Search engines, which collect, organize and display knowledge of the internet, remain central to the digital economy but are entering a period of rapid transformation driven by AI and shifting user behavior. Over the past five years, internet advertising spending maintained strong momentum, propelled by growing mobile internet access and consumer screen time. Consequently, industry revenue is expected to climb at a CAGR of 9.4% to $316.8 billion, including an anticipated rise of 7.7% in 2025, with profit at 18.6%. The industry stands apart from most in the tech sector, because of its platform-based revenue model, aggregation dynamics and deep integration with the broader digital ecosystem. While user engagement fuels relevance, it is advertiser demand that sustains revenue, requiring a careful balance between utility and monetization. This landscape has been reshaped by the rise of generative AI. Conversational tools and AI-generated summaries are reducing user interaction with traditional search results, challenging established SEO practices and disrupting referral-based traffic flows. Meanwhile, search engines are reconfiguring their ad models to prioritize quality and contextual relevance, moving away from legacy monetization strategies. These trends signal a broader shift in how search platforms operate, less as navigational tools and more as integrated, AI-driven environments. As digital behavior fragments and users seek information across apps like Amazon, TikTok and ChatGPT, industry revenue is still projected to climb at a CAGR of 7.3% to $449.9 billion through 2030. Advertisers are expected to continue investing in search, drawn by the format’s performance insights and optimization capabilities. However, AI is redefining search from a navigational tool into a task-oriented solution engine, where users expect conversational, multimodal and predictive answers instead of traditional results pages. To stay relevant, incumbent platforms must evolve into embedded AI utilities that power experiences across devices and enterprise workflows.

This provides a link to the Washington Secretary of State's Corporations Search tool. The Corporations Data Extract feature is no longer available. Customers needing a list of multiple businesses can use our advanced search to create a list of businesses under specific parameters. You can export this information to an Excel spreadsheet to sort and search more extensively. Below are the steps to perform this type of search. The more specified parameter searches provide narrower search results. Please visit our Corporations and Charities Filing System by following this link https://ccfs.sos.wa.gov/ Scroll down to the “Corporation Search” section and click the “Advanced Search” button on the right. Under the first section, specify how you would like the business name searched. Only use this for single business lookups unless all the businesses you are searching have a common name (use the “contains” selection). Select the appropriate business type from the dropdown if you are looking for a list of a specific business type. For a list of a particular business type with a specific status, select that status under “Business Status.” You can also search by expiration date in this section. Under the “Date of Incorporation/Formation/Registration,” you can search by start or end date. Under the “Registered Agent/Governor Search” section, you can search all businesses with the same registered agent on record or governor listed. Once you have made all your search selections, click the green “Search” button at the bottom right of the page. A list will populate; scroll to the bottom and select the green Excel document icon with CSV. An Excel document should automatically download. If you have popups blocked, please unblock our site, and try again. Once you have opened the downloaded Excel spreadsheet, you can adjust the width of each column and sort the data using the data tab. You can also search by pressing CTRL+F on a Windows keyboard.

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Dataset Card for "code-search-net-go"

  Dataset Summary

This dataset is the Go portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the function does.

  Languages

The dataset's comments are in English and the functions are coded in Go

  Data Splits

Train, test, validation labels are included in the dataset as… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-go.

A table listing published reports of macroinvertebrate richness in agriculturally impacted streams based on a literature search.

The goal of this research is to examine direct answers in Google web search engine. Dataset was collected using Senuto (https://www.senuto.com/). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.

Dataset contains the following elements:

keyword,

number of monthly searches,

featured domain,

featured main domain,

featured position,

featured type,

featured url,

content,

content length.

Dataset with visibility structure has 743 798 keywords that were resulting in SERPs with direct answer.

The primary function of the Livestock and Grain Market News Division of the Livestock and Seed Program (LSP) is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of livestock, meat, grain, and their related products nationally and internationally.

With a market share of more than ** percent, Google was the most popular desktop search engine in Japan in January 2024. It was followed by Bing, which accounted for more than ** percent of the desktop search engine market.

Introduction

Search Engine Statistics: Search engines are now widely used and have built a big industry that affects how other businesses run. Here are Search Engine Statistics to show you the current trends and possible future changes in the search industry.

Search engines are here to stay, but how people look for information online is changing. Most users now search using mobile devices. Google is still the leader, even with more competition, and more Americans are worried about privacy when using search engines.

As of January 2024, Google led the search engine market in Indonesia with a ***** percent share of the market. In the same year, Bing and Yahoo! followed with minor market shares.

Around ** percent of search engine referrals in Saudi Arabia in December 2024 were through Google. In comparison, **** percent of the referrals in the kingdom in the same period were through Bing.

1. The adaptation of plants to future climatic conditions is crucial for their survival. Not surprisingly, phenotypic responses to climate change have already been observed in many plant populations. These responses may be due to evolutionary adaptive changes or phenotypic plasticity. Especially plant species with a wide geographic range are either expected to show genetic differentiation in response to differing climate conditions or to have a high phenotypic plasticity. 2. We investigated phenotypic responses and plasticity as an estimate of the adaptive potential in the widespread species Silene vulgaris. In a greenhouse experiment, 25 European populations covering a geographic range from the Canary Islands to Sweden were exposed to three experimental precipitation and two temperature regimes mimicking a possible climate-change scenario for central Europe. We hypothesized that southern populations have a better performance under high temperature and drought conditions, as they are already adapted to a comparable environment. 3. We found that our treatments significantly influenced the plants, but did not reveal a latitudinal difference in response to climate treatments for most plant traits. Only flower number showed a stronger plasticity in northern European populations (e.g. Swedish populations) where numbers decreased more drastically with increased temperature and decreased precipitation treatment. 4. Synthesis. The significant treatment response in S. vulgaris, independent of population origin — except for the number of flowers produced — suggests a high degree of universal phenotypic plasticity in this widely distributed species. This reflects the likely adaptation strategy of the species and forms the basis for a successful survival strategy during upcoming climatic changes. However, as flower number, a strongly fitness related trait, did decrease in northern populations under a climate-change scenario, there might be limits to adaptation even in this widespread, plastic species.

China Google Search Trends: Online Shopping: Tmall data was reported at 8.000 Score in 14 May 2025. This stayed constant from the previous number of 8.000 Score for 13 May 2025. China Google Search Trends: Online Shopping: Tmall data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 70.000 Score in 22 Jan 2023 and a record low of 0.000 Score in 02 May 2025. China Google Search Trends: Online Shopping: Tmall data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s China – Table CN.Google.GT: Google Search Trends: by Categories.

We asked UK consumers about "Flight search engine online bookings by brand" and found that ************* takes the top spot, while ************* is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 2,014 consumers in the UK.

Previous studies on supporting free-form keyword queries over RDBMSs provide users with linked-structures (e.g.,a set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. In this paper, we study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. We define a keyword-based query language and an IR-style relevance model for coring/ranking cells in the text cube. Given a keyword query, our goal is to find the top-k most relevant cells. We propose four approaches, inverted-index one-scan, document sorted-scan, bottom-up dynamic programming, and search-space ordering. The search-space ordering algorithm explores only a small portion of the text cube for finding the top-k answers, and enables early termination. Extensive experimental studies are conducted to verify the effectiveness and efficiency of the proposed approaches. Citation: B. Ding, B. Zhao, C. X. Lin, J. Han, C. Zhai, A. N. Srivastava, and N. C. Oza, “Efficient Keyword-Based Search for Top-K Cells in Text Cube,” IEEE Transactions on Knowledge and Data Engineering, 2011.

Paid Search Software Market Outlook

The global paid search software market size was valued at approximately USD 5 billion in 2023 and is projected to reach USD 9 billion by 2032, registering a compound annual growth rate (CAGR) of around 6.5% during the forecast period. This substantial growth can be attributed to the increasing digitalization of businesses and the rising importance of online marketing strategies across various sectors. Paid search software has emerged as a critical tool for businesses looking to optimize their online presence and target specific audiences more effectively. The rise in consumer preference for online shopping and the increasing number of internet users globally also play a significant role in the market's expansion. As more businesses aim to enhance their digital footprint, the demand for advanced paid search solutions is expected to grow robustly.

One of the pivotal growth factors driving the paid search software market is the aggressive expansion of e-commerce platforms and the general shift of consumer behavior towards online shopping. The pandemic has further accelerated the trend of shopping online, which in turn has increased the demand for marketing tools that can effectively capture consumer interest in the digital space. Paid search software enables businesses to manage their search engine advertising campaigns more efficiently, providing insights and analytics that help in understanding consumer behavior and optimizing marketing strategies. The efficiency and effectiveness of these tools are compelling more businesses, from small and medium enterprises to large corporations, to invest in paid search solutions, thereby fostering market growth.

Another significant driver is the growing complexity and competitiveness of the digital advertising landscape, which necessitates sophisticated tools for effective campaign management. Businesses are increasingly seeking out paid search software that offers advanced features such as keyword research, bid management, and real-time analytics to gain a competitive edge. These tools help organizations to strategically position their ads, manage advertising budgets efficiently, and achieve higher ROI on their marketing campaigns. As the competition for online visibility intensifies, the demand for advanced paid search solutions is expected to surge, contributing to market growth during the forecast period.

The technological advancements in artificial intelligence and machine learning are also catalyzing the growth of the paid search software market. AI-powered features in paid search software, such as automated bid strategies, predictive analytics, and personalized ad targeting, are providing marketers with enhanced capabilities to optimize their digital advertising efforts. These technologies help in refining ad content, predicting customer preferences, and delivering personalized experiences, thereby improving the effectiveness of marketing campaigns. As AI and ML technologies continue to evolve, their integration into paid search software is anticipated to drive further market expansion.

Regionally, North America held the largest share of the paid search software market in 2023, driven by the high adoption of digital marketing strategies by businesses in the region. The presence of key market players and the rapid technological advancements in the United States are also contributing to market dominance. Meanwhile, the Asia Pacific region is expected to witness the fastest growth rate during the forecast period, owing to the rapid digital transformation of businesses, increased internet penetration, and the booming e-commerce sector in countries like China and India. Europe, with its strong economic base and increasing focus on digitalization, also presents significant growth opportunities for the market. Latin America and the Middle East & Africa are gradually embracing digital marketing strategies, which may offer potential growth avenues in the coming years.

Component Analysis

The paid search software market is segmented by component into software and services, both playing crucial roles in enabling organizations to harness the power of digital advertising. Software solutions are at the heart of this market, providing the necessary tools for managing and optimizing paid search campaigns. These software solutions range from basic keyword research tools to advanced platforms offering comprehensive features, including bid management, ad tracking, and conversion optimization. The increasing complexity of digital marketing campaigns and the need for data-driven decision making are driving the demand for sophisticated sof

The enterprise search market, valued at approximately $11.12 billion in 2025, is experiencing robust growth fueled by the increasing need for efficient information retrieval within organizations. The market is segmented by deployment type (local installations, hosted versions, search appliances) and application (government & commercial offices, banking & finance, healthcare, retail, and others). The strong growth is driven by several factors including the explosive growth of data volumes, the rising demand for improved employee productivity, and the increasing need for enhanced data security and compliance. Hosted versions are gaining traction due to their scalability and cost-effectiveness, while the healthcare and banking sectors are significant adopters due to their stringent data management requirements. Competitive pressures from established players like IBM, Microsoft, and Google, alongside emerging niche players focusing on specific application areas, are shaping the market landscape. We anticipate a consistent Compound Annual Growth Rate (CAGR) of approximately 15% (a reasonable estimate given the market dynamics) throughout the forecast period (2025-2033), driven by ongoing digital transformation initiatives and increasing adoption of AI-powered search capabilities. Technological advancements, such as the integration of artificial intelligence (AI) and machine learning (ML) into enterprise search solutions, are creating new opportunities for enhanced search accuracy and personalized results. This trend is likely to accelerate adoption, particularly in industries with large unstructured data sets. However, challenges remain, including the complexities of integrating diverse data sources, ensuring data security and privacy, and the need for ongoing training and support to maximize user adoption. Despite these hurdles, the long-term outlook for the enterprise search market remains positive, driven by the ongoing need for efficient and effective information access within increasingly complex organizational environments. The market is expected to surpass $30 billion by 2033, representing significant growth and investment opportunities.

The number of Google searches for jeans from Gucci in the United States reached approximately **** million in the year from May 2022 to April 2023. This was a decrease of more than four percent from the same period in 2020 to 2021.

According to an April 2024 survey, one-quarter of adults in the United States preferred to use social media as their primary choice for online search. Within different generations, Gen Z showed the highest interest in using social platforms over search engines to find information online, with around 46 percent of those respondents stating they preferred this method.