Structured dataset about schema markup implementation strategies and best practices for AI and voice assistant optimization.

Auto-generated structured data of Google Search Console Field Reference from table Fields

Our advanced data extraction tool is designed to empower businesses, researchers, and developers by providing an efficient and reliable way to collect and organize information from any online source. Whether you're gathering market insights, monitoring competitors, tracking trends, or building data-driven applications, our platform offers a perfect solution for automating the extraction and processing of structured data from websites. With seamless integration of AI, our tool takes the process a step further, enabling smarter, more refined data extraction that adapts to your needs over time.

In a digital world where information is continuously updated, timely access to data is critical. Our tool allows you to set up automated data extraction schedules, ensuring that you always have access to the most current information. Whether you're tracking stock prices, monitoring social media trends, or gathering product information, you can configure extraction schedules to suit your needs. Our AI-powered system also allows the tool to learn and optimize based on the data it collects, improving efficiency and accuracy with repeated use. From frequent updates by the minute to less frequent daily, weekly, or monthly collections, our platform handles it all seamlessly.

Our tool doesn’t just gather data—it organizes it. The extracted information is automatically structured into easily usable formats like CSV, JSON, or XML, making it ready for immediate use in applications, databases, or reports. We offer flexibility in the output format to ensure smooth integration with your existing tools and workflows. With AI-enhanced data parsing, the system recognizes and categorizes information more effectively, providing higher quality data for analysis, visualization, or importing into third-party systems.

Whether you’re collecting data from a handful of pages or millions, our system is built to scale. We can handle both small and large-scale extraction tasks with high reliability and performance. Our infrastructure ensures fast, efficient processing, even for the most demanding tasks. With parallel extraction capabilities, you can gather data from multiple sources simultaneously, reducing the time it takes to compile large datasets. AI-powered optimization further improves performance, making the extraction process faster and more adaptive to fluctuating data volumes.

Our tool doesn’t stop at extraction. We provide options for enriching the data by cross-referencing it with other sources or applying custom rules to transform raw information into more meaningful insights. This leads to a more insightful and actionable dataset, giving you a competitive edge through superior data-driven decision-making.

Modern websites often use dynamic content generated by JavaScript, which can be challenging to extract. Our tool, enhanced with AI, is designed to handle even the most complex web architectures, including dynamic loading, infinite scrolling, and paginated content.

Finally, our platform provides detailed logs of all extraction activities, giving you full visibility into the process. With built-in analytics, AI-powered insights can help you monitor progress, and identify issues.

In today’s fast-paced digital world, access to accurate, real-time data is critical for success. Our AI-integrated data extraction tool offers a reliable, flexible, and scalable solution to help you gather and organize the information you need with minimal effort. Whether you’re looking to gain a competitive edge, conduct in-depth research, or build sophisticated applications, our platform is designed to meet your needs and exceed expectations.

This dataset is about books. It has 2 rows and is filtered where the book is Structured search for big data : from keywords to key-objects. It features 7 columns including author, publication date, language, and book publisher.

FILS, Douglas, Ocean Leadership, 1201 New York Ave, NW, 4th Floor, Washington, DC 20005, SHEPHERD, Adam, Woods Hole Oceangraphic Inst, 266 Woods Hole Road, Woods Hole, MA 02543-1050 and LINGERFELT, Eric, Earth Science Support Office, Boulder, CO 80304

The growth in the amount of geoscience data on the internet is paralleled by the need to address issues of data citation, access and reuse. Additionally, new research tools are driving a demand for machine accessible data as part of researcher workflows. In the commercial sector, elements of this have been addressed by the use of the Schema.org vocabulary encoded via JSON-LD and coupled with web publishing patterns. Adaptable publishing approaches are already in use by many data facilities as they work to address publishing and FAIR patterns. While these often lack the structured data elements these workflows could be leveraged to additionally implement schema.org style publishing patterns.

This presentation will report on work that grew out of the EarthCube Council of Data Facilities known as, Project 418. Project 418 was a proof of concept funded by the EarthCube Science Support Office for exploring the approach of publishing JSON-LD with schema.org and extensions by a set of NSF data facilities. The goal was focused on using this approach to describe data set resources and evaluate the use of this structured metadata to address discovery. Additionally, we will discuss growing interest by Google and others in leveraging this approach to data set discovery.

The work scoped 47,650 datasets from 10 NSF-funded data facilities. Across these datasets, the harvester found 54,665 data download URLs, and approximately 560K dataset variables and 35k unique identifiers (DOIs, IGSNs or ORCIDs).

The various publishing workflows used by the involved data facilities will be presented along with the harvesting and interface developments. Details on how resources were indexed into text, spatial and graph systems and used for search interfaces will be presented along with future directions underway building on this foundation.

Zoro.com Product Data Sample – Explore Structured E-commerce Product Listings

This dataset is a sample extraction of product listings from Zoro.com, a leading industrial supply e-commerce platform. It provides structured product-level data that can be used for market research, price comparison engines, product matching models, and e-commerce analytics.

The sample includes a variety of products across tools, hardware, safety equipment, and industrial supplies — with clean, structured fields suitable for both analysis and model training.

Also available: Grainger Product Datasets – structured data from a top industrial supplier.

Submit your custom data requests via the Zoro products page or contact us directly at contact@crawlfeeds.com.

Ideal for previewing before requesting larger or full Zoro datasets

Use Cases:

• Building product comparison or search engines

• Price intelligence and competitor monitoring

• Product classification and attribute extraction

• Training data for e-commerce AI models

Want More?

This is a sample of a much larger dataset extracted from Zoro.com.
👉 Contact us to access full datasets or request custom category extractions.

Snapshot img

Enterprise Search Market Size 2025-2029

The enterprise search market size is forecast to increase by USD 4.21 billion, at a CAGR of 10.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing penetration of the Internet worldwide and the rise of digital assistants and voice search technologies. These trends reflect the evolving digital landscape, as businesses and organizations seek to optimize their online presence and enhance user experience. However, the market also faces challenges, most notably the growing concern related to cyberattacks. As businesses increasingly rely on digital platforms for information management and retrieval, ensuring the security of enterprise search systems becomes paramount. Improvements in information technology, such as 5G technology and broadband, are also contributing to the growth of the market.
Companies must invest in robust security measures to protect sensitive data and mitigate the risks associated with cyber threats. To capitalize on the opportunities presented by the market and navigate these challenges effectively, organizations should prioritize innovation, invest in advanced technologies, and maintain a strong focus on user experience and security. Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies are revolutionizing search experiences, enabling personalized results and improving user experience.

What will be the Size of the Enterprise Search Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, driven by advancements in technology and the increasing demand for efficient information access across various sectors. Key components of this dynamic landscape include keyword extraction, search indexing, and information architecture, which enable accurate and relevant results. Boolean search and search analytics provide further refinement, while metadata extraction and data governance ensure data quality. Personalized search, natural language processing, and vector search are transforming the user experience, delivering more precise and contextually relevant results. Knowledge management, relevance ranking, and search filtering further enhance the search process, while semantic search and user behavior analysis provide deeper insights.
Clickstream data and query logs offer valuable information for optimizing search UI and search performance. Document ranking and query understanding are essential for delivering accurate and timely results. Search UI and search performance are crucial factors in user satisfaction, driving the ongoing development of enterprise search solutions. According to recent industry reports, the market is expected to grow by over 15% annually, reflecting the continuous demand for advanced search capabilities and the integration of emerging technologies. For instance, a leading financial services company reported a 25% increase in sales following the implementation of a new enterprise search solution.

How is this Enterprise Search Industry segmented?

The enterprise search industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Local search
  Hosted search


End-user

  Large enterprises
  SMEs


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The Local search segment is estimated to witness significant growth during the forecast period. The market in the US is a significant and continually evolving sector, with local search holding a substantial share in 2024. Approximately 60% of all searches performed on Google are local, underlining its importance for businesses aiming to reach their target audience effectively. This trend is expected to persist, as the local search segment is projected to continue its dominance during the forecast period. Local search offers numerous advantages for various industries, including real estate, legal firms, dental clinics, and small businesses. By optimizing a website for a specific geographical area, businesses can attract more targeted traffic and improve online visibility. Deep learning applications, including natural language processing and large language models, are transforming software design patterns, such as microservices architecture and prompt engineering software.

This, in turn, can lead to increased footfall at brick-and-mortar locations and higher online sales. Furthermore, local search helps businesses maintain accurate online directories and citations, ensuring consi

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

Dataset Overview:

This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

Sourced Directly from Reddit:

All social media data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

Key Features:

• Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
• User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
• Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
• AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

Use Cases:

• Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
• Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
• Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
• Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

Data Quality and Reliability:

The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

Integration and Usability:

The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

User-Friendly Structure and Metadata:

The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

Ideal For:

• Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
• Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
• Researchers: Explore the social dynamics of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conduc...

This dataset is about book subjects. It has 5 rows and is filtered where the books is Structured search for big data : from keywords to key-objects. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

Data Provided by: SRK, Data Scientist at H2O.ai, Chennai, India

Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a growing research area, but existing benchmarks have been limited to displayed tables. Tables sized and formatted for display in a Wikipedia page or ArXiv paper are considerably different from data tables in both scale and style. By using metadata associated with open data from government portals, we create the first dataset to benchmark search over data tables at scale. We demonstrate three styles of table-to-table related table search. The three notions of table relatedness are: tables produced by the same organization, tables distributed as part of the same dataset, and tables with a high degree of overlap in the annotated tags. The keyword tags provided with the metadata also permit the automatic creation of a keyword search over tables benchmark. We provide baselines on this dataset using existing methods including traditional and neural approaches.

According to Cognitive Market Research, the global Enterprise Search Engine market size will be USD 4358.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.70% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1743.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1307.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1002.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 217.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 87.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2024 to 2031.
The Solution category is the fastest growing segment of the Enterprise Search Engine industry

Market Dynamics of Enterprise Search Engine Market

Key Drivers for Enterprise Search Engine Market

Increasing Data Volume to Boost Market Growth

The increasing volume of data generated by organizations is a primary driver of the Enterprise Search Engine Market. As businesses accumulate vast amounts of structured and unstructured data from various sources—such as emails, documents, social media, and databases—the need for efficient retrieval and management becomes critical. Enterprise search engines enable organizations to sift through this data quickly, providing employees with timely access to information that can enhance decision-making and productivity. Additionally, the proliferation of big data technologies and cloud storage solutions contributes to data growth, necessitating robust search capabilities to ensure that valuable insights are not lost. This demand for streamlined access to comprehensive information continues to fuel the expansion of the enterprise search engine market. For instance, Google launched local search functionalities that were previewed earlier this year. These features enable users to explore their environment using their smartphone camera. Additionally, Google has added an option to search for restaurants by specific dishes and introduced new search capabilities within the Live View feature of Google Maps.

Increasing Demand for Data-Driven Decision-Making to Drive Market Growth

The rising demand for data-driven decision-making is significantly driving the Enterprise Search Engine Market. Organizations increasingly recognize the value of leveraging data analytics to inform strategic decisions, enhance operational efficiency, and improve customer experiences. As businesses strive to become more agile and responsive to market changes, they require quick access to relevant data across various departments and sources. Enterprise search engines facilitate this by enabling employees to efficiently retrieve and analyze critical information, thus supporting informed decision-making processes. Moreover, the integration of advanced analytics and artificial intelligence into enterprise search solutions further empowers organizations to derive actionable insights from their data. This trend towards a data-centric approach in business operations continues to propel the growth of the enterprise search engine market.

Restraint Factor for the Enterprise Search Engine Market

High Implementation Costs will Limit Market Growth

High implementation costs are a significant restraint on the growth of the Enterprise Search Engine Market. Deploying enterprise search solutions often involves substantial initial investments in software, hardware, and integration services. Organizations must consider expenses related to customizing the search engine to fit their unique data architectures and user needs. Additionally, ongoing maintenance, updates, and training for staff can contribute to overall costs, making it challenging for smaller businesses or those with limited budgets to adopt these systems. This financial barrier can hinder organizations from fully realizing the benefits of enterprise search engines, leading to under...

Structured Data Archiving (SDA) Software Market Outlook

The Structured Data Archiving (SDA) Software market size is projected to grow from USD 2.5 billion in 2023 to USD 6.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 11.8% during the forecast period. This significant growth is driven by the increasing volume of structured data generated by businesses and the necessity for efficient data management solutions. The global market is experiencing rapid advancements due to the escalating need to streamline data storage, reduce costs, and ensure compliance with regulatory standards.

One of the primary growth factors for the SDA software market is the exponential increase in data generation across various industries. With the proliferation of digital transactions, social media, IoT devices, and enterprise applications, organizations are inundated with vast amounts of structured data. This data, often stored in databases and spreadsheets, requires effective archiving solutions to manage storage costs and improve retrieval efficiency. As businesses continue to digitize their operations, the demand for SDA software is poised to grow substantially. Additionally, regulatory requirements mandating long-term data retention for compliance purposes are further propelling the market.

Another significant factor contributing to the growth of the SDA software market is the rising need for cost optimization in data management. Large volumes of structured data occupy expensive primary storage systems, leading to higher operational costs. SDA solutions offer an economic alternative by offloading inactive data from primary storage to more cost-effective archival storage. This not only reduces storage expenses but also enhances system performance by freeing up primary storage resources. Consequently, organizations are increasingly adopting SDA software to achieve cost savings and operational efficiency.

The growing focus on data security and privacy is also driving the adoption of SDA solutions. With the introduction of stringent data protection regulations such as GDPR, CCPA, and HIPAA, businesses are compelled to ensure secure and compliant data archiving practices. SDA software provides robust security features, including encryption, access controls, and audit trails, enabling organizations to safeguard archived data against unauthorized access and breaches. This heightened emphasis on data security and regulatory compliance is significantly fostering the market's expansion.

From a regional perspective, North America is anticipated to dominate the SDA software market throughout the forecast period, owing to the presence of numerous technology giants and early adopters of innovative data management solutions. The region's strong regulatory framework and high adoption rates of cloud-based services further drive market growth. Europe is also expected to witness substantial growth, supported by stringent data protection regulations and the increased demand for cost-efficient data management solutions. Meanwhile, the Asia Pacific region is poised for rapid expansion, driven by the burgeoning IT and telecommunications sector, increasing digitalization, and favorable government initiatives promoting data management and security.

Component Analysis

The SDA software market is segmented by components into software and services. The software segment encompasses various standalone and integrated solutions designed to archive structured data from diverse sources. This segment is witnessing rapid growth due to the increasing adoption of advanced software tools that facilitate efficient data archiving, retrieval, and management. Organizations across different sectors are investing in sophisticated SDA software to enhance their data handling capabilities, comply with regulatory requirements, and optimize storage costs.

Within the software segment, there is a notable trend of integrating artificial intelligence and machine learning technologies. These advancements enable SDA solutions to automate data classification, enhance search functionalities, and provide predictive analytics. By leveraging AI and ML, businesses can achieve higher accuracy in data archiving and retrieval processes, thereby improving operational efficiency. Additionally, the integration of these technologies helps in identifying patterns and insights from archived data, which can be valuable for strategic decision-making.

The services segment, on the other hand, includes consulting, implementation, and maintenance services provided by vendors to support the

Auto-generated structured data of Google Search Ads 360 (SA360) from table Fields

ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics

Auto-generated structured data of Apple Search Ads Field Reference from table Fields

BigBox API provides reliable, real-time Home Depot product, category, reviews, and offers data. All data includes comprehensive coverage of each of the search results in a cleanly structured output.

You can originate your request from any zip code (US) to see results as they would appear to customers in the specified location i.e. shipping info. BigBox APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your Home Depot data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, search results page URL, or for single products, by the Home Depot item ID or by global identifiers such as GTIN, ISBN, UPC and EAN. GTIN-based requests work by looking up the GTIN/ISBN/UPC on Home Depot first, then retrieving the product details for the first matching item ID.

So what's in the data from BigBox API?

Product: - Item & parent ID - UPC - Store SKU - In-store bay &/or aisle - Product specifications - Description - Imagery - Product videos - Buy Box winner: price and fulfillment info - Rating & reviews count - Descriptive attributes

Search results: - Product details per search result: - Position - Related queries - Pagination - Facets

How can BigBox API be used? - Product listing management - Price monitoring - Category & product trends monitoring - Market research & competitor intelligence - Location-specific shipping data - Rank tracking on Home Depot

...and more, depending on your request parameters or the search result.

Who uses BigBox API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

Benchmark for End-User Structured Data User Interfaces (BESDUI) based on the Berlin SPARQL Benchmark (BSBM) but intended for benchmarking the user experience while exploring a structured dataset, not the performance of the query engine. BSBM is just used to provide the data to be explored. This is a cheap User Interface benchmark as it does not involve users but experts, who measure how many interaction steps are required to complete each of the benchmark tasks, if possible. This also facilitates comparing different tools without the bias that different end-user profiles might introduce. The way to measure this interaction steps and convert them to an estimate of the required time to complete a task is based on the Keystroke-Level Model (KLM)

found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive. For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at http://discuss.eol.org/c/eol-services