Available data formats for the FTP Server Market Report, Size, Share, Opportunities, and Trends Segmented By Type, Transmission Mode, Enterprise Size, and Geography – Forecasts from 2025 to 2030 report.

NOTE: An updated Introduction to ArcGIS GeoEvent Server Tutorial is available here. It is recommended you use the new tutorial for getting started with GeoEvent Server. The old Introduction Tutorial available on this page is relevant for 10.8.x and earlier and will not be updated.The Introduction to GeoEvent Server Tutorial (10.8.x and earlier) introduces you to the Real-Time Visualization and Analytic capabilities of ArcGIS GeoEvent Server. GeoEvent Server allows you to:

Incorporate real-time data feeds in your existing GIS data and IT infrastructure. Perform continuous processing and analysis on streaming data, as it is received. Produce new streams of data that can be leveraged across the ArcGIS system.

Once you have completed the exercises in this tutorial you should be able to:

Use ArcGIS GeoEvent Manager to monitor and perform administrative tasks. Create and maintain GeoEvent Service elements such as inputs, outputs, and processors. Use GeoEvent Simulator to simulate event data into GeoEvent Server. Configure GeoEvent Services to append and update features in a published feature service. Work with processors and filters to enhance and direct GeoEvents from event data.

The knowledge gained from this tutorial will prepare you for other GeoEvent Server tutorials available in the ArcGIS GeoEvent Server Gallery.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 7 - March 30, 2018 - Compatible with ArcGIS GeoEvent Server 10.6 and later.

Previous

Release 6 - January 12, 2018 - Compatible with ArcGIS GeoEvent Server 10.5 thru 10.8.

Release 5 - July 30, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 4 - July 30, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x.

Release 3 - April 24, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 2 - January 22, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 1 - April 11, 2014 - Compatible with ArcGIS GeoEvent Server 10.2.x.

This dataset is based on the model developed with the Ph.D. students of the Communication and Information Sciences Ph.D. program at the University of Hawaii at Manoa, intended to help new students get relevant information. The model was first presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students " by Stanislava Gardasevic and Rich Gazan (available at: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/9eebcea7-06fd-4db3-b420-347883e6379e/content)The database is created in Neo4J, and the .dump file can be imported to the cloud instance of this software. The dataset (.dump) contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph demonstrating progress through a student's program in this degree, and activities they have done while in the program. This dataset was made possible with the huge help of my collaborator, Petar Popovic, who ingested the data in the database.The model and dataset were developed while involving the end users in the design and are based on the actual information needs of a population. It is intended to allow researchers to investigate multigraph visualization of the data modeled by the said model.The knowledge graph was evaluated with CIS student population, and the study results show that it is very helpful for decision-making, information discovery, and identification of people in one's surroundings who might be good collaborators or information points. We provide the .json file containing the Neo4J Bloom perspective with styling and queries used in these evaluation sessions.

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.

Available data formats for the Rack Server Market Size, Share, Opportunities, And Trends By Rack Type (Wall Mounted, Open Frame, Enclosed Cabinet), By Enterprise Size (Small, Medium, Large), By End-User (IT & Telecommunication, Government & Defense, BFSI, Retail, Others), And By Geography - Forecasts From 2023 To 2028 report.

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.

LfULG Sachsen has developed a method for recording medium soil sealing for the entire state area from existing data sets. Information from the ATKIS base DLM (Land Survey, as of 2018) is used, and the mean soil sealing is assigned to the respective legend units. For the use of the information in the 3 planning areas of regional, regional and municipal planning, the information is classified into 3 different grids with the following cell sizes: 1000 × 1000 meters: Country planning; 100 × 100 meters: Regional planning; 25 × 25 meters: Local planning. Each cell carries the mean degree of sealing of the soil as surface information. The information will be gradually updated over the next few years and new levels of knowledge will be incorporated. This information will be published with updates.

Available data formats for the Enterprise Server Market Size, Share, Opportunities, And Trends By Operating System (UNIX, Linux, Windows, Others), By Industry Vertical (BFSI, Retail, Media and Entertainment, IT and Telecom, Healthcare, Manufacturing, Others), And By Geography - Forecasts From 2025 to 2030 report.

Available data formats for the Global Server Microprocessor Market Size, Share, Opportunities, And Trends By Design (X86, ARM, Others), By CPU Sockets (1 to 2 Socket Servers, 3 to 4 Socket Severs, 5+ Socket Servers), And By Geography - Forecasts From 2025 To 2030 report.

ObjectivesThe secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine.MethodsWe tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction “proton-pump inhibitor [PPI] use and osteoporosis”. Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard.ResultsPrecision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%.ConclusionWe could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.

The SPARC Knowledge base of the Autonomic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used by SPARC which is a combination of the NIF-Ontology and community ontologies.

The fastest way to get querying is to follow the instructions in the SCKAN readme file.

For background information please see https://scicrunch.org/sawg/about/SCKAN and the SPARC portal resource page about SCKAN.

This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.

The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the SCKAN readme file for the most up-to-date instructions.

We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.

This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.

The size of the Denmark Data Center Server market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 3.30% during the forecast period.The Denmark Data Center Server Market is the country's largest chunk of burgeoning digital infrastructure. Data center servers are huge computers housed in data centers and used for the storage, processing, and management of vast volumes of data. They are the spinal cord of modern digital services, enabling cloud computing, e-commerce, social media, and many more web applications. It is driven by an increase in the generation of data, the increasing cloud computing services, and digitalization being implemented by the government in the Danish market.Disclaimer: The given information is just for general awareness and knowledge purposes only. Specific data and analysis on the market will be obtained through industry research reports from reputed organizations. Recent developments include: May 2023: By combining the Intersight infrastructure management platform with Unified Computing System (UCS) X-Series servers, Cisco says it can reduce data center energy consumption by up to 52 percent at a four-to-one (4:1) server consolidation ratio., March 2023: Supermicro has launched a new server that uses a standalone liquid cooling system and is designed as a platform for developing and running AI software. SYS-751GE-TNRT-NV1 Server is overheating. It features four NVIDIA A100 GPUs consuming 300 W each and is liquid-cooled by a standalone system.. Key drivers for this market are: Increasing Number of Smartpone Users, Fiber Connectivity Network Expansion. Potential restraints include: Increasing number of Data Security Breaches. Notable trends are: Blade Servers To Grow At A Faster Pace In Coming Years.

Available data formats for the Data Center Rack Server Market Size, Share, Opportunities, And Trends By Type (Tier 1, Tier 2, Tier 3, Tier 4), By Services (Consulting, Installation, Support and Maintenance), By Organization Size (Small, Medium, Large), By Vertical (Telecom and IT, Banking and Financial Services, Healthcare, Retail, Government, Media and Entertainment, Others), And By Geography - Forecasts From 2025 To 2030 report.

The global rack server market is a dynamic and rapidly evolving sector, exhibiting significant growth potential. While precise figures for market size and CAGR are unavailable from the provided data, leveraging industry knowledge and acknowledging a period of robust expansion in recent years, a reasonable estimation for the 2025 market size could be approximately $25 billion USD. This is supported by the continued demand driven by factors such as the proliferation of cloud computing, big data analytics, and the increasing adoption of artificial intelligence (AI). The growth is further fueled by the ongoing digital transformation across various industries and the need for scalable and high-performance computing infrastructure. Key trends shaping this market include the rising adoption of edge computing, the increasing demand for energy-efficient servers, and the shift towards software-defined infrastructure. Furthermore, the growing popularity of hyperscale data centers and the expansion of 5G networks are contributing to the market's impressive trajectory. Leading vendors like Hewlett-Packard, Dell, and Lenovo are continually innovating to meet these evolving demands, offering a wide spectrum of server solutions catering to specific business needs and technological advancements. However, market expansion isn't without its challenges. The complexities and costs associated with managing increasingly intricate IT infrastructure could present a restraint. Furthermore, supply chain disruptions, component shortages, and economic fluctuations can impact market growth. Nevertheless, the long-term forecast remains positive, with sustained growth projected throughout the 2025-2033 period. Segmentation within the market, while not fully detailed, likely includes distinctions based on server type (e.g., blade servers, tower servers), processor type, memory capacity, and storage solutions. This segmentation creates specialized niches allowing companies to target specific client needs effectively and continue to drive innovation within the rack server industry.

Discover Market Research Intellect's Multi-node Server Market Report, worth USD 3.5 billion in 2024 and projected to hit USD 7.8 billion by 2033, registering a CAGR of 10.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Dielectric Fluid Immersion Server Market Outlook

According to our latest research, the Global Dielectric Fluid Immersion Server market size was valued at $1.26 billion in 2024 and is projected to reach $7.45 billion by 2033, expanding at a robust CAGR of 21.8% during 2024–2033. The primary growth driver for this market is the exponential surge in global data processing needs, particularly fueled by the proliferation of artificial intelligence, cloud computing, and edge computing applications. As organizations strive to optimize energy efficiency and reduce operational costs in data centers, dielectric fluid immersion cooling technologies are increasingly being adopted to address the limitations of traditional air-based cooling systems. This transition is not only enhancing server performance and longevity but also significantly reducing the carbon footprint of large-scale computing operations, making dielectric fluid immersion a cornerstone of next-generation IT infrastructure.

Regional Outlook

North America currently commands the largest share of the Dielectric Fluid Immersion Server market, accounting for over 39% of global revenue in 2024. The region’s dominance is underpinned by a mature data center ecosystem, early adoption of advanced cooling technologies, and supportive regulatory frameworks that incentivize energy-efficient infrastructure. The United States, in particular, is home to several hyperscale data centers and technology giants who are early adopters of immersion cooling to meet sustainability goals and manage escalating heat loads from high-density servers. Additionally, robust investments in research and development, coupled with partnerships between technology vendors and academic institutions, are accelerating innovation in dielectric fluid formulations and server hardware compatibility, solidifying North America’s leadership in this arena.

Asia Pacific is emerging as the fastest-growing region in the Dielectric Fluid Immersion Server market, projected to expand at a remarkable CAGR of 27.5% between 2024 and 2033. This rapid growth is driven by booming digital economies in China, India, Japan, and Southeast Asia, where increasing internet penetration and the expansion of cloud services are fueling massive investments in new data centers. Regional governments are also rolling out policies and incentives to promote green technology adoption, further accelerating the deployment of energy-efficient cooling solutions. Local enterprises are increasingly recognizing the operational and sustainability benefits of immersion cooling, while multinational cloud providers are establishing regional hubs to capitalize on the growing demand for low-latency, high-performance computing infrastructure.

In emerging markets such as Latin America, the Middle East, and Africa, the Dielectric Fluid Immersion Server market is still in its nascent stages, with adoption hampered by high initial capital requirements, limited technical expertise, and a lack of standardized regulatory frameworks. However, as digital transformation initiatives gain momentum and governments invest in ICT infrastructure, there is a growing awareness of the long-term cost and sustainability advantages of immersion cooling. Localized demand is being shaped by the unique climatic and power supply challenges in these regions, making immersion cooling an attractive solution for reliable, efficient server operation. International partnerships and pilot projects are beginning to bridge the knowledge gap, paving the way for more widespread adoption in the coming years.

Report Scope

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The global rack server market is experiencing robust growth, driven by the increasing demand for data storage and processing capabilities across various sectors. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This expansion is fueled by several key factors, including the proliferation of cloud computing, big data analytics, and the growing adoption of artificial intelligence (AI) and machine learning (ML) applications. Businesses across IT & Telecommunications, BFSI, Manufacturing, Retail, Healthcare, and Media & Entertainment are investing heavily in upgrading their IT infrastructure to support these technologies, leading to heightened demand for rack servers. The diverse range of operating systems (Linux, Windows, UNIX) and the availability of diverse server configurations cater to varied organizational needs and budgets. Significant regional growth is anticipated in Asia Pacific, driven by rapid technological advancements and increasing digitalization initiatives in countries like China and India. While potential restraints include supply chain disruptions and fluctuations in component costs, the long-term outlook remains positive, given the sustained demand for efficient and scalable computing solutions. The market segmentation reveals a strong preference for Linux-based rack servers, reflecting its cost-effectiveness and open-source nature. Major players like Hewlett-Packard, Dell Inc., IBM, and Cisco Systems dominate the market landscape, leveraging their established brand reputation and extensive product portfolios. However, the increasing presence of ODMs (Original Design Manufacturers) is intensifying competition and driving down prices, making rack servers more accessible to a wider range of businesses. Future market trends point towards increased demand for high-performance computing (HPC) servers, edge computing solutions, and environmentally friendly rack servers with enhanced energy efficiency. Continued innovation in server technologies, coupled with the expansion of 5G networks and the Internet of Things (IoT), will further propel market expansion in the coming years. This comprehensive report delves into the dynamic world of rack servers, a multi-billion dollar market projected to surpass $100 billion in revenue by 2028. We provide in-depth analysis of market size, key players, emerging trends, and future growth projections, empowering businesses to make informed decisions in this rapidly evolving landscape. This report uses data informed by industry knowledge and analysis to provide estimates when precise figures are unavailable.

Available data formats for the Data Center Blade Server Market Size, Share, Opportunities, And Trends By Function (Web Hosting, Virtualization, Cluster Computing), By End-User (Colocation Provider, Cloud Providers, Enterprise), And By Geography - Forecasts From 2025 To 2030 report.