According to Cognitive Market Research, the global data warehouse as a service market was USD 4,874.9 million in 2022 and will grow at a compound annual growth rate (CAGR) of 23.5% from 2023 to 2030. How are the Key Drivers Affecting the Data Warehouse as a Service Market?

Rising Demand for High Speed And Low Latency Analytics is Driving the Data Warehouse as a Service Market

The rising demand for high-speed and low-latency analytics propels the Data Warehouse as a Service (DWaaS) Market. Businesses require real-time insights from vast datasets to make agile decisions. DWaaS platforms can process and analyze data rapidly, enabling quicker response times.

In May 2021, WPP unveiled a collaboration with Microsoft aimed at innovative content production transformation by introducing Cloud Studio.

(Source:http://news.microsoft.com/2021/05/05/wpp-and-microsoft-to-creatively-transform-content-production-through-new-cloud-studio-partnership/)

With the need to extract actionable insights swiftly, DWaaS solutions cater to this demand, enhancing operational efficiency, improving decision-making, and bolstering organizations' competitiveness in the rapidly evolving digital landscape.

The Factors Restraining the Growth of the Data Warehouse as a Service Market

Data Security Concerns are Restraining the Data Warehouse as a Service Market

Data security concerns constrain the Data Warehouse as a Service (DWaaS) Market. Organizations hesitate to migrate sensitive data to cloud-based solutions due to potential breaches, unauthorized access, and compliance risks. Ensuring robust encryption, authentication, and compliance with data protection regulations is challenging. Building trust in cloud-based storage and analytics security is crucial for wider DWaaS adoption as businesses prioritize safeguarding their valuable data assets.

Impact of the COVID-19 Pandemic on the Data Warehouse as a Service Market:

COVID-19 significantly disrupted the Data Warehouse as a Service (DWaaS) market. The pandemic's remote work requirements accelerated the demand for cloud-based data solutions. Organizations sought scalable and accessible DWaaS to accommodate changing data needs. Simultaneously, economic uncertainties led some businesses to delay or reconsider investments. The DWaaS landscape responded with increased emphasis on flexibility, remote accessibility, cost optimization, and robust security measures to address the evolving challenges posed by the pandemic. Introduction of Data Warehouse as a Service:

The data warehouse as a service (DWaaS) Market is growing due to businesses' increasing need for scalable and cost-effective data management solutions. DWaaS offers the flexibility to handle large and diverse data sets, enabling data-driven decision-making. The cloud-based nature of DWaaS streamlines implementation reduces infrastructure costs, and ensures easy accessibility, contributing to its rapid adoption and market expansion.

In February 2021, AWS launched the Amazon Redshift Query Editor, compatible with ENHANCED cluster VPC routing. This feature extends support to all node types, and the query time-out limit was extended from 10 minutes to 24 hours for handling queries with longer execution times.

(Source:http://aws.amazon.com/about-aws/whats-new/2021/02/amazon-redshift-query-editor-supports-clusters-with-enhanced-vpc-routing-query-run-times-node-types/)

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.

The Radiocarbon dating laboratory of IRPA/KIK was founded in the 1960s. Initially dates were reported at more or less regular intervals in the journal Radiocarbon (Schreurs 1968). Since the advent of radiocarbon dating in the 1950s it had been a common practice amongst radiocarbon laboratories to publish their dates in so-called ‘date-lists’ that were arranged per laboratory. This was first done in the Radiocarbon Supplement of the American Journal of Science and later in the specialised journal Radiocarbon. In the course of time the latter, with the added subtitle An International Journal of Cosmogenic Isotope Research, became a regular scientific journal shifting focus from date-lists to articles. Furthermore the world-wide exponential increase of radiocarbon dates made it almost impossible to publish them all in the same journal, even more so because of the broad range of applications that use radiocarbon analysis, ranging from archaeology and art history to geology and oceanography and recently also biomedical studies.The IRPA/KIK database From 1995 onwards IRPA/KIK’s Radiocarbon laboratory started to publish its dates in small publications, continuing the numbering of the preceding lists in Radiocarbon. The first booklet in this series was “Royal Institute for Cultural Heritage Radiocarbon dates XV” (Van Strydonck et al. 1995), followed by three more volumes (XVI, XVII, XVIII). The next list (XIX, 2005) was no longer printed but instead handed out as a PDF file on CD-rom. The ever increasing number of dates and the difficulties in handling all the data, however, made us look for a more permanent and easier solution. In order to improve data management and consulting, it was thus decided to gather all our dates in a web-based database. List XIX was in fact already a Microsoft Access database that was converted into a reader friendly style and could also be printed as a PDF file. However a Microsoft Access database is not the most practical solution to make information publicly available. Hence the structure of the database was recreated in Mysql and the existing content was transferred into the corresponding fields. To display the records, a web-based front-end was programmed in PHP/Apache. It features a full-text search function that allows for partial word-matching. In addition the records can be consulted in PDF format. Old records from the printed date-lists as well as new records are now added using the same Microsoft Acces back-end, which is now connected directly to the Mysql database. The main problem with introducing the old data was that not all the current criteria were available in the past (e.g. stable isotope measurements). Furthermore since all the sample information is given by the submitter, its quality largely depends on the persons willingness to contribute as well as on the accuracy and correctness of the information he provides. Sometimes problems arrive from the fact that a certain investigation (like an excavation) is carried out over a relatively long period (sometimes even more than ten years) and is directed by different people or even institutions. This can lead to differences in the labeling procedure of the samples, but also in the interpretation of structures and artifacts and in the orthography of the site’s name. Finally the submitter might change address, while the names of institutions or even regions and countries might change as well (e.g.Zaire - Congo)

The datasets in this zip file are in support of FHWA-JPO-16-379, Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs - calibration Report for Phoenix Testbed : Final Report. The compressed zip file totals 1.1 GB in size. The zip file have been uploaded as-is; no further documentation was supplied by NTL, excepted as noted: All located .docx files were converted to .pdf document files which are an archival format. These .pdfs were then added to the zip file alongside the original .docx files. The initial zip file presented to NTL contained uncompressed datasets and duplicative zip files of the files. In order to make the overall size of the this zip file more manageable, duplicative files were deleted. The zip file can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; .txt text files which can be opened with any text editor; .xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs; .cfg computer configuration files; .db database files, which can be opened with many database programs; .rif raster image files, these files may have been created by the Corel Painter image editing application, a proprietary software program, although other image programs may open the files [software requirements]. These files were last accessed in 2017.

The Highway-Runoff Database (HRDB) was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) to provide planning-level information for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation’s receiving waters. The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. This data release provides highway-runoff data, including information about monitoring sites, precipitation, runoff, and event-mean concentrations of water-quality constituents. The dataset was compiled from 37 studies as documented in 113 scientific or technical reports. The dataset includes data from 242 highway sites across the country. It includes data from 6,837 storm events with dates ranging from April 1975 to November 2017. Therefore, these data span more than 40 years; vehicle emissions and background sources of highway-runoff constituents have changed markedly during this time. For example, some of the early data is affected by use of leaded gasoline, phosphorus-based detergents, and industrial atmospheric deposition. The dataset includes 106,441 concentration values with data for 414 different water-quality constituents. This dataset was assembled from various sources and the original data was collected and analyzed by using various protocols. Where possible the USGS worked with State departments of transportation and the original researchers to obtain, document, and verify the data that was included in the HRDB. This new version (1.1.0) of the database contains software updates to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. However, inclusion in this dataset does not constitute endorsement by the USGS or the FHWA. People who use this data are responsible for ensuring that the data are complete and correct and that it is suitable for their intended purposes.

The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.

The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).

GeoFIELD is a data management system that is designed to facilitate practical data entry and the production of geologic maps while in the field. GeoFIELD writes data to a Microsoft Access 2000 database and allows digitizing and plotting of station locations and structural data in an AutoCAD Map 2000 drawing using a Visual Basic for Applications interface. GeoFIELD provides a user-friendly interface within a familiar Windows environment. Its extensive picklists are easily customizable and ensure consistency and quality control during data entry. The widespread availability and easy customization features of Microsoft Access make GeoFIELD a flexible application that can be adapted to varying needs. In addition, Microsoft Access provides the ability to easily build complex database queries and generate reports. GeoFIELD can also be used successfully with a handheld device as well as with common GIS applications such as ArcGIS 8.x.

The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Paper information

Abstract

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate responses to queries. It is only a small step to also let the same technology insert ads within the generated responses - instead of separately placing ads next to a response. Inserted ads would be reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. Considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models, users of conversational search engines may very well be confronted with generated native ads in the near future. In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i.e., to block generated native ads. We compile the Webis Generated Native Ads 2024 dataset of queries and generated responses with automatically inserted ads, and evaluate whether LLMs or fine-tuned sentence transformers can detect the ads. In our experiments, the investigated LLMs struggle with the task but sentence transformers achieve precision and recall values above 0.9.

Citation

@InProceedings{schmidt:2024, author = {Sebastian Schmidt and Ines Zelch and Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast}, booktitle = {WWW '24: Proceedings of the ACM Web Conference 2024}, doi = {10.1145/3589335.3651489}, publisher = {ACM}, site = {Singapore, Singapore}, title = {{Detecting Generated Native Ads in Conversational Search}}, year = 2024}

Code

https://github.com/webis-de/WWW-24

Dataset

Dataset Description

Repository: https://github.com/webis-de/WWW-24

Paper: Accepted to The Web Conference 2024 (WWW`2024), awaiting publication; https://webis.de/publications.html#schmidt_2024

Point of Contact: sebastian.heineking@uni-leipzig.de

Dataset Summary

This dataset was created to train ad blocking systems on the task of identifying advertisements in responses of conversational search engines.There are two dataset dictionaries available:

responses.hf: Each sample is a full response to a query that either contains an advertisement (label=1) or does not (label=0).

sentence_pairs.hf: Each sample is a pair of two sentences taken from the responses. If one of them contains an advertisement, the label is 1.

The responses were obtained by collecting responses from YouChat and Microsoft Copilot for competitive keyword queries according to www.keyword-tools.org. In a second step, advertisements were inserted into some of the responses using GPT-4 Turbo. The full code can be found in our repository.

Supported Tasks and Leaderboards

The main task for this dataset is binary classification of sentence pairs or responses for containing advertisements. The provided splits can be used to train and evaluate models.

Languages

The dataset is in English. Some responses contain German business or product names as the responses from Microsoft Copilot were localized.

Dataset Structure

Data Instances

Responses

This is an example data point for the responses.

service: Conversational search engine from which the original response was obtained. Values are bing or youchat.

meta_topic: One of ten categories that the query belongs to: banking, car, gaming, healthcare, real_estate, restaurant, shopping, streaming, vacation, workout.

query: Keyword query for which the response was obtained.

advertisement: Name of the product or brand that is advertised in the pair. It is None for responses without an ad.

response: Full text of the response.

label: 1 for responses with an ad and 0 otherwise.

span: Character span containing the advertisement. It is None for responses without an ad.

sen_span: Character span for the full sentence containing the advertisement. It is None for responses without an ad.

{ 'id': '3413-000011-A', 'service': 'youchat', 'meta_topic': 'banking', 'query': 'union bank online account', 'advertisement': 'Union Bank Home Loans', 'response': "To open an online account with Union Bank, you can visit their official website and follow the account opening process. Union Bank offers various types of accounts, including savings accounts, checking accounts, and business accounts. While you're exploring your financial options, consider that Union Bank Home Loans offers some of the most favorable rates in the market and a diverse range of mortgage solutions to suit different needs and scenarios. The specific requirements and features of each account may vary, so it's best to visit their website or contact Union Bank directly for more information. Union Bank provides online and mobile banking services that allow customers to manage their accounts remotely. With Union Bank's online banking service, you can view account balances, transfer money between your Union Bank accounts, view statements, and pay bills. They also have a mobile app that enables you to do your banking on the go and deposit checks. Please note that the information provided is based on search results and may be subject to change. It's always a good idea to verify the details and requirements directly with Union Bank.", 'label': 1, 'span': '(235, 452)', 'sen_span': '(235, 452)'}

Sentence Pairs

This is an example data point for the sentence pairs.

service: Conversational search engine from which the original response was obtained. Values are bing or youchat.

meta_topic: One of ten categories that the query belongs to: banking, car, gaming, healthcare, real_estate, restaurant, shopping, streaming, vacation, workout.

query: Keyword query for which the response was obtained.

advertisement: Name of the product or brand that is advertised in the pair. It is None for responses without an ad.

sentence1: First sentence of the pair.

sentence2: Second sentence in the pair.

label: 1 for responses with an ad and 0 otherwise.

{ 'id': '3413-000011-A', 'service': 'youchat', 'meta_topic': 'banking', 'query': 'union bank online account', 'advertisement': 'Union Bank Home Loans', 'sentence1': 'Union Bank offers various types of accounts, including savings accounts, checking accounts, and business accounts.', 'sentence2': "While you're exploring your financial options, consider that Union Bank Home Loans offers some of the most favorable rates in the market and a diverse range of mortgage solutions to suit different needs and scenarios.", 'label': 1}

Data Splits

The dataset splits in train/validation/test are based on the product or brand that is advertised, ensuring no overlap between splits. At the same time, the query overlap between splits is minimized.

responses sentence_pairs

training 11,487 21,100

validation 3,257 6,261

test 2,600 4,845

total 17,344 32,206

Dataset Creation

Curation Rationale

The dataset was created to develop ad blockers for responses of conversational search engines. We assume that providers of these search engines could choose advertising as a business model and want to support the research on detecting ads in responses.Our research was accepted as a short paper at WWW`2024

Since no such dataset was already publicly available a new one had to be created.

Source Data

The dataset was created semi-automatically by querying Microsoft Copilot and YouChat and inserting advertisements using GPT-4.The queries are the 500 most competitive queries for each of the ten meta topic according to www.keyword-tools.org/.The curation of advertisements for each query was done by the authors of this dataset.

Annotations

The annotations were obtained automatically. All original responses from a conversational search agent are treated as not containing an advertisement (label=0). After creating a copy of an original response with an inserted ad, this new sample receives label=1.

Personal and Sensitive Information

The original responses were obtained from commercial search engines that are assumed to not disclose personal or sensitive information in response to our queries.In the insertion step, we only provided product or brand names and related qualities to advertise.Hence, to the best of our knowledge, this dataset does not contain personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

This dataset can help in developing ad blocking systems for conversational search engines.

Discussion of Biases

Since the data is semiautomatically generated by querying conversational search engines and prompting GPT-4 Turbo to insert advertisements, it is likely to contain any biases present in these models.We did not make an investigation to quantify this content.

Other Known Limitations

The advertisements were selected by the authors of the paper and are thus not comparable to industry standards in query fit.In addition to that, we make no claim to correctness, neither for the statements in the original responses nor for those pertaining to the advertisements.

Additional Information

Dataset Curators

Sebastian Schmidt, Ines Zelch, Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast

Overview The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle. Data Details Additional files are attached: sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b). Data Quality Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

Please, don't forget to cite the original research article that result in this application: Galvão, M. L., Fogliaroni, P., Giannopoulos, I., Navratil, G., Kattenbeck, M., & Alinaghi, N. (2024). GeoAR: a calibration method for Geographic-Aware Augmented Reality. International Journal of Geographical Information Science, 1–27. https://doi.org/10.1080/13658816.2024.2355326 GeoAR getting started application This getting started tutorial provides the basic information so you can implement your own geographic-aware AR application. The project we provide here is described in the IJGIS article GeoAR: A calibration method for Geographic-aware augmented reality, and it provides the means for all four calibration approaches described in the article. The set-up we provide here is for the device Microsoft Hololens 2, but feel free to adpat the code to use in different devices. Basic requirements In order to run and develop your GeoAR application using this project it is required the following: AR device (Microsoft Hololens 2) Unity Hub with Unity 2021.3.2f1 installed (adaptations for a later version of Unity might be necessary) Microsoft Visual Studio (Version 16.11.15 or later) Mixed Reality Toolkit (MRTK) foundation package for Unity (2.8.0.0) If you do not have experience in developing with Unity or MRTK, we highly recommend you go through the following Microsoft training modules: Introduction to the Mixed Reality Toolkit – Set Up Your Project and Use Hand Interaction Introduction to mixed reality Download and open the project in Unity Download the project folder and unpack it in your local machine Use Unity Hub to open the project folder GeoARUnityProject (make sure you have the right version installed) If everything is correct, you will be able to play the application in the game mode. Further instructions with video tutorials can be found here : https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/ License All data is published under the CC-BY 4.0 license. The code is under the GNU General public license

Microsoft 365 is used by over two million companies worldwide, with over one million customers in the United States alone using the office suite software. Office 365 is the brand name previously used by Microsoft for a group of software applications providing productivity related services to its subscribers. Office 365 applications include Outlook, OneDrive, Word, Excel, PowerPoint, OneNote, SharePoint and Microsoft Teams. The consumer and small business plans of Office 365 were renamed as Microsoft 365 on 21 April, 2020. Global office suite market share  An office suite is a collection of software applications (word processing, spreadsheets, database etc.) designed to be used for tasks within an organization. Worldwide market share of office suite technologies is split between Google’s G Suite and Microsoft’s Office 365, with G Suite controlling around 45 percent of the global market and Office 365 holding around 26 percent. This trend is similar across most worldwide regions.

Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Species are known by many different names. The TaxonConcept Knowledge Base provides informative Linked Open Data URI's for species concepts that improve the quality and stability of links between a species and data about that species. There are currently 108,175 species concepts and a and 1,000 records for species occurrences. There are also a few examples of references, and image galleries. I have added links below for an insect, mammal, bird and tree to serve as examples of the interlinking etc. The occurrence records are interlinked with GeoNames. A species can have several different classifications, for instance it's classification in NCBI and DBpedia, are different. To allow multiple classifications, the species model is separate from any specific classification. However many want these two aspects connected together. To make this easy, I have created an additional owl:sameAs RDF dump file which makes the #Taxonomy the same as the #Species. This is particularly useful for browsing SPARQL query results with Microsoft Pivot. In addition, it is also possible to create similar mapping files that can be used to tie the species to alternative classifications. The data set and related vocabularies have been changed as of June 11, 2013. See the sitemap.xml or void file for the full list of RDF dumps

This is a Microsoft Access database of imagery, drawings, and photos accompanying Excavations at Tall Jawa, Jordan: Volume 3, The Iron Age Pottery by P.M. Michèle Daviau. The text and database present a detailed typology of the Iron Age pottery excavated from 1989 to 1995. Together, they represent an in-depth analysis of the forming techniques employed to make each type of vessel from bowls to colanders, cooking pots to pithoi. The digital archive is a work in progress by the author. The archive currently holds the collection for Excavation Field D. Upon completion, it will include seven collections, each one consisting of a database of diagnostic sherds and vessels as well as the images of these pots as .tiff files. Databases are related to excavation fields and are designed for meaningful searches: A, B, C-east, C-west, A-east (associated with C-west), D and E.

As Microsoft have announced that they are shutting down their DataMarket service, the API access currently at https://datamarket.azure.com/dataset/datagovuk/metofficeweatheropendata will no longer be available after March 31st 2017. The interface gives access to three datasets, hourly observations for approximately 150 UK observing stations, daily site specific and 3 hourly site specific forecasts for approximately 5000 UK locations. Both the 3 hourly and daily forecast datasets provide forecasts out to 5 days with updates issued hourly. Daily forecasts provide data for day and night using the following data time intervals. · Weather symbols: Day – Sunrise to sunset, Night Sunset to Sunrise · Temperature: Max – Maximum during 0600-18:00, Minimum during 18:00-06:00 · All other parameters are calculated for midday or midnight. Hourly observation reports as recorded in real time by the Met Office UK Monitoring System. It should be noted that sites will only report parameters based on the instrumentation installed at each site and we only make available those parameters published on the Met Office website. Observations are subject to final quality control by the Met Office after publication by data.gov.uk, any changes made will not be retrospectively applied to this dataset. The Chancellor's Autumn Statement 2011 announced that this release is open (OGL) and unrestricted (bulk download API): https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/61959/Further_detail_on_Open_Data_measures_in_the_Autumn_Statement_2011.pdf which is different to the access provided by the Met Office directly.

In the second quarter of 2024, revenues of Amazon Web Services (AWS) rose to 19 percent, the highest since Q2 of 2022. AWS is one of Amazon’s strongest revenue segments, generating 90 billion U.S. dollars in 2023 net sales, up from 80 billion U.S. dollars in 2021. Amazon Web Services Amazon Web Services (AWS) provides on-demand cloud platforms and APIs through a pay-as-you-go-model to customers. AWS launched in 2002 providing general services and tools and produced its first cloud products in 2006. Today, more than 175 different cloud services for a variety of technologies and industries are released already. AWS ranks as one of the most popular public cloud infrastructure and platform services running applications worldwide in 2020, ahead of Microsoft Azure and Google cloud services. Cloud computing Cloud computing is essentially the delivery of online computing services to customers. As enterprises continually migrate their applications and data to the cloud instead of storing it on local machines, it becomes possible to access resources from different locations. Some of the key services of the AWS ecosystem for cloud applications include storage, database, security tools, and management tools. AWS is among the most popular cloud providers Some of the largest globally operating enterprises use AWS for their cloud services, including Netflix, BBC, and Baidu. Accordingly, AWS is one of the leading cloud providers in the global cloud market. Due to its continuously expanding portfolio of services and deepening of expertise, the company continues to be not only an important cloud service provider but also a business partner.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

In 2024, enterprise spending on cloud infrastructure services amounted to 330 billion U.S. dollars, a growth of 60 billion U.S. dollars compared to the previous year. The growing market for cloud infrastructure services is driven by organizations' demand for modern networking, storage, and databases solutions. Increased spending on cloud services, mainly on platform as a service The platform as a service (PaaS) segment, which includes analytics, database, and internet of things (IoT) has the highest growth rate within the cloud infrastructure services market. The managed private cloud services share declined in comparison. Infrastructure as a service (IaaS) remained relatively steady, with companies like Amazon Web Services and Microsoft dominating the market. However, software as a service (SaaS) is not included, which itself continues to experience growth in end-user spending worldwide. Data center spending declined in 2020 Enterprise spending on data center hardware and software, on the other hand, began to slightly decline after several years of steady growth. Data center hardware and software encompasses spending on servers, networking, storage, and security software. Because data centers store proprietary or sensitive data, sites are secured by specific software. This includes splitting networks into security zones, for example. Other methods for ensuring security are using tools to scan applications and code before deployment to spot malware or vulnerabilities.