The Near Real-time Data Access (NeRDA) Portal is making near real-time data available to our stakeholders and interested parties. We're helping the transition to a smart, flexible system that connects large-scale energy generation right down to the solar panels and electric vehicles installed in homes, businesses and communities right across the country. In line with our Open Networks approach, our Near Real-time Data Access (NeRDA) portal is live and making available power flow information from our EHV, HV, and LV networks, taking in data from a number of sources, including SCADA PowerOn, our installed low voltage monitoring equipment, load model forecasting tool, connectivity model, and our Long-Term Development Statement (LTDS). Making near real-time data accessible from DNOs is facilitating an economic and efficient development and operation in the transition to a low carbon economy. NeRDA is a key enabler for the delivery of Net Zero - by opening network data, it is creating opportunities for the flexible markets, helping to identify the best locations to invest flexible resources, and connect faster. You can access this information via our informative near real-time Dashboard and download portions of data or connect to our API and receive an ongoing stream of near real-time data.

Streaming APIs allow for big data processing of native data structures by providing MapReduce-like operations over these structures. However, unlike traditional big data systems, these data structures typically reside in shared memory accessed by multiple cores. Although popular, this emerging hybrid paradigm opens the door to possibly detrimental behavior, such as thread contention and bugs related to non-execution and non-determinism. This study explores the use and misuse of a popular streaming API, namely, Java 8 Streams. The focus is on how developers decide whether or not to run these operations sequentially or in parallel and bugs both specific and tangential to this paradigm. Our study involved analyzing 34 Java projects and 5.53 million lines of code, along with 719 manually examined code patches. Various automated, including interprocedural static analysis, and manual methodologies were employed. The results indicate that streams are pervasive, stream parallelization is not widely used, and performance is a crosscutting concern that accounted for the majority of fixes. We also present coincidences that both confirm and contradict the results of related studies. The study advances our understanding of streams, as well as benefits practitioners, programming language and API designers, tool developers, and educators alike.

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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

Social media can be mirrors of human interaction, society, and world events. Their reach enables the global dissemination of information in the shortest possible time and thus the individual participation of people all over the world in global events in almost real-time. However, equally efficient, these platforms can be misused in the context of information warfare in order to manipulate human perception and opinion formation. The outbreak of war between Russia and Ukraine on February 24, 2022, demonstrated this in a striking manner.

Here we publish a dataset of raw tweets collected by using the Twitter Streaming API in the context of the onset of the war which Russia started on Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia's invasion of Ukraine. We publish the IDs of all tweets we streamed during that time, the time we rehydrated them using Twitter's API as well as the result of the rehydration. If you use this dataset, please cite our related Paper:

Pohl, Janina Susanne and Seiler, Moritz Vinzent and Assenmacher, Dennis and Grimme, Christian, A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 (March 25, 2022). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543

Line feature representing the centerline of smaller stream channels in the City of Alexandria, Virginia. Provides centerline location of all streams less than 5 feet in width.

Perennial streams in Fairfax County. Perennial streams are bodies of water flowing in a natural or man-made channel year-round, except during periods of drought.

This data displays the access locations of rivers and streams for fishing in New York State, as determined by fisheries biologists working for the New York State Department of Environmental Conservation. Although every effort has been made to ensure the accuracy of information, errors may be reflected in the data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions.

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.

Publication of EPA’s Nutrient Inventory is a critical step towards thorough mapping and accounting of sources of N and P to US landscapes. However, summaries of nutrients within accumulative watersheds are needed to develop accurate watershed-level nutrient budgets and relate landscape inputs to instream nutrient concentrations. This subproduct will accumulate the Nutrient Inventory across available years for all streams and lakes within the medium resolution National Hydrography Dataset Plus version 2 (NHDPlus), i.e., 2.6 million stream segments and nearly 400,000 lakes across the conterminous US. These data will allow OW to easily and rapidly identify the dominant sources of N or P for any stream segment or lake in the US. Further, these data will be made accessible through the EPA’s StreamCat and LakeCat datasets and a soon-to-be released online database and an application programming interface (API). This database and API will make nutrient watershed accumulations readily accessible and easily integrated by a variety of OW programs and tools. Finally, the accumulated nutrient data will serve as the basis for a multiple proposed StRAP subproducts and models in SSWR.401, SSWR.404, and SSWR.405. These data will contribute directly to OW, region, and state efforts to identify and reduce non-point nutrient sources. Having spatially explicit data about nutrient sources and loads can help target and inform restoration and conservation efforts, as well as more formal TMDLs, nutrient reduction plans, and groundwater management approaches. This subproduct will produce a database of accumulated nutrient values for at least 2.6 million stream segments and 400,000 lakes of the medium resolution National Hydrography Dataset Plus version 2 (NHDPlus). These data will be made accessible through the StreamCat and LakeCat datasets. They will also be made available as an online database with application programming interface (API) that will facilitate data acquisition and use by OW and state partners. This database will provide a state-of-the science accounting of nutrient sources that drain to all streams and lakes in the conterminous US. It will allow EPA and state partners to identify dominant sources of N and P to individual waterbodies and will greatly facilitate nutrient reduction strategies and planning.

River and Stream lines that represent flowlines and cartographic features such as stream centerlines and river banks.

Success.ai’s Connected TV Data for Broadcast Media & Entertainment Professionals Worldwide offers a comprehensive dataset tailored for businesses seeking to engage with key decision-makers and innovators in the broadcast and entertainment industries. Covering professionals from global media corporations, production studios, streaming platforms, and ad-tech companies, this dataset provides verified contact numbers, email addresses, and geographic location data.

With access to over 700 million verified global profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and strategic planning are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution empowers businesses to thrive in the dynamic world of connected TV and entertainment.

Why Choose Success.ai’s Connected TV Data?

1. Verified Contact Data for Precision Targeting

   • Access verified phone numbers, work emails, and LinkedIn profiles of professionals in broadcast media, OTT platforms, and connected TV ecosystems.
   • AI-driven validation ensures 99% accuracy, minimizing wasted efforts and maximizing campaign efficiency.

2. Comprehensive Global Coverage

   • Includes profiles of professionals and companies from major entertainment hubs like Los Angeles, London, Mumbai, Seoul, and more.
   • Gain insights into regional content trends, technology adoption, and market opportunities across continents.

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   • Real-time updates reflect changes in leadership, business expansions, content strategies, and technology investments.
   • Stay aligned with fast-evolving industry trends and maintain relevance in your outreach efforts.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data for your campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, content creators, and tech innovators in broadcast media and entertainment worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic locations.
• Contact and Location Data: Gain direct access to verified phone numbers, email addresses, and physical office locations for strategic outreach.
• Leadership Profiles: Engage with CEOs, CTOs, production heads, and ad-tech professionals shaping connected TV strategies.

Key Features of the Dataset:

1. Decision-Maker Profiles in Media & Entertainment

   • Identify and connect with executives, content strategists, and technology leaders driving innovation in connected TV and broadcast media.
   • Target professionals responsible for ad placement, content distribution, and audience engagement.

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   • Access detailed business information, including company hierarchies, geographic locations, and operational structures.
   • Pinpoint key players in regional markets and identify emerging content hubs.

3. Advanced Filters for Precision Campaigns

   • Filter companies by segment (streaming platforms, cable networks, production studios), geographic location, company size, or revenue range.
   • Tailor outreach to align with specific industry challenges, such as audience targeting, ad-tech integration, or content localization.

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   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with media professionals.

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   • Present innovative ad-placement technologies, audience analytics tools, or campaign management platforms to ad-buyers and media strategists.
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   • Analyze trends in content consumption, connected TV adoption, and audience preferences to refine your product or service offerings.
   • Leverage these insights to identify untapped opportunities and create competitive advantages.

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Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality connected TV data at competitive prices, ensuring ...

This dataset shows river streams in Ireland. This dataset contains just Order 0 and 1 rivers. Stream order is a measure of the relative size of streams. The smallest tributaries are referred to as first-order streams.

City of Fairfax Streams data. Updated on an as needed basis.

This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "work, gym, party, sleep | morning, run, night, dance | car, train, relax, club".

Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "linear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type". The users are represented as embeddings based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "age, country, gender".

The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: https://github.com/KarimMibrahim/Situational_Session_Generator.git

The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an anonymized user_id which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100].

Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: ColdTrack (no overlap of tracks between the splits), ColdUser (no overlap of users between the splits), and WarmCase (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "fold" number in each condition.

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ~642 thousand lines of code. We found that 36.31% of candidate streams were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

Explore comprehensive insights in TLD profiles, featuring detailed information for .stream. Utilize our API for efficient data access.

This layer represents modeled stream temperatures derived from the NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the > 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.

Used software versions

Self-built:

PIConGPU: https://github.com/franzpoeschel/picongpu/tree/smc2021-paper GAPD: closed source software, Git tag smc2021-paper in private repository openPMD-api: https://github.com/franzpoeschel/openPMD-api/tree/smc2021-paper ADIOS2: https://github.com/ornladios/ADIOS2, Git hash bf25ad59b8b15b9f48ddabad65a41f2050d3bd7f libfabric: 1.6.3a1

Summit modules:

1) gcc/8.1.1
2) spectrum-mpi/10.3.1.2-20200121
3) cmake/3.18.2
4) git/2.20.1
5) cuda/10.1.243
6) boost/1.66.0
7) zlib/1.2.11
8) libpng/1.6.34 9) freetype/2.9.1 10) python/3.7.0-anaconda3-5.3.0