Snapshot img

Live Streaming Market Size and Forecast 2025-2029

The live streaming market size estimates the market to reach by USD 20.64 billion, at a CAGR of 16.6% between 2024 and 2029. APAC is expected to account for 50% of the growth contribution to the global market during this period. In 2019 the platform segment was valued at USD 7.96 billion and has demonstrated steady growth since then.

Market Size & Forecast

  Market Opportunities: USD 0.310 Billion
  Future Opportunities: USD 20.64 Billion 
  CAGR : 16.6%
 North America: Largest market in 2023

The market is experiencing significant growth, driven by the increasing penetration of smartphones and easy access to the internet. This trend is particularly prominent in the consumer sector, where users seek real-time engagement and immersive experiences. The integration of advanced technologies, such as artificial intelligence and virtual reality, with online streaming services further enhances the market's potential. However, the market also faces challenges, including growing privacy regulations and security concerns. Companies must navigate these obstacles by implementing robust security measures and adhering to regulatory frameworks to maintain user trust and comply with evolving data protection requirements. To capitalize on market opportunities and effectively address challenges, businesses must stay informed of technological advancements and regulatory developments, while prioritizing user experience and data security.

What will be the Size of the Live Streaming Market during the forecast period?

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The market continues to evolve, with viewer experience emerging as a key differentiator for broadcasters. Low-latency streaming and mobile streaming have become essential for engaging audiences on-the-go. Integrating live chat and stream recording solutions further enhances the viewer experience, enabling real-time interaction and post-event replay. The market's dynamism is evident in the adoption of advanced technologies such as 4k live streaming, secure streaming protocols, and HDR live streaming. Esports streaming and VR live streaming are also gaining traction, pushing the boundaries of audience engagement. Industry growth is expected to reach double digits, with bandwidth optimization and video player technology playing crucial roles in delivering high-definition streaming.

Cloud-based streaming, video encoding formats, and video transcoding pipelines are streamlining production workflows, enabling remote production and broadcast automation. An example of this market's continuous unfolding can be seen in a major broadcaster's shift to adaptive bitrate streaming, resulting in a 30% increase in viewer retention during live events. The implementation of digital rights management and streaming infrastructure further ensures secure and monetized content delivery. In conclusion, the market is a vibrant and ever-changing landscape, with ongoing innovations in technology, viewer experience, and monetization strategies shaping its future.

How is this Live Streaming Industry segmented?

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

Product

  Platform
  Services


End-user

  Media and entertainment
  Education
  Esports
  Events
  Others


Type

  Audio Streaming
  Video Streaming


Revenue Model

  Ad-Supported
  Subscription-Based
  Pay-Per-View


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The platform segment is estimated to witness significant growth was estimated at USD 7.96 billion, and it is forecast to see a moderate upward trend through the forecast period.

Live streaming, a real-time video and audio content delivery solution, is experiencing significant growth in the digital media landscape. Platforms, which account for the largest segment of the market, enable users to access and play compressed content instantly over the internet. The viewer experience is paramount, with low-latency streaming ensuring minimal delay, while mobile streaming caters to the increasing number of mobile users. Live chat integration fosters audience engagement, and stream recording solutions allow viewers to revisit content. The market supports 4k live streaming for high-definition visuals, secure streaming protocols for data security, and esports streaming for gaming enthusiasts.

Bandwidth optimization and video player technology facilitate seamless streaming, while hdr live streaming enhances visual quality. Cloud-based streaming, video encoding formats, and

Snapshot img

Streaming Analytics Market Size 2024-2028

The streaming analytics market size is forecast to increase by USD 39.7 at a CAGR of 34.63% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need to improve business efficiency in various industries. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies is a key trend driving market growth. These technologies enable real-time data processing and analysis, leading to faster decision-making and improved operational performance. However, the integration of streaming analytics solutions with legacy systems poses a challenge. IoT platforms play a crucial role In the market, as IoT-driven devices generate vast amounts of data that require real-time analysis. Predictive analytics is another area of focus, as it allows businesses to anticipate future trends and customer behavior, leading to proactive decision-making.Overall, the market is expected to continue growing, driven by the need for real-time data processing and analysis in various sectors.

What will be the Size of the Streaming Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for real-time insights from big data generated by emerging technologies such as IoT and API-driven applications. This market is driven by the strategic shift towards digitization and cloud solutions among large enterprises and small to medium-sized businesses (SMEs) across various industries, including retail. Legacy systems are being replaced with modern streaming analytics platforms to enhance data connectivity and improve production and demand response. The financial impact of real-time analytics is substantial, with applications in fraud detection, predictive maintenance, and operational efficiency. The integration of artificial intelligence (AI) and machine learning algorithms further enhances the market's potential, enabling businesses to gain valuable insights from their data streams.Overall, the market is poised for continued expansion as more organizations recognize the value of real-time data processing and analysis.

How is this Streaming Analytics Industry segmented and which is the largest segment?

The streaming analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn premisesTypeSoftwareServicesGeographyNorth AmericaCanadaUSAPACChinaJapanEuropeUKMiddle East and AfricaSouth America

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Cloud-deployed streaming analytics solutions enable businesses to analyze data in real time using remote computing resources, such as the cloud. This deployment model streamlines business intelligence processes by collecting, integrating, and presenting derived insights instantaneously, enhancing decision-making efficiency. The cloud segment's growth is driven by benefits like quick deployment, flexibility, scalability, and real-time data visibility. Service providers offer these capabilities with flexible payment structures, including pay-as-you-go. Advanced solutions integrate AI, API, and event-streaming analytics capabilities, ensuring compliance with regulations, optimizing business processes, and providing valuable data accessibility. Cloud adoption in various sectors, including finance, healthcare, retail, and telecom, is increasing due to the need for real-time predictive modeling and fraud detection.SMEs and startups also benefit from these solutions due to their ease of use and cost-effectiveness. In conclusion, cloud-based streaming analytics solutions offer significant advantages, making them an essential tool for organizations seeking to digitize and modernize their IT infrastructure.

Get a glance at the Streaming Analytics Industry report of share of various segments Request Free Sample

The Cloud segment was valued at USD 4.40 in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 34% to the growth of the global market during the forecast period.

Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

In North America, the region's early adoption of advanced technology and high data generation make it a significant market for streaming analytics. The vast amounts of data produced in this tech-mature region necessitate intelligent analysis to uncover valuable relationships and insights. Advanced software solutions, including AI, virtualiza

Internet of Things sensor networks collect real-time data, characterized by spatial and temporal correlations, for process monitoring, significantly altering daily life and enabling automation. Considering sensor resource constraints due to limited budget of sensor operation and the complexity of capturing spatiotemporal correlation structure among data streams, sensor networks face challenges in monitoring such data streams via a parametric model and distribution, particularly when only subsets of data are available at each acquisition time. This article develops a nonparametric scheme for monitoring such complex spatiotemporally correlated and partially observed data streams. It employs decorrelated rank-based statistics combined with data augmentation over multiple subdata streams, which are derived from the original high-dimensional data using ensemble random projections for dimensionality reduction. Monitoring and sampling decisions are informed by aggregated local statistics of all subdata streams. This method is distribution-free that eschews parametric spatiotemporal models and distributions for real-time monitoring, enhancing its practical applicability to various complex spatiotemporal engineering cases that cannot be accurately characterized by parametric models. The efficacy of the decorrelated rank-based statistics and sampling strategy is substantiated through theoretical analysis. Numerical experiments and case studies focusing on thermal data monitoring in grain storage and solar flare detection affirm robust performance of the proposed method across various scenarios.

Global live streaming market size was $107.69 billion in 2024 and is projected to reach $564.16 billion by 2034, a CAGR of 23.0% from 2025 to 2034

The free streaming software market is experiencing robust growth, driven by the increasing popularity of live streaming across various platforms like Twitch, YouTube, and Facebook. The market's accessibility, coupled with the rising number of content creators and gamers, fuels this expansion. While precise market sizing requires proprietary data, a reasonable estimation, based on the growth of related sectors like gaming and live streaming platforms, suggests a current market value in the hundreds of millions of dollars. The Compound Annual Growth Rate (CAGR) is likely to remain strong, potentially exceeding 15% over the next decade, owing to continuous technological advancements and the ongoing diversification of streaming content. The market segmentation reveals a preference for cloud-based solutions due to their scalability and ease of use. However, on-premise solutions continue to hold a significant share, especially among users with high bandwidth requirements or privacy concerns. The dominance of certain operating systems is expected, with Windows holding a substantial majority, followed by MacOS, and Linux representing a niche but growing segment. Competitive landscape analysis shows a mix of established players like OBS Studio and newer entrants continuously innovating to capture market share. Geographic analysis reveals strong growth across North America and Asia-Pacific regions, driven by high internet penetration and a large base of content creators and consumers. Europe and other regions are also showing steady growth. Future market expansion hinges on several factors. The increasing affordability and accessibility of high-speed internet will be a major driver. Furthermore, improvements in streaming technology, such as enhanced video quality and reduced latency, will further incentivize adoption. However, challenges remain, including potential limitations in monetization options for creators using free software and the ongoing need to address potential security vulnerabilities in open-source solutions. The market will see continued consolidation, with smaller players likely to be acquired by larger companies. This trend will likely lead to increased feature sets and enhanced user experiences for free streaming software users. The development and integration of AI-powered features, such as automated scene switching and enhanced streaming analytics, will also shape the market's trajectory.

This location data product focuses on real-time GPS pings collected across North, Central, and South America. Using the Irys Location API, users can access polygon-based movement patterns from anonymized mobile devices across urban and rural areas.

Events include timestamps, precise geocoordinates, country codes, and device identifiers. Query the dataset using polygon filters and receive structured outputs via API or cloud endpoints. Supported formats include JSON, CSV, and Parquet.

With historical backfill and fresh updates every day, the dataset is ideal for retailers, advertisers, city planners, and researchers analyzing behavior and trends across the Americas. It supports use cases like retail site selection, mobility forecasting, and geofencing for public safety.

All data is delivered with a maximum lag of three days and complies with GDPR and CCPA regulations.

Context

This is the first live data stream on Kaggle providing a simple yet rich source of all soccer matches around the world 24/7 in real-time.

What makes it unique compared to other datasets?

• It is the first live data feed on Kaggle and it is totally free
• Unlike “Churn rate” datasets you do not have to wait months to evaluate your predictions; simply check the match’s outcome in a couple of hours
• you can use your predictions/analysis for your own benefit instead of spending your time and resources on helping a company maximizing its profit
• A Five year old laptop can do the calculations and you do not need high-end GPUs
• Couldn’t make it to the top 3 submissions? Nevermind, you still have the chance to get your prize on your own
• You can’t get accurate results on all samples? Do not worry, just filter out the hard ones (e.g. ignore international friendly) and simply choose the ones you are sure of.
• Need help from human experts for each sample? Every sample comes with at least two opinions from experts
• You wish you could add your complementary data? Just contact us and we will try to facilitate it.
• Couldn’t win “Warren Buffett's 2018 March Madness Bracket Contest”? Here is your chance to make your accumulative profit.

Simply train your algorithm on the first version of training dataset of approximately 11.5k matches and predict the data provided in the following data feed.

Fetch the data stream

The CSV file is updated every 30 minutes at minutes 20’ and 50’ of every hour. I kindly request not to download it more than twice per hour as it incurs additional cost.

You may download the csv data file from the following link from Amazon S3 server by changing the FOLDER_NAME as below,

https://s3.amazonaws.com/FOLDER_NAME/amasters.csv

*. Substitute the FOLDER_NAME with "**analyst-masters**"

Content

Our goal is to identify the outcome of a match as Home, Draw or Away. The variety of sources and nature of information provided in this data stream makes it a unique database. Currently, FIVE servers are collecting data from soccer matches around the world, communicating with each other and finally aggregating the data based on the dominant features learned from 400,000 matches over 7 years. I describe every column and the data collection below in two categories, Category I – Current situation and Category II – Head-to-Head History. Hence, we divide the type of data we have from each team to 4 modes,

• Mode 1: we have both Category I and Category II available
• Mode 2: we only have Category I available
• Mode 3: we only have Category II available
• Mode 4: none of Category I and II are available

Below you can find a full illustration of each category.

I. Current situation

Col 1 to 3:

Votes_for_Home Votes_for_Draw Votes_for_Away

The most distinctive parts of the database are these 3 columns. We are releasing opinions of over 100 professional soccer analysts predicting the outcome of a match. Their votes is the result of every piece of information they receive on players, team line-up, injuries and the urge of a team to win a match to stay in the league. They are spread around the world in various time zones and are experts on soccer teams from various regions. Our servers aggregate their opinions to update the CSV file until kickoff. Therefore, even if 40 users predict Real-Madrid wins against Real-Sociedad in Santiago Bernabeu on January 6th, 2019 but 5 users predict Real-Sociedad (the away team) will be the winner, you should doubt the home win. Here, the “majority of votes” works in conjunction with other features.

Col 4 to 9:

Weekday Day Month  Year  Hour  Minute

There are over 60,000 matches during a year, and approximately 400 ones are usually held per day on weekends. More critical and exciting matches, which are usually less predictable, are held toward the evening in Europe. We are currently providing time in Central Europe Time (CET) equivalent to GMT +01:00.

*. Please note that the 2nd row of the CSV file represents the time, data values are saved from all servers to the file.

Col 10 to 13:

Total_Bettors   Bet_Perc_on_Home    Bet_Perc_on_Draw   Bet_Perc_on_Away

This data is recorded a few hours before the match as people place bets emotionally when kickoff approaches. The percentage of the overall number of people denoted as “Total_Bettors” is indicated in each column for “Home,” “Draw” and “Away” outcomes.

Col 14 to 15:

Team_1 Team_2   

The team playing “Home” is “Team_1” and the opponent playing “Away” is “Team_2”.

Col 16 to 36:

League_Rank_1  League_Rank_2  Total_teams     Points_1  Points_2  Max_points Min_points Won_1  Draw_1 Lost_1 Won_2  Draw_2 Lost_2 Goals_Scored_1 Goals_Scored_2 Goals_Rec_1 Goal_Rec_2 Goals_Diff_1  Goals_Diff_2

If the match is betw...

Streaming Analytics Market Outlook

According to our latest research, the global streaming analytics market size reached USD 19.2 billion in 2024, driven by accelerated digital transformation across industries and the surging need for real-time data analysis. The market is exhibiting robust momentum, with a recorded CAGR of 25.7% from 2025 to 2033. Based on this growth trajectory, the streaming analytics market is forecasted to achieve a value of USD 143.2 billion by 2033. The primary growth factor propelling this expansion is the increasing demand for advanced analytics solutions that can process and analyze large volumes of data in real time, enabling organizations to make faster, data-driven decisions.

A major driver fueling the growth of the streaming analytics market is the exponential rise in data generated from connected devices, IoT sensors, social media, and enterprise applications. Organizations across sectors are recognizing the competitive advantage of leveraging streaming analytics to monitor operations, detect anomalies, and respond proactively to business events as they unfold. The ability to harness real-time insights has become essential in high-stakes environments such as financial services for fraud detection, telecommunications for network optimization, and manufacturing for predictive maintenance. The proliferation of 5G networks and the expansion of cloud computing infrastructure have further accelerated the adoption of streaming analytics platforms by providing the necessary bandwidth and computational power to process massive data streams with minimal latency.

Another significant growth factor is the integration of artificial intelligence (AI) and machine learning (ML) technologies with streaming analytics solutions. These advanced capabilities enable organizations to move beyond traditional descriptive analytics toward predictive and prescriptive analytics, uncovering actionable insights from live data feeds. The demand for real-time analytics is also being amplified by the increasing adoption of edge computing, which allows data to be processed closer to its source, reducing response times and improving decision-making efficiency. As organizations continue to invest in digital transformation initiatives, the need to analyze data as it is generated—rather than after it is stored—has become a critical differentiator for operational agility and customer experience.

The streaming analytics market is also benefitting from the growing emphasis on regulatory compliance and risk management. In highly regulated industries such as banking, healthcare, and energy, organizations are leveraging streaming analytics to monitor transactions, detect suspicious activities, and ensure compliance with evolving regulatory requirements. The ability to analyze data in real time not only helps in mitigating risks but also supports proactive incident response and reporting. Furthermore, the increasing focus on customer-centricity in retail and e-commerce is driving the adoption of streaming analytics to personalize marketing efforts, optimize inventory, and enhance overall service delivery. These factors collectively underscore the market’s strong growth outlook through the forecast period.

Regionally, North America continues to dominate the streaming analytics market, accounting for the largest share in 2024 due to the presence of leading technology providers, rapid adoption of advanced analytics solutions, and substantial investments in cloud infrastructure. However, the Asia Pacific region is emerging as the fastest-growing market, driven by the digitalization of enterprises, expanding e-commerce sector, and government initiatives supporting smart cities and IoT deployments. Europe maintains a strong position, particularly in sectors such as BFSI and manufacturing, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions increasingly recognize the value of real-time data analytics. This global expansion highlights the universal relevance of streaming analytics across industries and geographies.

Component

This Java Script Open Notation (JSON) Feed and ArcGIS Online map presents the unplanned road closures within the ACT in real-time.

The fields of the JSON string include:

Object ID (Numeric field) Global ID (Alpha-numeric field) Project title (Free text field string) Type (Selected from: light rail, special event, road works, emergency, utilities, telecommunications, building construction, ActewAGL, NBN, charitable organisation, inclement weather, other) Describe activity (Free text field string required if "other" type selected) Roads closed (Free text field string) Reason for road closure (Free text field string) Closure start time (Unix Format - see data dictionary for syntax) Closure end time (Unix Format - see data dictionary for syntax) Geometry (X,Y longitude, latitude format)

More information regarding the syntax for querying this API can be found in the linked data dictionary.

PowerMap can infer the subject of trading volume in real-time. This information allows users to predict the order flow by investor type of institutional, foreign, and retail traders. By implementing Direct Market Access (DMA) and High-Frequency Trading (HFT) technology, PowerMap processes and delivers large-scale transactions in real time for the Korean market. Processing high volumes of stock transactions instantly requires robust data processing capabilities. PowerMap receives direct trade data from KRX and analyzes buy and sell signals for approximately 1,000 stocks in real time, covering KOSPI stocks with a market cap over 200 billion KRW ($133.38 million) and KOSDAQ stocks over 150 billion KRW ($103.81 million).

Key Features: 💠 Real-time investor type classification (institutional, and foreign institutional) 💠 Low-latency data ingestion 💠 Coverage of over 1,200 liquid KOSPI and KOSDAQ stocks 💠 Instantaneous detection of large-block trades and directional flow 💠 Scalable architecture for high-volume transaction analysis

Primary Use Cases: 🔹 Institutional and proprietary traders monitoring market sentiment shifts 🔹 Quant desks identifying real-time trade triggers and flow-based signals 🔹 Algo developers incorporating investor-type flow into trading strategies 🔹 Broker-dealers and research teams analyzing intraday market dynamics 🔹 Portfolio managers assessing liquidity and participation trends

Contact us for a real time order flow data in different markets. Stay ahead with TradePulse's order flow insights.

The global video live social platform market is experiencing robust growth, driven by the increasing adoption of smartphones, rising internet penetration, and the surging popularity of live streaming content across various demographics. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors: the continuous innovation in live streaming technologies offering enhanced user experiences (e.g., improved video quality, interactive features, and AR/VR integration); the growing preference for real-time engagement and interaction among users; and the increasing integration of live streaming into various sectors, including e-commerce, education, and entertainment. The mobile segment currently dominates the market, owing to the widespread accessibility of smartphones, while the "free with in-app purchases" model represents a significant revenue stream for platform providers. However, challenges remain, including concerns over content moderation, maintaining user engagement amidst platform saturation, and ensuring data privacy and security. Competition within the market is intense, with established players like Facebook Live, Instagram Live, and YouTube Live competing with newer entrants vying for market share. Geographic growth is uneven, with North America and Asia-Pacific exhibiting the highest adoption rates due to high internet penetration and a tech-savvy populace. However, increasing smartphone penetration in developing economies presents significant growth opportunities in regions such as South America, Africa, and parts of Asia. The market is also segmented by monetization strategies, with "free with in-app purchases" models becoming increasingly prevalent. Future growth will depend on continuous innovation, effective content moderation strategies, and a focus on enhancing user experiences to overcome challenges related to data privacy and security. Strategic partnerships and acquisitions are also expected to play a significant role in shaping the competitive landscape in the coming years.

The global free streaming software market is estimated to be valued at USD XXX million in 2025 and is projected to expand at a CAGR of XX% during the forecast period (2025-2033). The growth of the market is primarily driven by the increasing popularity of live streaming, the rising adoption of cloud-based software, and the growing demand for affordable and user-friendly software. Additionally, the increasing use of free streaming software by businesses for marketing and promotional purposes is further contributing to the market growth. The free streaming software market is segmented based on application, type, and region. In terms of application, the market is divided into Windows, Mac, and Linux. Based on type, the market is classified into on-premise and cloud-based. Regionally, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market throughout the forecast period due to the high adoption of streaming software and the presence of major players in the region. Europe and Asia Pacific are expected to witness significant growth during the forecast period due to the increasing popularity of live streaming and the rising demand for affordable software.

Context

I wanted to find a better way to provide live traffic updates. We dont all have access to the data from traffic monitoring sensors or whatever gets uploaded from people's smart phones to Apple, Google etc plus I question how accurate the traffic congestion is on Google Maps or other apps. So I figured that since buses are also in the same traffic and many buses stream their GPS location and other data live, that would be an ideal source for traffic data. I investigated the data streams available from many bus companies around the world and found MTA in NYC to be very reliable.

Content

This dataset is from the NYC MTA buses data stream service. In roughly 10 minute increments the bus location, route, bus stop and more is included in each row. The scheduled arrival time from the bus schedule is also included, to give an indication of where the bus should be (how much behind schedule, or on time, or even ahead of schedule).

Acknowledgements

Data is recorded from the MTA SIRI Real Time data feed and the MTA GTFS Schedule data.

Inspiration

I want to see what exploratory & discovery people come up with from this data. Feel free to download this dataset for your own use however I would appreciate as many Kernals included on Kaggle as we can get.

Based on the interest this generates I plan to collect more data for subsequent months down the track.

Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi’s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging.

By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers.

Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods.

The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities.

To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not.

The pilot system ran on three layers of data:

Layer One

Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network.

Layer Two

When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space.

Layer Three

To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi

mac: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone.

market: The name of the market where the phone was seen

role: Self-identified role if the user connected to the wifi and filled-out the layer 2 form

gender: Self-identified role if the user connected to the wifi and filled-out the layer 2 form

median_first_seen: The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market)

median_last_seen: The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market)

average_time_day: The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit.

average_duration_day: The average duration spent on the market daily.

average_day_week: The average number of visits per week.

average_total_day_seen: The total number of days a user was seen on the market.

total_durantion: Total duration spent by a single user on the market.

Both the scheduled timetable and the real-time data are available to you for free. There are two GTFS feeds available: The scheduled data which are updated daily The real-time data which are updated every 30 seconds

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

We tested our hypotheses with a questionnaire. This questionnaire was administered electronically, in three languages (French, English and German), to viewers of two major opera streaming platforms: Staatsoperlive.comFootnote2 and Operavision.Footnote3 Staatsoperlive.com was the paid streaming service offered by the Vienna State Opera, launched in 2013. It broadcasted 50 live operas in HD every season, which made it the virtual opera house with the largest repertoire in the world. The platform also offered a video library of some 30 operas and its content was reviewed every month. The number of viewers for a live streamed performance was equivalent to a capacity audience at the Vienna State Opera. Viewers could access the service through a subscription or packages. They could also buy single live performances or videos on demand. Operavision is the digital platform of the European opera association, Opera Europa. It is the major stakeholder of free opera streaming. It broadcasts operas – mostly live – from its more than 25 partner opera houses, on its website and on YouTube. More than 40 operas per season are available for replay for several weeks or months. During a live broadcast, Operavision viewers can interact by taking part in a chat available on YouTube. The links to the questionnaires were sent in the newsletters of both institutions. 213 responses were collected (153 for Staatsoperlive.com and 60 for Operavision). 60% of the surveyed people are men. The respondents are usually older and predominantly highly educated, as it is the case of the 'in-situ' opera audience (Agid & Tarondeau, Citation2010). 62% are over 60 years of age and 58% of them hold at least a bachelor's degree, while only 8% did not complete secondary education. Besides, more than half of the sample (56%) have taken music or singing lessons. On another level, a substantial majority of the respondents, three quarters of which watched the streamed performance alone, enjoy opera frequently; like the spectators of Live HD operas in cinemas as Villanueva Benito and Lacasa-Mas (Citation2017) detected and analysed. In all of the channels we considered, almost three quarters of respondents (73%) enjoy watching at least one opera per month and half of them enjoy more than 20 operas per year. The surveyed viewers are regular streamed opera patrons, since 60% of them watch an opera at least every two months. They are also mostly opera houses customers (92%): more than half of the sample (54%) attends more than 5 operas in a theatre each year

Free-Cash-Flow-To-Equity Time Series for Net263 Ltd. NET263 Ltd. provides cloud services in China and internationally. The company offers cloud network solutions, including virtual private network, data center, real-time audio and video transmission network, and 5G communication solutions; and cloud communication solutions, such as corporate live streaming, enterprise email, video conferencing, teleconference, and call center solutions. It also provides virtual space construction, 3D virtual live broadcast, AI digital human design, and other services for various industries, as well as replacement, digital marketing, smart office and home, smart part building and cultural tourism, and AIGC application solutions. The company was founded in 1997 and is headquartered in Beijing, China.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

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