Projections of commercial airline flight schedules worldwide. Describe characteristics of each flight such as: departure and arrival airports, flight times, carrier, fares, capacity, and more. Projections are made at the beginning of every time period (month or year) and project the schedules for that time period until the next update is received. Data from 1979-March 1987 are available monthly. Data from 1987 onward are available annually.DATA AVAILABLE FOR YEARS: 1978-2024 (see Note for specifics)

Data sets are available for consumer complaints, workplace complaints and citations, debarred contractors, bid protest decisions, and pending Open Meeting Law complaints.

Copies of formal Iowa Attorney General Opinions issued after 1898 and informal Letter Opinions issued by the Attorney General’s Office are available through this resource. The link below contains links to other Web sites operated by West Group, Inc., which is responsible for the privacy practices and policies of those sites. Please review the privacy policy of each site you visit. West Group, Inc. has agreed not to collect personally identifiable information from users who access the Iowa Attorney General Opinion Database.

oag-cs, oag-eng, oag-chem are new heterogeneous networks composed of subsets of the Open Academic Graph (OAG). Each of the datasets contains papers from three different subject domains -- computer science, engineering, and chemistry. These datasets also contain four types of entities -- papers, authors, institutions, and fields of study. Each paper is associated with a 768-dimensional feature vector generated from a pre-trained XLNet applying on the paper titles. The representation of each word in the title are weighted by each word's attention to get the title representation for each paper. Each paper node is labeled with its published venue (paper or conference). We split the papers published up to 2016 as the training set, papers published in 2017 as the validation set, and papers published in 2018 and 2019 as the test set. The publication year of each paper is also included in these datasets. This means those datasets can also be converted to use the publication year as class labels.

OAG Aviation Worldwide, UAB financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

The global Aviation Analytics market is experiencing robust growth, projected to reach a substantial size by 2033, driven by a compound annual growth rate (CAGR) of 13.35% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing operational complexities within the aviation industry necessitate sophisticated analytical tools for enhanced decision-making. Airlines and airports are leveraging analytics for improved efficiency in areas such as fuel management, revenue optimization, and risk mitigation. Secondly, the burgeoning adoption of advanced technologies like AI and machine learning is further propelling market growth. These technologies enable predictive analytics, providing airlines and airports with valuable insights into passenger behavior, operational performance, and potential disruptions. Furthermore, the rising need for enhanced customer experience and streamlined operations is driving demand for solutions that offer comprehensive data analysis and actionable insights. Segmentation reveals strong growth across various end-users (airlines, airports), business functions (sales & marketing, finance, MRO operations, supply chain), and applications (risk management, inventory management, fuel management, revenue management, customer analytics). North America and Europe currently hold significant market shares, but the Asia-Pacific region is poised for rapid expansion due to increasing air travel and infrastructure development. The competitive landscape is marked by a mix of established players like L3Harris Technologies, Honeywell International, GE Digital, IBM, and Collins Aerospace, alongside specialized aviation analytics providers. These companies are continuously innovating to provide comprehensive and integrated solutions catering to the diverse needs of the aviation sector. The market's future trajectory indicates a continued shift towards cloud-based solutions and advanced analytics capabilities, driven by the need for real-time insights and scalable infrastructure. Challenges remain, including data integration complexities, cybersecurity concerns, and the need for skilled professionals to interpret and leverage the analytical outputs effectively. Nevertheless, the overall growth outlook for the Aviation Analytics market remains positive, presenting substantial opportunities for established players and new entrants alike. This comprehensive report provides a detailed analysis of the Aviation Analytics Market, offering invaluable insights for stakeholders across the aviation ecosystem. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report leverages historical data (2019-2024) to project future market trends and growth. The market is segmented by end-user (Airlines, Airports), business function (Sales & Marketing, Finance, MRO Operations, Supply Chain), and application (Risk Management, Inventory Management, Fuel Management, Revenue Management, Customer Analytics). The report values the market in millions of USD and analyzes key players like L3Harris Technologies, Honeywell, and Boeing, among others. Recent developments include: July 2023: Noida International Airport in India selected SITA's Airport Management System to help automate and streamline the operations of the airport., June 2022: LexisNexis Risk Solutions launched flight status data tracking using Chainlink Node.. Key drivers for this market are: Increase in Internet of Things (IoT) and Autonomous Systems, Rise in Demand for Military and Defense Satellite Communication Solutions. Potential restraints include: Cybersecurity Threats to Satellite Communication, Interference in Transmission of Data. Notable trends are: Airlines Segment to Witness Highest Growth During the Forecast Period.

Oag Technology Company Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

OAG Dynamic Schedules Market Outlook

According to our latest research, the global OAG Dynamic Schedules market size reached USD 1.34 billion in 2024, reflecting robust adoption across the aviation and travel sectors. The market is projected to expand at a CAGR of 10.8% during the forecast period, reaching USD 3.16 billion by 2033. This growth is primarily driven by increasing demand for real-time scheduling, operational efficiency, and digital transformation in airline and airport operations. As per our 2025 research, the OAG Dynamic Schedules market continues to benefit from technological advancements and the growing emphasis on passenger experience and operational agility.

A significant growth factor for the OAG Dynamic Schedules market is the accelerating digitalization of the aviation industry. Airlines and airports are increasingly leveraging dynamic scheduling solutions to optimize flight operations, manage disruptions, and enhance passenger satisfaction. The need for real-time data integration and predictive analytics has never been more crucial, especially in the wake of fluctuating travel demand and heightened competition. Dynamic scheduling enables stakeholders to make informed decisions quickly, minimize delays, and respond proactively to operational changes, thus driving widespread adoption across the sector. The rise of artificial intelligence and machine learning is further enriching the capabilities of these platforms, enabling predictive maintenance, resource allocation, and demand forecasting.

Another key driver is the increasing complexity of airline networks and the growing interconnectivity between airlines, airports, and travel management companies. As the global air travel ecosystem becomes more interconnected, the need for seamless and dynamic schedule management has become paramount. OAG Dynamic Schedules solutions facilitate the integration of multiple data sources, such as weather, air traffic control, and passenger information systems, to provide a unified and actionable view of operations. This holistic approach is essential for managing code-share agreements, alliance operations, and multi-modal travel experiences, thereby ensuring operational efficiency and improved service delivery. The ongoing expansion of low-cost carriers and the emergence of new airline alliances are also fueling demand for advanced scheduling solutions.

Furthermore, regulatory pressures and the need for compliance with international standards are prompting airlines and airports to adopt sophisticated scheduling systems. Regulatory bodies require accurate and up-to-date schedule information for monitoring, reporting, and safety purposes. OAG Dynamic Schedules solutions offer automated compliance checks, real-time updates, and seamless reporting capabilities, reducing the administrative burden on operators. Additionally, the increasing focus on sustainability and resource optimization is encouraging the adoption of dynamic scheduling, as it enables better fuel management, optimized crew scheduling, and reduced operational wastage. These factors collectively contribute to the sustained growth of the OAG Dynamic Schedules market.

From a regional perspective, North America currently leads the OAG Dynamic Schedules market, followed closely by Europe and Asia Pacific. The United States, in particular, is witnessing strong adoption due to its large and complex airline networks, high passenger volumes, and advanced technological infrastructure. Europe is also experiencing significant growth, driven by regulatory initiatives and the presence of major international hubs. The Asia Pacific region is emerging as a high-growth market, supported by rapid air travel expansion, infrastructure development, and increasing investments in digital transformation. Latin America and the Middle East & Africa are gradually catching up, with investments in airport modernization and airline fleet expansion contributing to market growth.

Component Analysis

The OAG Dynamic Schedules market by component is

Modelo Digital Batimétrico

Using a combination of OAG flight schedule and ch-aviation fleet data, Capacities - Scheduled provides an overview of future flights scheduled per calendar day with a breakdown of seat capacity for five cabin classes (Economy, Economy Plus/Comfort, Premium Economy, Business, First) by operator and route (Continent, Country, Subdivision, Metro Group, Airport).

The data set is updated weekly.

The sample data shows capacity figures for Alaska Airlines, Swiss, and Horizon Air for one week.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=capacities_scheduled/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/capacities-scheduled/

Attorney-General's Department Annual Report Data - annual spend on contractors presented as a time series.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Complaint data from consumer complaints filed with the Consumer Protection Division. The existence of a complaint is not evidence of wrongdoing.

Federal Attorney General Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

O A G Stellar Enterprise Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Comprehensive dataset containing 84 verified Attorney General locations in United States with complete contact information, ratings, reviews, and location data.

OAGT is a paper topic dataset consisting of 6942930 records which comprise various scientific publication attributes like abstracts, titles, keywords, publication years, venues, etc. The last two fields of each record are the topic id from a taxonomy of 27 topics created from the entire collection and the 20 most significant topic words. Each dataset record (sample) is stored as a JSON line in the text file.

The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released
under ODC-BY license.

This data (OAGT Paper Topic Dataset) is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/).

If using it, please cite the following paper:

Erion Çano, Benjamin Roth: Topic Segmentation of Research Article Collections. ArXiv 2022, CoRR abs/2205.11249, https://doi.org/10.48550/arXiv.2205.11249

The development plan (BPL) contains the legally binding determinations for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “Development Plan OAG-Gelaende incl. 1. Modification of the city of Bühl from XPlanung 5.0. Description: OAG-Gelaende incl. 1. Change.