The structured data management software market is experiencing robust growth, driven by the increasing need for organizations to efficiently manage and analyze ever-expanding data volumes. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching approximately $150 billion by the end of the forecast period. This expansion is fueled by several key factors. The rise of big data analytics, cloud computing adoption, and the stringent regulatory requirements for data governance are all compelling businesses to invest in sophisticated structured data management solutions. Furthermore, the growing demand for real-time data processing and improved data security contribute to the market's dynamism. Major players like Google, Salesforce, and IBM are actively shaping the market landscape through continuous innovation and strategic acquisitions. The market is segmented by deployment (cloud, on-premise), organization size (small, medium, large), and industry vertical (finance, healthcare, retail, etc.), presenting diverse growth opportunities across various niches. Competition is fierce, with both established tech giants and specialized vendors vying for market share. Despite the positive outlook, challenges remain, including the complexity of integrating these solutions with existing systems and the need for skilled professionals to manage these complex technologies. The competitive landscape is characterized by a mix of established players and emerging vendors. While giants like Google, Salesforce, and IBM leverage their extensive resources and existing customer bases to maintain market dominance, agile smaller companies are focusing on niche solutions and innovative technologies to capture market share. The global distribution of the market is expected to show strong growth across North America and Europe, driven by high levels of technology adoption and established digital infrastructure. However, growth opportunities also exist in rapidly developing economies in Asia-Pacific and Latin America as businesses in these regions accelerate their digital transformation initiatives. The ongoing development of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), integrated into structured data management software, is a significant catalyst for future market growth, enabling more sophisticated data analysis and improved decision-making.

A complete list of live websites using the Structured Data technology, compiled through global website indexing conducted by WebTechSurvey.

The size of the Structured Data Management Softwares market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Brood structure data for: Asymmetric sibling rivalry extends to hosts and brood parasites project. Includes Cowbird and redwing parasitized/unparasitized data and statistics.

Structured Data Archiving And Application Retirement Market size was valued at USD 6.43 Billion in 2024 and is projected to reach USD 14.413 Billion by 2032, growing at a CAGR of 9.5% from 2026 to 2032.

Structured Data Archiving And Application Retirement Market Drivers

Regulatory Compliance Requirements: Organizations in a variety of sectors must adhere to legal requirements pertaining to data archiving and preservation. Structured data must be kept on file for legal, auditing, and compliance reasons, according to regulations. Data from defunct or decommissioned applications must be archived by organizations in order to comply with laws like Sarbanes-Oxley (SOX), GDPR, HIPAA, and others. The demand for application retirement and structured data archiving solutions is driven by the necessity to comply with regulations.

Cost Optimization and Efficiency: By retiring old programs that are no longer in active use, businesses aim to reduce IT expenses and streamline processes. Updating out-of-date apps requires resources for infrastructure, upkeep, and license. Organizations can enhance operational efficiency, save storage costs, and decommission outdated applications by using structured data archiving and application retirement solutions. These services also free up resources for more strategic projects.

Data Governance and Risk Management: Organizations must manage data at every stage of its lifespan, including the archiving and retirement procedures, in order to implement effective data governance standards. Solutions for structured data archiving make it easier to manage structured data assets by offering features like data classification, audit trails, retention policies, and access controls. Through the implementation of application retirement and organized data archiving methods, organizations can reduce the risks associated with data loss, security breaches, and unauthorized access.

The size of the Structured Data Archiving (SDA) Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Structure measurements of individual woody and non-woody plants, mapped positions of qualifying woody and non-woody plants, and metadata required to draw inference from individual measurements at the plot scale.

Under the direction and funding of the National Cooperative Mapping Program with guidance and encouragement from the United States Geological Survey (USGS), a digital database of three-dimensional (3D) vector data, displayed as two-dimensional (2D) data-extent bounding polygons. This geodatabase is to act as a virtual and digital inventory of 3D structure contour and isopach vector data for the USGS National Geologic Synthesis (NGS) team. This data will be available visually through a USGS web application and can be queried using complimentary nonspatial tables associated with each data harboring polygon. This initial publication contains 60 datasets collected directly from USGS specific publications and federal repositories. Further publications of dataset collections in versioned releases will be annotated in additional appendices, respectfully. These datasets can be identified from their specific version through their nonspatial tables. This digital dataset contains spatial extents of the 2D geologic vector data as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports, any digital model data released as a separate publication, and input type of vector data, using several classification schemes. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Rang ...

These data are a compilation of fishway structures collected by the Atlantic States Marine Fisheries Commission state representatives at the request of the U.S. Geological Survey. The variables included within this dataset range from locality information and structure metadata (eg. latitude/longitude and year of construction) to metrics specifically about the fishway structure (eg. fishway width). The dataset ranges in dates of construction from 1882 to 2020 and includes fishways from all states on the eastern coast of the United States.

Demonstration data set for data prior to the changes made on 20 April 2017: https://data.sfgov.org/City-Infrastructure/Case-Data-from-San-Francisco-311-SF311-/vw6y-z8j6

This dataset covers annotations of Framing Structure for panels in the 1,030 comics in the TINTIN Corpus. This includes framing categories and characters per panel. For information about the annotation scheme, see: Cohn, Neil. 2024. "Morphology: Framing Structure v.2." In TINTIN Project Documentation: Visual Language Theory Annotation Guides, edited by Neil Cohn, Irmak Hacımusaoğlu, Bien Klomberg and Ana Krajinović. Tilburg University: Visual Language Lab Resources http://www.visuallanguagelab.com/tintin.

The size of the Structured Data Archiving and Application Retirement Software market was valued at USD 71 million in 2024 and is projected to reach USD 134.02 million by 2033, with an expected CAGR of 9.5 % during the forecast period.

Layer of Structures (road and pedestrian bridges, rail bridges, sign gantries and tunnels), in Western Australia.This layer provides general inventory information for all road or pedestrian Structures on State roads, Local roads, or Department of Biodiversity Conservation and Attractions (DBCA) roads.A structure is defined as the portion of the carriageway that carries vehicular, pedestrian, and bicycle traffic over an obstruction such as a watercourse, another road, or railway line. Note on Rail Bridges: Rail bridges are included only in selected circumstances. This layer shows the location of Structures on all public access roads in Western Australia and is provided for information only. You are accessing this data pursuant to a Creative Commons (Attribution) Licence which includes a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence, the following notice must be included when you share the Licensed Material:“The Commissioner of Main Roads is the creator and owner of the data and Licensed Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.” Creative Commons CC BY 4.0 Update FrequencyUpdates to ArcGIS layer data are triggered upon changes to data in IRIS and are available the next business day.Data Domain Steward:Structures Design and Standards EngineerData Custodian:Data and Systems ManagerOperational Data Steward:Structures Asset and Condition Engineer

As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. Although online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we study a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.

This file geodatabase contains datasets that are physical feature updates. Feature classes include buildings, miscellaneous (locks and dams, tanks, water towers), parking, and recreation structures.

The following links can be used to obtain individual metadata pages:

Building: struc_building.html
Miscellaneous: struc_miscellaneous.html
Parking: struc_parking.html
Recreation: struc_recreation.html

These data are for the bioRxiv publication entitled: Structural variation in Drosophila melanogaster spermathecal ducts and its association with sperm competition dynamics.

found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.

Structured data vectors utilized in machine learning algorithms.