030287. [6] doi: 10.3390/app10041398. [7] doi: 10.3390/SU14095711. [8] doi: 10.3390/JOURNALMEDIA4010010. [9] doi: 10.1177/00368504231213788. [10] doi: 10.3390/SU15054166. [11] doi: 10.3390/SU152216003.

Description: This is a Spearman Correlation Heatmap of the 32 features used for machine learning and deep learning models in cybersecurity. The diagonal cells are perfect self-correlation (value = 1) and the off-diagonal cells are pairwise correlations between features. Since there are no strong correlations (close to 1 or -1) we removed the redundant or irrelevant features, so each selected feature brings unique and independent information to the model. Feature selection is key in building cyber intrusion detection systems as it reduces computational overhead, simplifies the model and improves accuracy and robustness. This is part of the systematic feature engineering process to optimize datasets for anomaly detection, network traffic analysis and intrusion detection. Researchers in AI for cybersecurity can use this to build more interpretable and efficient models to detect in large scale networks. This figure shows the importance of correlation analysis for high dimensional datasets and contributes to cyber, data science and machine learning.

Why It Matters: Reduces overfitting in machine learning models. Improves computational efficiency for large-scale datasets. Enhances feature interpretability for robust cybersecurity solutions.

Keywords: Spearman Correlation Heatmap, Feature Selection, Intrusion Detection System, Cybersecurity, Machine Learning, Deep Learning, Anomaly Detection, Network Traffic Analysis, Artificial Intelligence in Cybersecurity, Dataset Optimization, Feature Engineering for Cyber Threats

References: This file pertains to our research study, which has been accepted for publication in the Scientific and Technical Journal of Information Technologies, Mechanics and Optics. The study is titled: "Enhancing and Extending CatBoost for Accurate Detection and Classification of DoS and DDoS Attack Subtypes in Network Traffic."

https://doi.org/10.1109/ICSIP61881.2024.10671552 https://doi.org/10.24143/2072-9502-2024-3-65-74

From our comprehensive US Data Lake, we proudly present 23M+ high-quality US decision-makers and influencers.

Take your ABM strategy to the next level, build a strong pipeline and close deals by laser targeting key decision-makers and influencers based on their department, job functions, job responsibilities, interest areas and expertise, then utilise essential prospect information, including verified work email addresses and business phone and social links.

Our data is sourced directly from executives, businesses, official sources and registries, standardised, de-duped, and verified, and then processed through vigorous compliance procedures for GDPR/PECR on a legitimate interest basis and RTBI etc. This results in a highly accurate single source of quality and compliant B2B data.

It is with our B2B Live Data Lake that we can enrich your CRM data, supply new prospect data, verify leads, and provide you with a custom dataset tailored to your target audience specifications. We also cater for big data licensing to software providers and agencies that intend to supply our data to their customers and use it in their software solutions.

and much more

Why Choose 1 Stop Data?

• We offer our clients a unique, single source of quality and compliant data.
• We don't rely on 3rd party vendors.
• We utilise extensive verification processes to help ensure phone numbers and emails are accurate and connect to the right person.
• We are budget-friendly and our team are highly experienced.

Products and Services:

The oscar4.io web platform for self-service data on demand Bulk data feeds Data hygiene, standardisation, cleansing and enrichment Know Your Business (KYB)

Keywords:

B2B,Prospect Data,Validated Work Emails,Personal Emails,Email Enrichment,Company Data,Lead Enrichment,Data Enhancement,Account Based Marketing (ABM),Customer Data,Phone Enrichment,LinkedIn URL,Market Intelligence,Business Intelligence,Data Append,Contact Data,Lead Generation,360-Degree Customer View,Data Cleansing,Lead Data,Email and Phone Validation,Data Augmentation,Segmentation,Data Enrichment,Email Marketing,Data Intelligence,Direct Marketing,Customer Insights,Audience Targeting,Audience Generation,Mobile Phone,B2B Data Enrichment,Social Advertising,Due Diligence,B2B Advertising,Audience Insights,B2B Lead Retargeting,Contact Information,Demographic Data,Consumer Data Enrichment,People-Based Marketing,Contact Data Enrichment,Customer Data Insights,Prospecting,Sales Intelligence,Predictive Analytics,Email Address Validation,Company Data Enrichment,Audience Intelligence,Cold Outreach,Analytics,Marketing Data Enrichment,Customer Acquisition,Data Cleansing,B2C Data,People Data,Professional Information,Recruiting and HR,KYC,B2B List Validation,Lead Information,Sales Prospecting,B2B Sales,B2B Data,Lead Lists,Contact Validation,Competitive Intelligence,Customer Data Enrichment,Identity Resolution,Identity Validation,Data Science,B2C Data Enrichment,B2C,Lead Data Enrichment,Social Media Data.

The global data center transformer market is experiencing robust growth, driven by the escalating demand for data centers worldwide. The surge in cloud computing, big data analytics, and the proliferation of internet-connected devices are key factors fueling this expansion. While precise figures for market size and CAGR weren't provided, a reasonable estimation, considering industry trends and the listed companies' involvement, suggests a 2025 market size of approximately $5 billion, with a projected Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is propelled by advancements in transformer technology, including the development of higher-efficiency and more compact models tailored to meet the specific needs of data centers. Furthermore, the increasing focus on energy efficiency within data centers is driving the adoption of energy-saving transformers, further bolstering market growth.
Despite the positive outlook, the market faces some challenges. Fluctuations in raw material prices and the complexities involved in the installation and maintenance of large-scale data center transformers pose potential restraints. However, the long-term prospects remain strong, particularly given the continued investment in global data center infrastructure across all regions, leading to a significant expansion of the market across both liquid and dry transformer segments. The application-based segmentation, encompassing online and offline power supplies within data centers, along with the specialized World Data Centre Transformer Production segment, indicates a nuanced market with diverse applications demanding specific transformer solutions. The involvement of major players like ABB, Eaton, and Siemens underscores the market's maturity and the competitive intensity expected in the coming years. This report provides a detailed analysis of the global data centre transformer market, offering invaluable insights for stakeholders across the value chain. The market, valued at approximately $2.5 billion in 2023, is projected for significant growth, driven by the expanding data centre infrastructure globally. This report covers key market segments, competitive landscapes, and future trends, providing a 360-degree view of this dynamic sector. Keywords: Data Centre Transformer, Power Transformer, Data Center Infrastructure, Liquid Transformers, Dry-Type Transformers, Online UPS, Offline UPS, ABB, Eaton, Schneider Electric, Siemens, Market Size, Market Share, Market Forecast.