This dataset contains application and network measurements of 6,982 individual Twitch.tv streaming sessions of 222 different streamers summing up to more than 1,000h live streaming. The data are aggregated to uplink requests.

We propose a novel technology to leverage rapidly evolving cloud based infrastructure to improve time constrained situational awareness for real-time decision making. Our "CloudTurbine" innovation eliminates the distinction between files and streams to distribute live streaming sensor and video data over cloud file sharing services.
Streaming and static data have long been considered separately, with unique mechanisms for data transmittal and viewing of each. Files are the greatest common denominator linking static data across all computers. However, real-time streaming data distribution is widely presumed to be sensor-centric; i.e. up-front requirements to "keep up" with live data trump all other considerations.
A great unification of cloud based services for static data has recently occurred. There are now many providers of "file sharing" cloud based services. The paradigm for all is simple: (1) put data in a local file folder, (2) it automatically shows up at other linked systems via a cloud service. Wouldn't it be nice if one could unify an approach to streaming data that leveraged this file-sharing cloud infrastructure? That is precisely what we propose. Building upon a functional prototype, we propose to characterize, evaluate, refine and adapt CloudTurbine technology to NASA and commercial applications. CloudTurbine is a streaming data interface to and from standard file sharing cloud services. It delegates much of the data transmittal, security, and server resources to the cloud service provider. It provides robust continuous streaming for high data and frame rates while trading off manageable amounts of delivery latency (on the order of seconds). In so doing, it eliminates the distinction between files and streams, and enables a simple, cost effective new paradigm for streaming data middleware.

users.csv User_id: Unique identifier of user Country_code: Country code where the user registered assets.csv Show_type: Type of content, whether the asset is a movie or an episode of a TV series Genre: Genre of content Running_miutes: Runtime of content (Playable number of minutes) Source_language: Production language of content Asset_id: Unique identifier of video content at the most granular level (a movie or an episode of a TV series) Season_id: Unique identifier of content at season level. This is only applicable to TV series Series_id: Unique identifier of content at series level. This is only applicable to TV series Studio_id: Unique identifier of production studio for the content plays.csv Platform: Platform of consumption Minutes_viewed : Total number of minutes viewed, rounded to the nearest integer (0 means less than 30 seconds) Demographics.csv Psychographics.csv The dataset identifies psychographic and demographic tags about some iflix users. Each user-tag pair has an associated confidence score (1 is the highest, and 0 is the lowest confidence). Each trait can have up to 3 levels, depending on its granularity. Some traits can be identified by only considering the first two levels. At the same time, there are others that make more sense when all the three levels are considered, e.g., ‘iflix Viewing Behaviour’ is a level 2 psychographic trait that only makes sense when it is looked at in combination with the level 3 traits corresponding to it (‘casual,’ ‘player’ and ‘addict’). These traits represent different levels of viewing behavior of iflix users. Casual users have less than five viewing days in a month, player users have 5 to 12 viewing days in a month, and people with an addiction have more than 12 viewing days in a month. Traits are available corresponding to a user_id in the dataset only if we have certain confidence that the user belongs to the trait. Column and Description Level_1: Identifies the first level of the trait (psychologic or demographic) Level_2: Identifies the second level of the trait (e.g., Music Lovers, Movies Lovers) Level_3 : Identifies the third level of the trait, if available/relevant (e.g. Malay Movies Lovers, Indonesian TV Fans) Confidence_score: Confidence in associating the said trait (level_1, level_2, level_3) with the user

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000 scale and exists at that scale for the whole country. High resolution NHD adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Like the 1:100,000-scale NHD, high resolution NHD contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria set out by the Federal Geographic Data Committee.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The USGS National Hydrography Dataset (NHD) downloadable data collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP include NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards. The next generation of national hydrography data is the USGS 3D Hydrography Program (3DHP).

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

This dataset represents the dam density and storage volumes within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on National Inventory of Dams (NID) data. Attributes were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics.(See Supplementary Info for Glossary of Terms). The NID database contains information about the dams location, size, purpose, type, last inspection, regulatory facts, and other technical data. Structures on streams reduce the longitudinal and lateral hydrologic connectivity of the system. For example, impoundments above dams slow stream flow, cause deposition of sediment and reduce peak flows. Dams change both the discharge and sediment supply of streams, causing channel incision and bed coarsening downstream. Downstream areas are often sediment deprived, resulting in degradation, i.e., erosion of the stream bed and stream banks. This database was improved upon by locations verified by work from the USGS National Map (Jeff Simley Group). It was observed that some dams, some of them major and which do exist, were not part of the 2009 NID, but were represented in the USGS National Map dataset, and had been in the 2006 NID. Approximately 1,100 such dams were added, based on the USGS National Map lat/long and the 2006 NID attributes (dam height, storage, etc.) Finally, as clean-up, a) about 600 records with duplicate NIDID were removed, and b) about 300 records were removed which represented the same location of the same dam but with a different NIDID, for the largest dams (did visual check of dams with storage above 5000 acre feet and are likely duplicated - about the 10,000 largest dams) . The (dams/catchment) and (dam_storage/catchment) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a point data type

Mapping of rivers and non-stream water points in Puy-de-Dôme prepared in accordance with the Government Instruction of 3 June 2015 on the mapping and identification of rivers and their maintenance and the Ministerial Orders of 04/05/2017 and Prefectural of 05/07/2017 on untreated areas.

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

The National Hydrography Dataset (NHD) High Resolution flowlines were used as a base to provide additional information on the connectivity of the stream network for the hydrographic basins in and around Montana. In addition to the attributes that are published as part of the NHD data, two fields were added to the attribute table to associate streams that do not have a Geographic Names Information System (GNIS) name with the GNIS name and NHD reachcode of the nearest downstream named flowline. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data were originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. This dataset is NHD Model version 2.2.1. For more information on the NHD High Resolution dataset, see Model Diagram at: http://ftp.geoinfo.msl.mt.gov/Data/Spatial/MSDI/Hydrography.

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The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. For additional information on NHD, go to https://www.usgs.gov/national-hydrography.

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.

Real-Time Streaming Processing Platform Market Outlook

The global real-time streaming processing platform market size was valued at approximately USD 12.5 billion in 2023 and is projected to reach around USD 45.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.5% during the forecast period. This impressive growth is driven by the increasing demand for quick data analysis, proliferating IoT devices, and the surge in real-time analytics across various industries.

One of the primary growth factors for this market is the exponential rise in data generation from multiple sources such as social media platforms, sensors, and IoT devices. The need for immediate data processing and decision-making in areas like fraud detection, customer experience management, and predictive analytics has led to the adoption of real-time streaming processing platforms. Companies are now more focused on extracting actionable insights from vast volumes of data in real-time to stay competitive in their respective industries.

Furthermore, advancements in technologies such as artificial intelligence and machine learning are significantly contributing to the growth of the real-time streaming processing platform market. These technologies enable more sophisticated data analysis, allowing businesses to derive deeper insights and make informed decisions swiftly. The integration of AI and ML models with real-time streaming data has opened new avenues for innovations in predictive maintenance, personalized marketing, and dynamic pricing models.

The growing adoption of cloud-based solutions is another major factor boosting the market. Cloud platforms offer scalable, flexible, and cost-effective solutions for real-time data processing, making them highly attractive to businesses of all sizes. The ability to process large streams of data efficiently and the ease of integration with various cloud services are propelling the shift towards cloud-based deployment. This trend is expected to continue, driving market growth further.

The integration of a Real Time Database into streaming platforms is becoming increasingly crucial as businesses seek to enhance their data processing capabilities. Real Time Databases allow for the immediate storage and retrieval of data, which is essential for applications that require up-to-the-second accuracy. By leveraging these databases, organizations can ensure that their data is not only processed swiftly but also stored in a manner that allows for rapid querying and analysis. This capability is particularly beneficial for industries that rely on real-time decision-making, such as finance and telecommunications, where the timeliness of data can significantly impact outcomes.

Regionally, North America holds a dominant position in the real-time streaming processing platform market, attributed to the advanced IT infrastructure and the presence of major technology companies. Europe and Asia-Pacific are also significant markets, with the Asia-Pacific region expected to witness the highest CAGR during the forecast period. The increasing digital transformation initiatives and the growing adoption of advanced analytics solutions are key drivers in these regions.

Component Analysis

The real-time streaming processing platform market is segmented by component into software, hardware, and services. The software segment is anticipated to hold the largest share of the market due to the continuous advancements and innovations in software solutions that facilitate real-time data processing and analytics. Companies are increasingly investing in sophisticated software tools that can seamlessly integrate with their existing systems and enhance their data processing capabilities.

Hardware components, though a smaller segment compared to software, play a crucial role in the overall efficiency of real-time streaming processing platforms. High-performance servers, storage systems, and networking equipment are essential to handle the immense data volumes and speed required for real-time processing. The demand for specialized hardware capable of supporting intensive data workloads is on the rise, contributing to the market growth.

Services, including consulting, implementation, and support services, are also integral to this market. Many organizations lack the necessary expertise in-house to deploy and manage real-time streaming platforms effectively. As a result, they turn to e

(Link to Metadata) VHDCARTO is a simplified version of the local resolution Vermont Hydrography Dataset (VHD) that has been enriched with stream perenniality, e.g., "intermittent" vs. "perennial", as well as, Strahler stream order attribution for the single linear feature class only. The primary means of accessing this information cartographically is via the FCODE and STREAM_ORDER fields, respectively. See the Entity and Attribution Information section for details. NOTE! Perenniality data does not exist for stream reaches contained within, or intersected by, Essex or Caledonia counties, thus the FCODE "46000" in these areas. The absence of Soil SUrvey GeOgraphic (SSURGO) database information in these areas precluded the computation of perenniality. These areas will be processed at some future date. For information on the FCODE symbol for attribution or analysis see the following document https://www.usgs.gov/national-hydrography/national-hydrography-dataset (NHDFlowline). A two dimensional feature class for lakes, ponds and larger streams is also included in VHDCARTO. Both layers are derived from the latest National Hydrography Dataset (NHD) data. The NHD is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. For information on the science behind computing perenniality attribution please refer to the following U.S. Geological Survey Scientific Investigative Report (SIR) # 2006-5217 - https://pubs.usgs.gov/sir/2006/5217/pdf/SIR2006-5217_report.pdf

YouTube was created in 2005, with the first video – Me at the Zoo - being uploaded on 23 April 2005. Since then, 1.3 billion people have set up YouTube accounts. In 2018, people watch nearly 5 billion videos each day. People upload 300 hours of video to the site every minute.

According to 2016 research undertaken by Pexeso, music only accounts for 4.3% of YouTube’s content. Yet it makes 11% of the views. Clearly, an awful lot of people watch a comparatively small number of music videos. It should be no surprise, therefore, that the most watched videos of all time on YouTube are predominantly music videos.

On August 13, BTS became the most-viewed artist in YouTube history, accumulating over 26.7 billion views across all their official channels. This count includes all music videos and dance practice videos.

Justin Bieber and Ed Sheeran now hold the records for second and third-highest views, with over 26 billion views each.

Currently, BTS’s most viewed videos are their music videos for “**Boy With Luv**,” “**Dynamite**,” and “**DNA**,” which all have over 1.4 billion views.

Headers of the Dataset Total = Total views (in millions) across all official channels Avg = Current daily average of all videos combined 100M = Number of videos with more than 100 million views

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.

Wadhwani AI Pest Management Open Data

This dataset is a Hugging Face adaptor to the official dataset hosted on Github. Please refer to that repository for detailed and up-to-date documentation.

  Usage

This dataset is large. It is strongly recommended users access it as a stream: from datasets import load_dataset dataset = load_dataset('wadhwani-ai/pest-management-opendata', streaming=True)

Bounding boxes are stored as geospatial types. Once loaded, they can be… See the full description on the dataset page: https://huggingface.co/datasets/wadhwani-ai/pest-management-opendata.

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).