FactEngine is dedicated to providing product and services for the aerospace industry. The aerospace industry is characterised by large data models used to manage air, space and ground based system (e.g. satellites, aeroplanes, ground control systems) and the outputs of use of those products (e.g. data transmitted by satellites and aeroplanes to land and space/air-based receivers). Because of the safety and reliability requirements of the industry, reams of data are stored about nearly every part used in the construction of aerial and space vehicles. Satellite System Reference Databases need to be created in a timely manner to support the successful launch and operation of satellite systems.

Much of the data used within the aerospace industry is electronically digitised and conceptual modelling of that data is important. The accuracy of each conceptual model is increased the more that electronically digitised information becomes an integral part of the design and operational aspects of aerial vehicles.

Safe and reliable data use within the airline industry has long been understood to be of the utmost importance. Airline disasters and costly mistakes have been directly attributed to incorrect data entry and the systems that allowed incorrect data entry [1],[2].

Within the space industry, poor conceptual modelling of data and/or errant use of modelled data has resulted in costly mission failures [3] [4].

The aim of conceptual modelling is to reduce errors in underlying physical implementations of the data models produced, while maximising the utility of the data stored.

FactEngine's Boston conceptual modelling software is designed as best-in-class conceptual modelling software from a company dedicated to working with the aerospace industry.

Satellite System Reference Databases

Satellite System Reference Databases (SRDBs) are essential tools in the aerospace industry. They provide a centralized repository of information on objects in space, such as satellites, debris, planets, and asteroids. This information is used to simulate and model various space operations, including satellite communications, navigation, and collision avoidance.

A typical SRDB includes information on the orbit parameters of objects in space, such as their altitude, inclination, eccentricity, and argument of periapsis. It also includes data on the physical characteristics of objects, such as their size, shape, mass, and composition. The database may also contain information on the history of space missions, such as launch dates, mission objectives, and mission status.

SRDBs are used for design, control, and management of space systems. For example, the information in the database can be used to design satellite orbits, and plan communication networks. The data can also be used to control the movement of spacecraft and ensure they are operating within safe parameters.

One use of an SRDB is to enable scientists and engineers to simulate and predict the behaviour of satellites in space. For example, an SRDB can be used to predict the trajectory of a satellite and determine if it will collide with other objects in orbit. This information can then be used to plan manoeuvres to avoid collisions and ensure the safety of space operations.

SRDBs provide a centralized source of information for satellite systems. Instead of each organization having its own database of space object information, SRDBs provide a common platform for sharing and accessing this data. This helps to improve collaboration and coordination among different organizations involved in space operations.

The development of an SRDB is a complex process that requires expertise in data management, software engineering, and space physics. To ensure the accuracy and reliability of the database, it is essential to use high-quality conceptual modelling techniques. This involves identifying entities, relationships, and attributes of objects relating to the ground, launch and satellite/space systems and creating a clear and organized schema of the database structure.

The accuracy of the SRDB is also essential to its effectiveness. Objects in space are constantly moving and changing, and the database must be updated regularly to reflect these changes. This requires a robust data management system that can track changes and updates to the database in real-time.

The development of an SRDB requires expertise in data management and space physics and involves the use of high-quality conceptual modeling techniques. The accuracy of the SRDB is essential to its effectiveness and requires a robust data management system.

1:02:21

Aerospace & Object-Role Modeling

A video showing how Object-Role Modeling is used in the aerospace industry. Legals: Images under license. This video affirms no working relationship or endorsement of, for or by any other person or entity and provides the opinions of FactEngine, Australia, only. Any and all logo use here under fair use legislation, and/or as happenstance of presenting commentary, and in no way implies endorsement of, for or by any entity. Errata: Verbalisation read out should read: For each SubserviceType, exactly one of the following holds: - some StandardSubserviceType is that SubserviceType - some MissionSpecificSubserviceType is that SubserviceType

34:13

Aerospace And Object Role Modeling Pt 1

A video on the user of Object-Role Modeling in the Aerospace industry. Pt 2 at: https://www.youtube.com/watch?v=WHw... Legals: Images under license. This video affirms no working relationship or endorsement of, for or by any other person or entity and is provides the opinions of FactEngine, Australia, only. Any and all logo use here under fair use legislation, and/or as happenstance of presenting commentary, and in no way implies endorsement of, for or by any entity.

16:23

Metadata Lineage in Boston

An introduction to Metadata Lineage in the Boston conceptual modelling software. Tracking the lineage of where the definition of model elements of your enterprise model emanates is an important process and tying it directly to your conceptual modelling tool of choice. Metadata Lineage empowers you to find the information you need to maintain your conceptual models. Metadata lineage includes the documentation and tracking of metadata of conceptual models to source documentation, such as cardinality rules and model element definitions. Throughout the lifecycle of a conceptual model Metadata Lineage enables quality data governance and understanding of your conceptual models. Legals: Images under license. This video affirms no working relationship or endorsement of, for or by any other person or entity and is provides the opinions of FactEngine, Australia, only. Any and all logo use here under fair use legislation, and/or as happenstance of presenting commentary, and in no way implies endorsement of, for or by any entity.

20:03

Knowledge Extraction & Metadata Lineage with Fact Engine Knowledge Language

Watch how to do Knowledge Extraction and Metadata Lineage with Fact Engine Knowledge Language (FEKL). You'll see how to: 1. Dynamically load models into Boston using FEKL; 2. Metadata Lineage in Boston; 3. Knowledge Extraction in Boston;

[1] Varig Flight 254, https://en.wikipedia.org/wiki/Varig_Flight_254

[2] "AirAsia captain entered wrong coordinates before aborted flight, ATSB finds", http://www.abc.net.au/news/2016-09-08/airasia-flight-human-error/7826216

[3] "Mars lander slammed into red planet after data glitch - CNN",  https://edition.cnn.com/2016/11/24/health/schiaparelli-cause-mars-crash/index.html

[4] "Russian satellite lost after being set to launch from wrong spaceport ...", https://www.theguardian.com/world/2017/dec/28/russian-satellite-lost-wrong-spaceport-meteor-m