Google Analytics data for the Queensland Government website (qld.gov.au) (Date range: 1 July 2017 to 30 June 2018)

The present dataset is generated in the frame of the Horizon 2020 project "InnORBIT: Empowering innovation intermediaries to generate sustainable initiatives to accelerate the commercialisation of space innovation" (innorbit.eu). This dataset describes the InnORBIT project's dissemination and communication plan and also includes the data collected from dissemination and communication activities to measure the progress against the project's targets for outreach during the first 18 months of project implementation (January 1st, 2021 - June 30th, 2022). This dataset will be updated in a second and final version, after the end of InnORBIT's grant duration in July 2023. The final version will provide a full dataset accounting for the project's outreach activities. This first version of the dataset contains the following files and documents: [InnORBIT-DisseminationCommunicationPlan_v2_20220929.pdf]: Final version of the project's Dissemination, Awareness raising and Communication Plan (DACP), that describes the key target audiences, key messages and value offered by InnORBIT through in terms of knowledge, services and solutions boosting entrepreneurship in the space industry and the digital tools offered via the InnORBIT digital toolbox. The InnORBIT DACP also describes the channels, tools and activities employed to reach out effectively the project's target groups. The core Key Performance Indicators (KPIs) that indicate the performance level of the project's strategy and indicates areas for improvement are outlined. The updated version also outlines the achievements of the project's dissemination for the first 18 months of implementation (January 2021 - June 2022). [InnORBIT_DisseminationActivities_Data_20220929. xlsx]: A spreadsheet used to collect raw data about the project's dissemination activities, calculate the InnORBIT's KPIs for Dissemination and Communication to track progress against targets. The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceOverview_20220929.pdf]: A Google Analytics report summarising InnORBIT website's audience demographics and overall page performance (visits, sessions, users). The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceAcquisition_20220929.pdf]: A Google Analytics report summarising the main sources generating traffic for the InnORBIT website and the bahaviour of users coming from each source. The data span from January 1st, 2021 to June 30th, 2022. [InnORBIT-WebsiteAnalytics-AudienceBehaviour_20220929.pdf]: A Google Analytics report providing further insight on users' behaviour when using the InnORBIT website. The data span from January 1st, 2021 to June 30th, 2022.

The app analytics market is experiencing robust growth, driven by the increasing adoption of mobile applications across various sectors and the need for businesses to understand user behavior and optimize app performance. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This growth is fueled by several key trends, including the rising demand for personalized user experiences, the proliferation of mobile-first strategies, and the increasing sophistication of analytics tools offering deeper insights into app usage, user engagement, and monetization strategies. Key players like Google, Adobe, Amazon Web Services, and others are continuously innovating to provide advanced features such as predictive analytics, real-time dashboards, and integration with other marketing technologies. Furthermore, the market is segmented by various factors, including deployment mode (cloud-based, on-premise), application type (gaming, social media, e-commerce), and enterprise size, allowing for targeted solutions and specialized analytics. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and competitive pricing. Despite the positive outlook, the market faces certain restraints, such as the increasing cost of implementing and maintaining advanced analytics solutions and the need for skilled professionals to interpret and leverage the generated data effectively. Data privacy concerns and the increasing complexity of app analytics tools also pose challenges. However, the overall market trajectory is positive, with continuous innovation driving market expansion and adoption across diverse industries. The growth trajectory indicates a significant opportunity for businesses to leverage app analytics for better decision-making, enhanced user experiences, and increased profitability. The anticipated market size in 2033 is projected to exceed $50 billion, reflecting the continued dominance and evolution of mobile applications and the crucial role of analytics in their success.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2017-01_RAMP_all.csv. Using this example, the file 2017-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2017.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

The streaming analytics tools market is experiencing robust growth, driven by the increasing volume of real-time data generated across various industries. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This surge is fueled by the need for businesses to derive actionable insights from streaming data for improved decision-making and operational efficiency. Key drivers include the rise of IoT devices, the proliferation of cloud computing, and the growing demand for real-time business intelligence across sectors like finance, healthcare, and manufacturing. The market is segmented by application (SMEs and large enterprises) and type (cloud-based and on-premises solutions). Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of deployment. Large enterprises are currently the dominant segment, but the SME segment is expected to witness substantial growth as these businesses increasingly adopt data-driven strategies. Competitive landscape is highly fragmented, with major players including established tech giants like Amazon, Google, Microsoft, and IBM, alongside specialized vendors such as RapidMiner, Memgraph, and StreamSQL. Geographic distribution shows strong concentration in North America and Europe, but Asia Pacific is emerging as a key growth region driven by rapid digital transformation initiatives in countries like India and China. The market faces certain restraints, including the complexity of implementing streaming analytics solutions, data security concerns, and the need for skilled professionals to manage these systems. However, these challenges are being addressed through advancements in technology and the increasing availability of training resources. The forecast period (2025-2033) anticipates continuous market expansion, primarily driven by the wider adoption of advanced analytics techniques and the increasing need for real-time insights across various sectors. The shift towards cloud-based solutions will continue to drive market growth. Growth in specific regions, particularly in the Asia Pacific region, promises to significantly contribute to overall market expansion. Continued innovation within the space will lead to new and more refined offerings that cater to a broader range of user needs, further bolstering the market's trajectory. The competitive landscape will remain dynamic, with ongoing innovation, mergers and acquisitions shaping the future market players. The successful deployment of streaming analytics solutions, however, will be predicated on organizations effectively addressing data security and addressing the skill gap in data science and engineering.

Contains Gallup data from countries that are home to more than 98% of the world's population through a state-of-the-art Web-based portal. Gallup Analytics puts Gallup's best global intelligence in users' hands to help them better understand the strengths and challenges of the world's countries and regions. Users can access Gallup's U.S. Daily tracking and World Poll data to compare residents' responses region by region and nation by nation to questions on topics such as economic conditions, government and business, health and wellbeing, infrastructure, and education.

The Gallup Analytics Database is accessed through the Cornell University Libraries here. In addition, a CUL subscription also allows access to the Gallup Respondent Level Data. For access please refer to the documentation below and then request the variables you need here.

Before requesting data from the World Poll, please see the Getting Started guide and the Worldwide Research Methodology and Codebook (You will need to request access). The Codebook will give you information about all available variables in the datasets. There are other guides available as well in the google folder. You can also access information about questions asked and variables using the Gallup World Poll Reference Tool. You will need to create your user account to access the tool. This will only give you access to information about the questions asked and variables. It will not give you access to the data.

For further documentation and information see this site from New York University Libraries. The Gallup documentation for the World Poll methodology is also available under the Data and Documentation tab.

In addition to the World Poll and Daily Tracking Poll, also available are the Gallup Covid-19 Survey, Gallup Poll Social Series Surveys, Race Relations Survey, Confidence in Institutions Survey, Honesty and Ethics in Professions Survey, and Religion Battery.

The process for getting access to respondent-level data from the Gallup U.S. Daily Tracking is similar to the World Poll Survey. There is no comparable discovery tool for U.S. Daily Tracking poll questions, however. Users need to consult the codebooks and available variables across years.

The COVID-19 web survey began on March 13, 2020 with daily random samples of U.S. adults, aged 18 and older who are members of the Gallup Panel. Before requesting data, please see the Gallup Panel COVID-19 Survey Methodology and Codebook.

The Gallup Poll Social Series (GPSS) dataset is a set of public opinion surveys designed to monitor U.S. adults’ views on numerous social, economic, and political topics. More information is available on the Gallup website: https://www.gallup.com/175307/gallup-poll-social-series-methodology.aspx As each month has a unique codebook, contact CCSS-ResearchSupport@cornell.edu to discuss your interests and start the data request process.

Starting in 1973, Gallup started measuring the confidence level in several US institutions like Congress, Presidency, Supreme Court, Police, etc. The included dataset includes data beginning in 1973 and data is collected once per year. Users should consult the list of available variables.

The Race Relations Poll includes topics that were previously represented in the GPSS Minority Relations Survey that ran through 2016. The Race Relations Survey was conducted November 2018. Users should consult the codebook for this poll before making their request.

The Honesty and Ethics in Professions Survey – Starting in 1976, Gallup started measuring US perceptions of the honesty and ethics of a list of professions. The included dataset was added to the collection in March 2023 and includes data ranging from 1976-2022. Documentation for this collection is located here and will require you to request access.

Religion Battery: Consolidated list of items focused on religion in the US from 1999-2022. Documentation for this collection is located here and will require you to request access.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

The AI and Big Data Analytics market within the telecommunications sector is experiencing robust growth, driven by the increasing need for network optimization, personalized customer experiences, and advanced fraud detection. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $60 billion by 2033. This expansion is fueled by several key factors. Firstly, the exponential growth of data generated by 5G networks and IoT devices necessitates sophisticated analytical tools to manage and extract value. Secondly, telecom operators are increasingly adopting AI-powered solutions for predictive maintenance of network infrastructure, resulting in significant cost savings and improved service reliability. Thirdly, personalized marketing campaigns driven by AI-powered customer segmentation and predictive analytics are boosting customer engagement and revenue generation. Finally, the rising threat of fraud and security breaches is driving demand for AI-based security systems capable of detecting and mitigating these threats in real-time. The market is segmented by application (private vs. commercial) and deployment type (cloud-based vs. on-premise), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. Major players like AWS, Google, and IBM are actively shaping the market landscape through strategic partnerships and continuous innovation, while numerous smaller specialized firms cater to specific needs within the sector. Geographic distribution shows strong growth across North America and Asia-Pacific, reflecting high technological adoption and expanding digital infrastructure in these regions. The competitive landscape is characterized by both large technology companies offering comprehensive solutions and specialized niche players focusing on specific segments within the telecom industry. While the rapid adoption of cloud-based solutions presents opportunities for growth, challenges remain, including data privacy concerns, the need for skilled professionals to implement and manage these systems, and the high initial investment costs associated with AI and big data infrastructure. Despite these challenges, the long-term outlook for the AI and Big Data Analytics market in telecommunications remains extremely positive, driven by ongoing technological advancements and the increasing reliance of telecom operators on data-driven decision-making to enhance operational efficiency and improve customer satisfaction. The market's evolution will be further influenced by the development of 6G technologies and the expansion of the Internet of Things (IoT), which will generate even larger volumes of data requiring sophisticated AI and big data analytics for effective management and analysis.

The global Supply Chain Big Data Analytics market is experiencing robust growth, driven by the increasing need for enhanced visibility, efficiency, and predictive capabilities across complex supply chains. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors. The proliferation of IoT devices generates massive volumes of data offering rich insights into operational inefficiencies, demand forecasting accuracy, and risk mitigation strategies. Furthermore, advancements in cloud computing, machine learning, and artificial intelligence are enabling sophisticated analytics solutions that translate this data into actionable intelligence. Businesses are increasingly adopting these solutions to optimize inventory management, improve logistics, reduce costs, and enhance customer satisfaction. The rising adoption of predictive analytics and real-time data visualization tools further accelerates market growth, helping businesses proactively adapt to disruptions and make data-driven decisions. The market's segmentation reflects this diverse application. While specific segment breakdowns are unavailable, it's reasonable to assume significant segments based on deployment (cloud, on-premise), industry (retail, manufacturing, healthcare), and analytics type (descriptive, predictive, prescriptive). Key players like Accenture, IBM, Google, and SAP are investing heavily in research and development, fostering competition and innovation within the market. Despite the strong growth trajectory, challenges remain, including data security concerns, the complexity of integrating disparate data sources, and the need for skilled professionals capable of interpreting and utilizing complex analytical outputs. However, these challenges are unlikely to significantly hamper the overall market expansion given the compelling advantages of leveraging big data analytics for supply chain optimization.

The streaming data processing solutions market is experiencing robust growth, driven by the increasing volume of real-time data generated across various industries. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $50 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of IoT (Internet of Things) devices and the proliferation of big data are generating immense volumes of unstructured data streams that require real-time processing for actionable insights. Secondly, the growing adoption of cloud-based solutions offers scalability and cost-effectiveness, appealing to both SMEs and large enterprises. Furthermore, advancements in technologies such as machine learning and AI are enhancing the capabilities of streaming processing platforms, enabling advanced analytics and real-time decision-making. The demand for real-time operational intelligence across sectors like finance, healthcare, and manufacturing is another major driver. The market segmentation highlights a preference for cloud-based solutions over on-premise deployments, reflecting the trend towards agility and reduced infrastructure management. Large enterprises are currently the dominant segment, but SMEs are exhibiting increasing adoption rates, driven by the availability of cost-effective and easy-to-use cloud-based platforms. While the market faces challenges such as data security concerns and the complexity of managing real-time data streams, ongoing technological innovations and increased awareness of the benefits of real-time analytics are expected to mitigate these restraints and sustain the market's impressive growth trajectory. Key players, including established technology giants like Google, Amazon, and Microsoft, alongside specialized vendors like Memgraph and Upsolver, are actively innovating and competing to capture market share in this dynamic landscape. Geographical analysis reveals strong growth potential in Asia-Pacific and North America, fueled by robust technological adoption and increasing digital transformation initiatives.

The global text analytics tool market is experiencing robust growth, driven by the increasing volume of unstructured text data generated across various industries and the rising need for extracting actionable insights. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $50 billion by 2033. This growth is fueled by several key factors. The proliferation of social media, e-commerce platforms, and customer service interactions produces vast amounts of textual data. Businesses are increasingly leveraging text analytics tools to gain competitive advantages through sentiment analysis, topic modeling, and trend identification. Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and accessibility, while the demand for on-premises solutions remains significant among enterprises with stringent data security requirements. Furthermore, the increasing adoption of artificial intelligence (AI) and machine learning (ML) algorithms is enhancing the accuracy and efficiency of text analytics tools. Large enterprises are leading the adoption due to their extensive data volumes and analytical needs, while SMEs are increasingly adopting these tools to improve customer engagement and operational efficiency. However, challenges like data privacy concerns, the need for skilled professionals, and the complexity of integrating text analytics into existing systems pose restraints to market growth. The market is segmented by deployment (cloud-based and on-premises) and application (SMEs and large enterprises). Cloud-based solutions dominate due to their flexibility and scalability, offering a compelling value proposition for both SMEs and large enterprises. Geographically, North America holds a substantial market share, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness significant growth in the coming years, fueled by rapid digitalization and economic expansion across key regions like China and India. Europe also presents a significant market opportunity with strong growth potential across various sectors, especially in countries like the UK and Germany. The competitive landscape is characterized by a mix of established players like IBM, Google, and Microsoft, as well as specialized text analytics vendors. Continuous innovation, strategic partnerships, and mergers and acquisitions are shaping the market dynamics. The future growth trajectory of the text analytics tool market remains highly promising, driven by technological advancements and evolving business needs for extracting valuable insights from textual data.

The global market for stream data pipeline processing tools is experiencing robust growth, driven by the increasing volume and velocity of data generated across diverse industries. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant growth is fueled by several key factors: the rising adoption of cloud-native architectures, the proliferation of real-time analytics applications (particularly in finance and security), and the increasing need for efficient and scalable data processing solutions to handle the ever-growing data streams from IoT devices, social media, and other sources. The demand for real-time insights is a major driver, pushing organizations to adopt tools capable of processing and analyzing data instantly, rather than relying on batch processing methods. Further, the continued expansion of cloud computing and the availability of sophisticated, managed services are simplifying implementation and reducing the total cost of ownership for these tools. The market is segmented by tool type (real-time, proprietary, and cloud-native) and application (finance and security, with other sectors like healthcare and logistics also showing increasing adoption). While North America currently holds a dominant market share, fueled by early adoption and a strong technology ecosystem, regions like Asia-Pacific are experiencing rapid growth due to increasing digitalization and investment in data infrastructure. However, factors such as the complexity of implementation, the need for skilled personnel, and data security concerns pose challenges to market expansion. The competitive landscape is highly fragmented, with a mix of established players like Google, IBM, and Microsoft, alongside emerging niche providers. The ongoing innovation in areas such as AI-powered data processing, serverless architectures, and enhanced security features will continue to shape the market landscape in the coming years.

The Big Data and Machine Learning (BDML) in Telecom market is experiencing robust growth, driven by the increasing volume of telecom data, the need for enhanced network optimization, and the demand for personalized customer experiences. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the proliferation of 5G networks generates exponentially more data, necessitating advanced analytics for efficient management and monetization. Secondly, the rise of IoT devices and the growing adoption of cloud-based infrastructure further contribute to data explosion, creating opportunities for BDML solutions to extract valuable insights. Finally, telecom companies are increasingly leveraging BDML to enhance customer service, personalize marketing campaigns, predict network failures, and optimize resource allocation, leading to improved operational efficiency and increased revenue streams. The market segmentation reveals strong growth across various analytics types (Descriptive, Predictive, Machine Learning, Feature Engineering) and applications (Processing, Storage, Analyzing). While Machine Learning solutions are currently driving significant demand, the adoption of Feature Engineering techniques is expected to increase rapidly in the coming years due to its capability to improve the accuracy and efficiency of machine learning models. Geographically, North America and Europe currently dominate the market share due to early adoption and advanced technological infrastructure. However, the Asia-Pacific region is projected to witness the fastest growth, primarily fueled by the rapid expansion of 5G networks and increasing digitalization in countries like India and China. This growth trajectory is tempered by challenges such as data security concerns, the need for skilled professionals, and the high initial investment costs associated with implementing BDML solutions. Nevertheless, the overall market outlook remains positive, with continued innovation and increasing industry adoption anticipated to drive sustained growth throughout the forecast period.

The Enterprise Data Monetization Platform market is experiencing robust growth, driven by the increasing need for organizations to leverage their data assets for revenue generation and competitive advantage. The market is projected to be valued at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the growing demand for advanced analytics and AI-driven insights, and the increasing regulatory focus on data privacy and security. Businesses are increasingly recognizing the potential of their data to create new revenue streams through personalized services, targeted advertising, and data-driven product development. Furthermore, the emergence of innovative data monetization strategies, such as data marketplaces and data-as-a-service models, is further accelerating market growth. However, challenges remain. Data security and privacy concerns continue to be significant hurdles, requiring robust security measures and compliance with regulations like GDPR and CCPA. The complexity of data integration and management, along with the need for skilled professionals to effectively monetize data, also pose barriers to entry for some organizations. Despite these challenges, the long-term outlook for the Enterprise Data Monetization Platform market remains positive, with continued technological advancements and evolving business models expected to drive further expansion in the coming years. Major players like Microsoft, Google, and Salesforce are heavily investing in this space, indicating its strategic importance within the broader technology landscape.

The real-time data transfer service market is experiencing robust growth, driven by the increasing need for immediate data access across diverse sectors. The market's expansion is fueled by the proliferation of IoT devices, the rise of big data analytics, and the demand for real-time decision-making in industries like healthcare, manufacturing, and finance. Cloud-based solutions are gaining significant traction, offering scalability and cost-effectiveness compared to local deployments. The global market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This strong growth is projected to continue, driven by advancements in 5G technology, edge computing, and the growing adoption of AI and machine learning, all of which demand high-speed, low-latency data transfer capabilities. The North American market currently holds a substantial share, but regions like Asia-Pacific are witnessing rapid growth, fueled by increasing digitalization and infrastructure development. Challenges remain, however, including data security concerns, regulatory compliance, and the need for robust network infrastructure to support real-time data transmission. The diverse applications across various industries ensure the market's continued expansion and evolution in the coming years. The competitive landscape is characterized by a mix of established technology giants (IBM, Amazon Web Services, Microsoft, Google) and specialized providers catering to niche applications. These companies are constantly innovating to offer enhanced security features, improved data compression techniques, and increased integration with existing business workflows. The market is also seeing the emergence of specialized solutions targeting specific sectors, such as healthcare and financial services, where real-time data accuracy and security are paramount. Strategic partnerships and mergers and acquisitions are likely to shape the market dynamics in the coming years, consolidating market leadership and accelerating innovation. As data volumes continue to grow exponentially, the demand for efficient and reliable real-time data transfer services will only increase, ensuring continued market expansion and opportunity for players in this rapidly evolving space.

Data Set Information:

This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.

Attribute Information:

Attribute 1 : Unique user id Attribute 2 : Average ratings on churches Attribute 3 : Average ratings on resorts Attribute 4 : Average ratings on beaches Attribute 5 : Average ratings on parks Attribute 6 : Average ratings on theatres Attribute 7 : Average ratings on museums Attribute 8 : Average ratings on malls Attribute 9 : Average ratings on zoo Attribute 10 : Average ratings on restaurants Attribute 11 : Average ratings on pubs/bars Attribute 12 : Average ratings on local services Attribute 13 : Average ratings on burger/pizza shops Attribute 14 : Average ratings on hotels/other lodgings Attribute 15 : Average ratings on juice bars Attribute 16 : Average ratings on art galleries Attribute 17 : Average ratings on dance clubs Attribute 18 : Average ratings on swimming pools Attribute 19 : Average ratings on gyms Attribute 20 : Average ratings on bakeries Attribute 21 : Average ratings on beauty & spas Attribute 22 : Average ratings on cafes Attribute 23 : Average ratings on view points Attribute 24 : Average ratings on monuments Attribute 25 : Average ratings on gardens