Singapore Internet Usage: Search Engine Market Share: Tablet: Seznam data was reported at 0.000 % in 12 Aug 2024. This stayed constant from the previous number of 0.000 % for 11 Aug 2024. Singapore Internet Usage: Search Engine Market Share: Tablet: Seznam data is updated daily, averaging 0.000 % from Mar 2024 (Median) to 12 Aug 2024, with 27 observations. The data reached an all-time high of 0.130 % in 06 Mar 2024 and a record low of 0.000 % in 12 Aug 2024. Singapore Internet Usage: Search Engine Market Share: Tablet: Seznam data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Singapore – Table SG.SC.IU: Internet Usage: Search Engine Market Share.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

Next Generation Search Engines Market Outlook

According to our latest research, the global Next Generation Search Engines market size reached USD 16.2 billion in 2024, with a robust year-on-year growth driven by rapid technological advancements and escalating demand for intelligent search solutions across industries. The market is expected to witness a CAGR of 18.7% during the forecast period from 2025 to 2033, propelling the market to a projected value of USD 82.3 billion by 2033. The accelerating adoption of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) within search technologies is a key growth factor, as organizations seek more accurate, context-aware, and personalized information retrieval solutions.

One of the most significant growth drivers for the Next Generation Search Engines market is the exponential increase in digital content and data generation worldwide. Enterprises and consumers alike are producing vast amounts of unstructured data daily, from documents and emails to social media posts and multimedia files. Traditional search engines often struggle to deliver relevant results from such complex datasets. Next generation search engines, powered by AI and ML algorithms, are uniquely positioned to address this challenge by providing semantic understanding, contextual relevance, and intent-driven results. This capability is especially critical for industries like healthcare, BFSI, and e-commerce, where timely and precise information retrieval can directly impact decision-making, operational efficiency, and customer satisfaction.

Another major factor fueling the growth of the Next Generation Search Engines market is the proliferation of mobile devices and the evolution of user interaction paradigms. As consumers increasingly rely on smartphones, tablets, and voice assistants, there is a growing demand for search solutions that support voice and visual queries, in addition to traditional text-based searches. Technologies such as voice search and visual search are gaining traction, enabling users to interact with search engines more naturally and intuitively. This shift is prompting enterprises to invest in advanced search platforms that can seamlessly integrate with diverse devices and channels, enhancing user engagement and accessibility. The integration of NLP further empowers these platforms to understand complex queries, colloquial language, and regional dialects, making search experiences more inclusive and effective.

Furthermore, the rise of enterprise digital transformation initiatives is accelerating the adoption of next generation search technologies across various sectors. Organizations are increasingly seeking to unlock the value of their internal data assets by deploying enterprise search solutions that can index, analyze, and retrieve information from multiple sources, including databases, intranets, cloud storage, and third-party applications. These advanced search engines not only improve knowledge management and collaboration but also support compliance, security, and data governance requirements. As businesses continue to embrace hybrid and remote work models, the need for efficient, secure, and scalable search capabilities becomes even more pronounced, driving sustained investment in this market.

Regionally, North America currently dominates the Next Generation Search Engines market, owing to the early adoption of AI-driven technologies, strong presence of leading technology vendors, and high digital literacy rates. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, expanding internet penetration, and increasing investments in AI research and development. Europe is also witnessing steady growth, supported by robust regulatory frameworks and growing demand for advanced search solutions in sectors such as BFSI, healthcare, and education. Latin America and the Middle East & Africa are gradually catching up, as enterprises in these regions recognize the value of next generation search engines in enhancing operational efficiency and customer experience.

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.

AG's News Topic Classification Dataset

ORIGIN

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

DESCRIPTION

The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.

The file classes.txt contains a list of classes corresponding to each label.

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ".

The confluence of Search and Recommendation (S&R) services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within the academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in this field. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 300 million daily active users. Previous research in this field has predominantly employed publicly available datasets that are semi-synthetic and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users’ transitions between the two services. This work aids in joint modeling of S&R, and the utilization of search data for recommenders (and recommendation data for search engines). Additionally, due to the diverse feedback labels of user-video interactions, KuaiSAR also supports a wide range of other tasks, including intent recommendation, multi-task learning, and long sequential multi-behavior modeling etc. We believe this dataset will facilitate innovative research and enrich our understanding of S&R services integration in real-world applications.

This dataset features over 1,000,000 high-quality images of cars, sourced globally from photographers, enthusiasts, and automotive content creators. Optimized for AI and machine learning applications, it provides richly annotated and visually diverse automotive imagery suitable for a wide array of use cases in mobility, computer vision, and retail.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and detailed annotations such as car make, model, year, body type, view angle (front, rear, side, interior), and condition (e.g., showroom, on-road, vintage, damaged). Ideal for training in classification, detection, OCR for license plates, and damage assessment.

1. Unique Sourcing Capabilities: the dataset is built from images submitted through a proprietary gamified photography platform with auto-themed competitions. Custom datasets can be delivered within 72 hours targeting specific brands, regions, lighting conditions, or functional contexts (e.g., race cars, commercial vehicles, taxis).

2. Global Diversity: contributors from over 100 countries ensure broad coverage of car types, manufacturing regions, driving orientations, and environmental settings—from luxury sedans in urban Europe to pickups in rural America and tuk-tuks in Southeast Asia.

3. High-Quality Imagery: images range from standard to ultra-HD and include professional-grade automotive photography, dealership shots, roadside captures, and street-level scenes. A mix of static and dynamic compositions supports diverse model training.

4. Popularity Scores: each image includes a popularity score derived from GuruShots competition performance, offering valuable signals for consumer appeal, aesthetic evaluation, and trend modeling.

5. AI-Ready Design: this dataset is structured for use in applications like vehicle detection, make/model recognition, automated insurance assessment, smart parking systems, and visual search. It’s compatible with all major ML frameworks and edge-device deployments.

6. Licensing & Compliance: fully compliant with privacy and automotive content use standards, offering transparent and flexible licensing for commercial and academic use.

Use Cases: 1. Training AI for vehicle recognition in smart city, surveillance, and autonomous driving systems. 2. Powering car search engines, automotive e-commerce platforms, and dealership inventory tools. 3. Supporting damage detection, condition grading, and automated insurance workflows. 4. Enhancing mobility research, traffic analytics, and vision-based safety systems.

This dataset delivers a large-scale, high-fidelity foundation for AI innovation in transportation, automotive tech, and intelligent infrastructure. Custom dataset curation and region-specific filters are available. Contact us to learn more!

Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.

Description of the Report and Topic Justification

Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.

In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).

One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.

In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.

Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
- For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015). - McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013). - Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979). -Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and Shirkey, 1974). Both descriptive statistics and reliability analyses were performed via JASP 0.14.1.0 software.

To this end, this report contributes to the knowledge expansion of all the interest parties and stakeholders related to the research topic of improving the visibility and findability of LAMs and their content on the Web. It constitutes a well-informed compass, that could be adopted by such organizations, in order to implement potential strategies that combine both domain knowledge and data-driven culture in terms of awareness optimization on the internet realm.

The Research Team Behind the Project

The whole project is managed and optimized on a weekly basis by a big young and smiley team of scientists (alphabetically referred in the next section). All of them are undergraduate students at the Department of Archival, Library and Information Studies of the University of West Attica.

They are responsible for the overall process of publishing the Technical Report which includes the initial organizations’ identification, and subsequently, websites testing, data gathering, curation and pre-processing, analysis, validation and visualization. Of course, the Team will continue to expand the capabilities of this report while involving new features, metrics, and further information regarding Libraries, Archives and Museums websites from all over the world.

Notice: includes a plurality of technical and behavioral factors and variables concerning the examined information organizations' websites. Potentially, more features will be included on the next versions.

Report Version 0.9.6 Correspondence: Ioannis C. Drivas (PhDc) idrivas@uniwa.gr | http://users.uniwa.gr/idrivas/ Research Lab of Information Management Department of Archival, Library Science and Information Studies University of West Attica.

ObjectivesTo systematically review recidivism rates internationally, report whether they are comparable and, on the basis of this, develop best reporting guidelines for recidivism.MethodsWe searched MEDLINE, Google Web, and Google Scholar search engines for recidivism rates around the world, using both non-country-specific searches as well as targeted searches for the 20 countries with the largest total prison populations worldwide.ResultsWe identified recidivism data for 18 countries. Of the 20 countries with the largest prison populations, only 2 reported repeat offending rates. The most commonly reported outcome was 2-year reconviction rates in prisoners. Sample selection and definitions of recidivism varied widely, and few countries were comparable.ConclusionsRecidivism data are currently not valid for international comparisons. Justice Departments should consider using the reporting guidelines developed in this paper to report their data.

This project updates INTERNATIONAL MILITARY INTERVENTION (IMI), 1946-1988 (ICPSR 6035), compiled by Frederic S. Pearson and Robert A. Baumann (1993). This newer study documents 447 intervention events from 1989 to 2005. To ensure consistency across the full 1946-2005 time span, Pearson and Baumann's coding procedures were followed. The data collection thus "documents all cases of military intervention across international boundaries by regular armed forces of independent states" in the international system (Pearson and Baumann, 1993). "Military interventions are defined operationally in this collection as the movement of regular troops or forces (airborne, seaborne, shelling, etc.) of one country inside another, in the context of some political issue or dispute" (Pearson and Baumann, 1993). As with the original IMI (OIMI) collection, the 1989-2005 dataset includes information on actor and target states, as well as starting and ending dates. It also includes a categorical variable describing the direction of the intervention, i.e., whether it was launched in support of the target government, in opposition to the target government, or against some third party actor within the target state's borders. The intensity of the military intervention is captured in ordinal variables that document the scale of the actor's involvement, "ranging from minor engagement such as evacuation, to patrols, act of intimidation, and actual firing, shelling or bombing" (Pearson and Baumann, 1993). Casualties that are a direct result of the military intervention are coded as well. A novel aspect of IMI is the inclusion of a series of variables designed to ascertain the motivations or issues that prompted the actor to intervene, including to take sides in a domestic dispute in the target state, to affect target state policy, to protect a socio-ethnic or minority group, to attack rebels in sanctuaries in the target state, to protect economic or resource interests, to intervene for strategic purposes, to lend humanitarian aid, to acquire territory or to dispute its ownership, and to protect its own military/diplomatic interests. There are three main differences between OIMI and this update. First, the variable, civilian casualties, which complements IMI's information on the casualties suffered by actor and target military personnel has been added. Second, OIMI variables on colonial history, previous intervention, alliance partners, alignment of the target, power size of the intervener, and power size of the target have been deleted. The Web-based resources available today, such as the CIA World Fact Book, make information on the colonial history between actor and target readily available. Statistical programs allow researchers to generate all previous interventions by the actor into the target state. Since competing measures and data collections are used for alliances and state power, it was thought best to allow analysts who use IMI the freedom to choose the variables or dataset that measure the phenomena of their choice. Third, the data collection techniques differ from OIMI. OIMI relied on the scouring of printed news sources such as the New York Times Index, Facts on File, and Keesing's to collect information on international military interventions, whereas the computer-based search engine, Lexis-Nexis Academic, was used as the foundation for the new study's data search. Lexis-Nexis Academic includes print sources as well as news wire reports and many others. After Lexis-Nexis searches were conducted for each year in the update by at least four different investigators, regional sources, the United Nations Web site, and secondary works were consulted.

We sampled Google Earth aerial images to get a representative and globally distributed dataset of treeline locations. Google Earth images are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.

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Doing so, we scaled Google Earth’s GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth’s surface. Within this buffer zone, we took coordinates and elevation of the highest realized treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline. We did not apply an automated image processing approach. We calculated mass elevation effect as the distance to t..., The file global-treeline-data.csv contains the whole data set. Please find further information about the data set in the README.md. Please download both files and load the .csv file into your stats software, e.g. R., The global-treeline-data.csv file can be opened with several software options, e.g. R, LibreOffice or any simple editor.