This dataset contains a Neo4j .dump file for the constructed PICO-based Biomedical Knowledge Graph (EBM-KG). The graph is built from the EBM-NLP dataset and represents key PICO (Population, Intervention, Comparator, Outcome) elements and their relationships.

The knowledge graph can be restored in Neo4j to support biomedical text mining, literature-based discovery, and advanced retrieval-augmented generation (RAG) pipelines.

Neo4j database dump file contains the following : - Document, keyword, and author nodes for each PubMed article in the EBM-NLP dataset. - PICO nodes with their sub-labels as defined in the EBM-NLP dataset.

Total 23 entity types and 22 relation types is present in the knowledge graph

How to Restore the Database 1. Install Neo4j (compatible with version used: Neo4j 5.24.0). 2. Stop the Neo4j server. 3. Run : neo4j-admin database load --from-path=[xxx/neo4j.dump]/backups --overwrite-destination=true 4. Start the Neo4j server

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.

'gophoxes - Gopher Server with Knowledge Boxes' is a gopher server with knowledge boxes manager and a web server for gopher client. You can use your Android phone & gophoxes for Android to make a hotspot which provides WIFI, gopher server, gopher client for web and knowledge boxes.

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.

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.

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.

This is the Wikidata5m knowledge graph, published in the KEPLER paper (https://arxiv.org/abs/1911.06136), see https://deepgraphlearning.github.io/project/wikidata5m for more information. The entities have been translated to integer IDs by libKGE (https://github.com/uma-pi1/kge). The triples in the train.del file have been shuffled.

This is the knowledge graph that was used in the paper NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access (https://arxiv.org/abs/2104.00501).

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.

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.

NerdOptimize Dataset (v1.0.0)

English dataset for SEO (Data‑Driven) and AI Search / AEO by NerdOptimize (Bangkok, TH).Built for GitHub, Hugging Face, and on‑site deployment, so LLMs can learn/cite the brand.

  Structure

data/*.json → core machine‑readable data (ICPs, services, case studies, frameworks, articles, labels, metadata, processing steps) server.js / openapi.json → tiny Express API to serve the dataset schema-dataset.jsonld → Dataset JSON‑LD for Google Dataset… See the full description on the dataset page: https://huggingface.co/datasets/NerdOptimize/nerd-knowledge-api.

The global Micro Server Market is poised for substantial growth, projected to reach an estimated market size of approximately $4,200 million by the end of 2025. This expansion is fueled by a robust Compound Annual Growth Rate (CAGR) of 9.11% throughout the forecast period of 2025-2033. Key drivers propelling this market include the escalating demand for efficient computing solutions in data centers and the burgeoning adoption of cloud computing services. Enterprises are increasingly recognizing the benefits of micro servers, such as lower power consumption, reduced heat output, and a smaller physical footprint, making them ideal for scalable and high-density deployments. Furthermore, the continuous advancements in processor technology, particularly with the increasing prevalence of ARM-based processors, are enhancing the performance and versatility of micro servers, further stimulating market expansion. The growing emphasis on data analytics and the need for specialized infrastructure to manage vast datasets are also significant contributors to this upward trajectory. The market is segmented across various processor types, with Intel, AMD, and ARM processors holding significant shares, each catering to different performance and efficiency needs. Applications span across critical areas like data centers, cloud computing, media storage, and data analytics, underscoring the wide-ranging utility of micro servers. The end-user landscape is diverse, encompassing small enterprises seeking cost-effective scalability, medium enterprises demanding balanced performance and efficiency, and large enterprises requiring high-density, power-efficient solutions for their extensive IT infrastructures. Geographically, North America and Europe are expected to lead the market due to early adoption of advanced technologies and significant investments in cloud infrastructure. The Asia Pacific region is anticipated to witness the fastest growth, driven by rapid digitalization and the expansion of IT services in emerging economies. Restraints such as initial deployment costs for some advanced configurations and the established dominance of traditional server architectures in certain legacy systems are being systematically addressed by technological innovation and growing awareness of the long-term cost benefits. This comprehensive report delves into the dynamic global Micro Server market, providing in-depth analysis of its current state, future projections, and key influencing factors. The market is segmented by processor type, application, and end-user, with a focus on regional trends and significant industry developments. Our analysis incorporates estimations and insights derived from extensive industry knowledge, presenting a robust overview for stakeholders. The projected market size for the micro server market is estimated to reach $9,850 million by 2028, exhibiting a compound annual growth rate (CAGR) of 12.5% from its 2023 valuation of $5,400 million. Key drivers for this market are: , Rise in Demand of Cloud Facilities for Various Applications; Rise in Number of Medium- and Small-scale Enterprises Globally. Potential restraints include: , Lack of Awareness. Notable trends are: Cloud Computing Micro Servers to Offer Potential Growth.

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.

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.

[ TEXTIFY math.stackexchange.com ]

The purpose of this knowledge box is converting articles of math.stackexchange.com to gopher text, then saving them in the file structures which is ready for using by "gophoxes - Gopher Server with Knowledge Boxes".

Discover Market Research Intellect's Web Server Market Report, worth USD 10.5 billion in 2024 and projected to hit USD 20.3 billion by 2033, registering a CAGR of 8.1% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Discover Market Research Intellect's White Box Server Market Report, worth USD 3.2 billion in 2024 and projected to hit USD 5.5 billion by 2033, registering a CAGR of 7.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

Discover Market Research Intellect's Managed Servers Market Report, worth USD 20 billion in 2024 and projected to hit USD 35 billion by 2033, registering a CAGR of 7.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Discover Market Research Intellect's Enterprise Network Time Servers Market Report, worth USD 1.2 billion in 2024 and projected to hit USD 2.5 billion by 2033, registering a CAGR of 9.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Discover Market Research Intellect's Blade Server Market Report, worth USD 3.45 billion in 2024 and projected to hit USD 6.12 billion by 2033, registering a CAGR of 8.1% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.