Unified ICM/Unified CCE software uses information in the central database to determine how to route N8NN calls, including information about telephone system configuration and routingscripts. The local database also contains tables of real-time information that describe activity at the callcenters. Historical information is stored in the central database.

Oceanographic data in high latitudes are sparse in both space and time. Most of these data are publicly available from different online archives. They often contain redundant profiles and data of different quality. To date, none of these archives offers a complete collection of all available temperature and salinity (T/S) measurements in the Arctic Ocean with a uniform quality level. We therefore compiled UDASH, a comprehensive hydrographic database of the Arctic Ocean, which aims at including all publicly available data. It so far consists of 288 532 quality-checked oceanographic profiles between 1980 and 2015, starting at 65°N.

Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, the USGWD comprises over 14.2 million well records with attributes such as well purpose, location, depth, and capacity for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States.

The files correspond to the rupture traces, slip measurement points and earthquake information of the Surface rupture database published in the paper "A Worldwide and Unified Database of Surface Ruptures (SURE) for Fault Displacement Hazard Analyses" by Stéphane Baize, Fiia Nurminen, Alexandra Sarmiento, Timothy Dawson, Makoto Takao, Oona Scotti, Takashi Azuma, Paolo Boncio, Johann Champenois, Francesca R. Cinti, Riccardo Civico, Carlos Costa, Luca Guerrieri, Etienne Marti, James McCalpin, Koji Okumura, and Pilar Villamor in Seismological Research Letters (doi: 10.1785/0220190144).

The size and share of the market is categorized based on Type (Block-based, File-based) and Application (Large Enterprise, SME) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Stores information around continuing disability reviews.

During a survey carried out mid-2023, 77 percent of responding customer data platform users stated that unified view one of three benefits they expected from a CDP. Analysis ranked second, named by 62 percent of the interviewed.

School Districts data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain three area geographic files of boundaries for elementary school districts, secondary school districts, and unifed school districts, each with associated Census and American Community Survey demographic data.

During a 2020 survey carried out among marketing technology decision makers from the United States, 47 percent of respondents stated they had already unified first-party customer profile data with their customer data platform; further 18 percent said they were hoping to unify such data with their CDP.

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. The Unified Library Database, or UniLib, takes a library-level view of the EST and SAGE libraries present in NCBI's dbEST, UniGene and SAGEmap resources. This database was initially developed by NCBI in order to track and annotate libraries being generated by NCI's CGAP project. The query bar of the UniLib Library browser provides the most friendly way to navigate through these libraries. When matches to the Library browser query are returned as summaries, full library records can be retrieved through the linked Record retriever.

The Berkeley Unified Numident Mortality Database (BUNMD) is a cleaned and harmonized version of the NARA Numident file. The BUNMD is a single standalone file comprised of the most informative parts of the 60+ application, claim, and death files released by the National Archives. All records are linked by Social Security Number. Variables of interest include race, place of birth, state in which the Social Security card was applied for, and ZIP Code of residence at the time of death. Two supplementary data files are available. The BUNMD supplemental geography file contains additional variables with place of birth and/or place of death information, such as county of birth and death, for a subset of the BUNMD. The BUNMD cleaned names file contains cleaned and standardized names (first, middle, last) for individuals and their parents. The BUNMD sibling datasets identify sibling groups in the BUNMD

ATP3 Unified Field Study DataThe Algae Testbed Public-Private Partnership ATP3 was established with the goal of investigating open pond algae cultivation across different geographic climatic seasonal and operational conditions while setting the benchmark for quality data collection analysis and dissemination. Identical algae cultivation systems and data analysis methodologies were established at testbed sites across the continental United States and Hawaii. Within this framework the Unified Field Studies UFS were designed to characterize the cultivation of different algal strains during all 4 seasons across this testbed network. The dataset presented here is the complete curated climatic cultivation harvest and biomass composition data for each season at each site. These data enable others to do in-depth cultivation harvest techno-economic life cycle resource and predictive growth modeling analysis as well as develop crop protection strategies for the nascent algae industry.NREL Sub award Number DE-AC36-08-GO28308

26 Global import shipment records of Unified Ip with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The global in-memory database market size was valued at USD 10.5643 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.19% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of in-memory databases in various industries to improve data processing speed and performance. In-memory databases store data in the computer's main memory (RAM) instead of on a physical disk, which allows for faster data access and retrieval. Key market drivers include the growing volume of data, the need for real-time data analysis, and the increasing adoption of cloud computing. The growing volume of data, often referred to as "big data," is a significant factor driving market growth. The need for real-time data analysis is another key driver, as in-memory databases can provide faster data access than traditional databases. The increasing adoption of cloud computing is also driving market growth, as cloud-based in-memory databases offer scalability and flexibility. Recent developments include: March 2023: SAP revealed SAP Datasphere, the company's next-gen data management system. It gives customers easy access to business-ready data across the data landscape. SAP also announced strategic agreements with top data and AI companies, including Collibra NV, Confluent Inc., Databricks Inc., and DataRobot Inc., to improve SAP Datasphere and allow organizations to build a unified data architecture that securely combines SAP software data and non-SAP data., June 2023: IBM has released a new tool to aid corporations in monitoring their carbon footprint pollution across cloud services and improve their sustainability as they move to hybrid and multi-cloud environments. The IBM Cloud Carbon Calculator, an AI-powered dashboard, is now available to everyone. It can help clients access emissions data for various IBM Cloud tasks, such as AI, high-performance computing (HPC), and financial services., SingleStoreDB for December 2022 was announced last year by IBM and SingleStore. With IBM introducing SingleStoreDB as a solution, businesses are now moving forward in their strategic relationship to deliver the quickest, most scalable data platform that supports data-intensive programs. For Azure, AWS, and Microsoft Azure marketplace, IBM has released SingleStoreDB as a service., In April 2022, McObject issued the eXtremeDB/rt database management system (DBMS) for Green Hills Software’s Integrity RTOS. The first-ever commercial off-the-shelf (COTS) real-time DBMS satisfying basic criteria of temporal and deterministic consistency in data is known as eXtremeDB/rt. It was initially conceived and built as an integrated in-memory database system for embedded systems., November 2022: Redis, provider of real-time in-memory databases, and Amazon Web Services have formed a multi-year strategic alliance. It is a networked open-source NoSQL system that stores data on disk for durability before moving it to DRAM as required. As such, it can be used as a message broker cache, streaming engine, or database., December 2022: The largest Indian stock exchange, National Stock Exchange, opted for Raima Database Manager (RDM) Workgroup 12.0 In-Memory System as its foundational component for upcoming versions of its trading platform front-end called National Exchange for Automated Trading (NEAT)., On January 13th, 2021, Oracle launched Oracle Database 21c – the latest version of the world’s leading converged database available on Oracle Cloud with the Always Free tier of Oracle Autonomous Database included. It includes more than two hundred new features, according to Oracle’s press release, including immutable blockchain tables; In-Database JavaScript; native JSON binary data type; AutoML for in-database machine learning (ML); persistent memory store; enhancements, including improvements regarding graph processing performance that support sharding, multitenant, and security., Stanford engineers have developed a new chip to increase the efficiency of AI computing in August 2022. Stanford engineers have created a more efficient and flexible AI chip that could bring the power of AI into tiny edge devices., In-Memory Database Market Segmentation,

Relational

NoSQL

NewSQL

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Online Analytical Processing (OLAP)

Online Transaction Processing (OLTP)

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Transaction

Reporting

Analytics

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North America

US

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Australia

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Australia

Rest of Asia-Pacific

Rest of the World

Middle East

Africa

Latin America

, . Potential restraints include: Security And Data Privacy Concerns 26.

Provides ad hoc and standard report data to support workload control and counts for Title II post-entitlement changes, reinstatements, payments, and checks.

Current agency tracking of all appeals information from reconsiderations through appeals council. Includes all appeals from Title 2, Title 16, and some Medicare Part D.

172 Global import shipment records of Unified,modem with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

See "01_ASDN_readme.txt" (under "Download Data" tab) for data author and contact information. Field data on shorebird ecology and environmental conditions were collected from 1993-2014 at 16 field sites in Alaska, Canada, and Russia. Data were not collected in every year at all sites. Studies of the population ecology of these birds included nest-monitoring to determine timing of reproduction and reproductive success; live capture of birds to collect blood samples, feathers, and fecal samples for investigations of population structure and pathogens; banding of birds to determine annual survival rates; resighting of color-banded birds to determine space use and site fidelity; and use of light-sensitive geolocators to investigate migratory movements. Data on climatic conditions, prey abundance, and predators were also collected. Environmental data included weather stations that recorded daily climatic conditions, surveys of seasonal snowmelt, weekly sampling of terrestrial and aquatic invertebrates that are prey of shorebirds, live trapping of small mammals (alternate prey for shorebird predators), and daily counts of potential predators (jaegers, falcons, foxes). Detailed field methods for each year are available in the ASDN_protocol_201X.pdf files. All research was conducted under permits from relevant federal, state and university authorities. Potential users of these data should first contact the relevant data author(s), listed below. This will enable coordination in terms of updates/corrections to the data and ongoing analyses. Key analyses of the data are in progress and will be included in the theses and dissertations of graduate students who collected these field data. Please acknowledge this dataset and the authors in any analysis, publication, presentation, or other output that uses these data. If you use the full dataset, we suggest you cite it as: Lanctot, RB, SC Brown, and BK Sandercock. 2016. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. If you use data from only one or a few sites, we suggest you cite data for each site as per this example, using the corresponding site PIs as the authors: Lanctot, RB and ST Saalfeld. 2016. Barrow, 2014. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. Note that each updated version of the full dataset has its own unique DOI. Disclaimers: The dataset is distributed “as is” and with absolutely no warranty. The data providers have invested considerable effort to ensure that the data are of highest quality, but it is possible that undetected errors remain. Data have been processed with several steps for quality assurance, but the data providers accept no liability or guarantee that the data are up-to-date, correct, or complete. Access to data is provided on the understanding that the data providers are not responsible for any damages from inaccuracies in the data. Note: An up-to-date version of data from Barrow/Utqiagvik, including corrected and more recent data, is now housed here: https://arcticdata.io/catalog/view/doi:10.18739/A2VT1GP7Q . Please contact the relevant site PIs to seek recent data (after 2014) from any other site.

Provides MI/BI for the Title XVI post eligibility workloads. Also houses Starz & Stripes data, which is used for workload control of redeterminations and limited issues.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset was created to establish a unified streamflow dataset from the source dataset provided by the BoM. The data will be used for the summarising the streamflow characteristics in …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset was created to establish a unified streamflow dataset from the source dataset provided by the BoM. The data will be used for the summarising the streamflow characteristics in the South Sydney Basin context report and surface water and ground water modelling (if required). Dataset History Data were extracted from the raw data .csv format to corresponding unified .csv files for surface water sites within the South Sydney Basin. The process steps are as follows To move one day backward to match precipitation data since the original 9:00am data is for the period of the current 10:00 am to next 9:00 am To identify gauge stuck issue To identify data linear interpolation issue To regard the issue data as missing data To generate streamflow data with the unified quality codes: (1: Good; 2: Fair; 3: Poor; 4: Unverified; 5: Non-conforming; 6: Missing) To separate daily streamflow into baseflow and quick flow using the standard filtering method (Lyne and Hollick (1979)). The data was created in MATLAB using scripts and functions. Dataset Citation Bioregional Assessment Programme (2015) SYD ALL Unified Stream Gauge Data v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fbcf2377-fc55-489e-a432-c7fa430efbd6. Dataset Ancestors Derived From SYD ALL Raw Stream Gauge Data BoM v01