Mike's Notes
This is where I will keep detailed working notes on creating Workspaces for Aviation. Eventually, these will become permanent, better-written documentation stored elsewhere. Hopefully, someone will come up with a better name than this working title.
This replaces coverage in Industry Workspace written on 13/10/2025.
Testing
The current online mockup is version 3 and will be updated frequently. If you are helping with testing, please remember to delete your browser cache so you see the daily changes. Eventually, a live demo version will be available for field trials.
Learning
(To come)
Why
(To come)
Resources
- https://www.blog.ajabbi.com/2025/10/industry-workspace.html
- https://en.wikipedia.org/wiki/List_of_aviation,_avionics,_aerospace_and_aeronautical_abbreviations
- https://veryon.com/company/integrations
- https://en.wikipedia.org/wiki/Flight_management_system
- https://en.wikipedia.org/wiki/Aircraft_registration
- https://en.wikipedia.org/wiki/Flight_number
- https://en.wikipedia.org/wiki/Airline_codes
- https://en.wikipedia.org/wiki/IATA_airport_code
- https://en.wikipedia.org/wiki/International_Civil_Aviation_Organization_airport_code
- https://en.wikipedia.org/wiki/Flight_information_display_system
- https://hdombrovskaya.wordpress.com/2021/01/22/a-training-database-available-now/
- https://www.partow.net/miscellaneous/airportdatabase/index.html#GlobalAirportDatabaseLicens
- https://openflights.org/data.html
- https://www.aircraftcompare.com/manufacturers/airbus/
- https://www.world-airport-codes.com/
- https://ext.eurocontrol.int/aixm_confluence/display/GEN/Overview
- https://en.wikipedia.org/wiki/Pushback_(aviation)
References
- Reference
Repository
- Home > Ajabbi Research > Library >
- Home > Handbook >
Last Updated
19/11/2025
Workspaces for Aviation
By: Mike Peters
On a Sandy Beach: 12/11/2025
Mike is the inventor and architect of Pipi and the founder of Ajabbi.
Open-source
This open-source SaaS cloud system will be shared on GitHub and GitLab.
Dedication
This workspace is dedicated to the life and work of ..
Change Log
Ver 3 includes aircraft, airport, airspace, and flight.
Existing products
- https://webmanuals.aero/
- https://traxxall.com/
- https://coflyt.com/
- https://www.aeronetsoftware.com/
- https://www.campsystems.com/
- https://veryon.com/
- https://airplanemanager.com/
- https://flightcert.com/
- https://www.ebiscloud.com/
- https://sparta.aero/
- https://www.ambryhill.com/
- https://gopreflight.co.nz/
- FlightRadar24
- https://www.simbrief.com/
- https://navigraph.com/
- https://asasoftware.aero/
- https://airwaysinternational.com/airspace-procedures/
- https://www.preflightaviation.com/
- https://www.velloxgroup.com/
- https://www.nseled.com/flight-information-display-systems/
- https://www.mvix.com/blog/the-basics-of-fids-features-capabilities-and-uses
Features
This is a basic comparison of features found in aviation
software.
[TABLE]
Data Model
wordsDatabase Entities
- Facility
- Party
- Party Relationship
- Party Role
- etc
Geodatabase
Airport Mapping Database (AMDB)
"An AMDB is a Geographic Information System (GIS) database of an airport
describing:
- the spatial layout of an airport;
- the geometry of features (e.g. runways, taxiways, buildings) described as points, lines and polygons;
- further information characterising the features and their functions which are stored as attributes (e.g. surface type, name/object identifier, runway slope).
- AMDB are used in a wide variety of applications but mostly in on-board applications such as Electronic Flight Bags (EFBs). These applications are intended primarily to improve the user’s situational awareness and/or to supplement surface navigation, thereby increasing safety margins and operational efficiency.
Multiple user groups, such as pilots, controllers, aerodrome managers,
aerodrome emergency/security personnel etc, can benefit from using
AMDBs.
Further Reading
- ICAO Annex 15 Chapter 11
- ICAO Annex 14 Chapter 2.1
- EUROCAE ED-99C (RTCA DO-272C) — User Requirements for Aerodrome Mapping Information
- EUROCAE ED-119B (RTCA DO-291B) — Interchange Standards for Terrain, Obstacle, and Aerodrome Mapping Data
- Eurocontrol AMDB website
Variables
Note that this simulation did not use realistic min, max, mean and std deviation for these calculations. The model itself was the result and will undergo further work and testing using industry data to refine it.
Source: Krombar.ai simulation platform (beta)
| Node Name | Type | Estimates / Formula |
|---|---|---|
| Scheduled Passengers | Input Variable | discrete_normal distribution |
| Landing & Departure Charge | Input Variable | Normal(μ=800.0, σ=593.0408874022407) |
| Security Screening Rate | Input Variable | beta distribution |
| Retail Participation Rate | Input Variable | beta distribution |
| Flight Departure Rate | Input Variable | beta distribution |
| Board at Gate | Calculation Step | round(Scheduled Passengers*Passenger show-up rate) |
| Check-in & Baggage Drop | Calculation Step | round(Passenger arrives at airport * Check-in Rate) |
| Passenger show-up rate | Input Variable | beta distribution |
| Passenger Fee | Input Variable | Normal(μ=20.0, σ=14.82602218505602) |
| Check-in Rate | Input Variable | beta distribution |
| Security Screening | Calculation Step | round(Check-in & Baggage Drop*Security Screening Rate) |
| Avg Retail Spend | Input Variable | Normal(μ=35.0, σ=37.065055462640046) |
| Staff Cost per Pax | Input Variable | Normal(μ=8.0, σ=5.930408874022408) |
| Staff Cost per Flight | Input Variable | Normal(μ=800.0, σ=593.0408874022407) |
| Ground Handling Cost per Flight | Input Variable | Normal(μ=1400.0, σ=889.5613311033611) |
| Aeronautical Revenue | Calculation Step | Flight Departs * Landing & Departure Charge + Passenger arrives at airport * Passenger Fee |
| Retail Revenue | Calculation Step | Airside waiting & retail * Avg Retail Spend |
| Operational Costs | Calculation Step | Passenger arrives at airport * Staff Cost per Pax + Flight Departs * Staff Cost per Flight |
| Ground Handling Costs | Calculation Step | Flight Departs * Ground Handling Cost per Flight |
| Airport Profit | Calculation Step | Aeronautical Revenue + Retail Revenue - Operational Costs - Ground Handling Costs |
| Passenger arrives at airport | Calculation Step | round(Scheduled Passengers * Passenger show-up rate) |
| Airside waiting & retail | Calculation Step | round(Security Screening*Retail Participation Rate) |
| Flight Departs | Calculation Step | round(Board at Gate*Flight Departure Rate) |
| Flights per Month | Input Variable | discrete_normal distribution |
| Boarding staff per passenger | Input Variable | Normal(μ=0.00035, σ=0.00022239033277584032) |
| Servicing events per flight | Input Variable | Normal(μ=3.0, σ=1.482602218505602) |
| Pushback events per turnaround | Input Variable | Normal(μ=1.5, σ=0.741301109252801) |
| Scheduled passengers | Input Variable | Normal(μ=170.0, σ=74.13011092528009) |
| Check-in rate | Input Variable | beta distribution |
| Screening rate | Input Variable | beta distribution |
| Retail participation rate | Input Variable | beta distribution |
| Boarding rate | Input Variable | beta distribution |
| Flight completion rate | Input Variable | beta distribution |
| Boarding Rate | Input Variable | beta distribution |
| Average bags per passenger | Input Variable | Normal(μ=1.4, σ=0.8895613311033611) |
| Boarding efficiency rate | Input Variable | beta distribution |
| Servicing requirement per flight | Input Variable | Normal(μ=2.0, σ=1.482602218505602) |
| Pushback requirement per aircraft | Input Variable | Normal(μ=1.1, σ=0.14826022185056015) |
| Baggage sort & load | Calculation Step | round(Check-in & Baggage Drop * Average bags per passenger) |
| Boarding coordination | Calculation Step | round(Board at Gate * Boarding staff per passenger) |
| Aircraft turnaround & servicing | Calculation Step | round(Flight Departs * Servicing events per flight) |
| Pushback & ground control | Calculation Step | round(Aircraft turnaround & servicing * Pushback events per turnaround) |
Simulation notes
- https://krobar.ai/
- https://en.wikipedia.org/wiki/Beta_distribution
- https://en.wikipedia.org/wiki/Log-normal_distribution
- https://www.ibm.com/think/topics/monte-carlo-simulation
- https://www.datacamp.com/tutorial/monte-carlo-simulation-in-excel
Formula used
- Beta Distribution =RAND()*(100-0)+0
- Log Normal Distribution =RAND()*(100-0)+0
- Discrete Normal Distribution =RAND()*(100-0)+0
Standards
The workspace needs to comply with all international standards.
- (To come)
API
- (To come)
Support
There will be extensive free documentation sets tailored for users, developers, and data scientists.
Ajabbi will provide free support to developers with a paid DevOps Account who are supporting end users of Workspaces for Aviation.
Workspace navigation menu
This default outline needs a lot of work. The outline can be easily customised by future users using drag-and-drop and tick boxes to turn features off and on.
- Enterprise Account
- Applications
- Aviation (v.3)
- Aircraft
- Aircraft Records
- Aircraft Registration
- Certificate of Registration
- Airframe
- Engine Cycles
- Engine Hours
- Certification
- Fuel & Oil
- Inventory
- Orders
- Parts Request
- MRO (Maintenance, Repair, and Overhaul)
- Fixed Wing
- Helicopter
- Personnel
- Engineer
- Pilot
- Scheduling
- Status & Readyness
- Airport
- Aerodrome
- Aerodrome Plate
- AIP
- Aircraft Stand
- Apron
- Helipad
- Runway
- Taxiway
- Air Traffic Control
- Frequencies
- Tower
- Navigational
- Slot
- Contact
- Emergency
- Facility
- Flight information display system (FIDS)
- Checkin Counter
- Baggage
- Gates
- Boarding
- Cafe
- Parking
- Shop
- Security
- Weather
- ATIS
- METAR
- NOTAM
- TAF
- Airspace
- Map
- Flight
- Cargo
- Flight Number
- Flight Plan
- Passenger
- Customer (v2)
- Bookmarks
- (To come)
- Support
- Contact
- Forum
- Live Chat
- Office Hours
- Requests
- Tickets
- (To come)
- Feature Vote
- Feedback
- Surveys
- Learning
- Explanation
- How to Guide
- Reference
- Tutorial
- Settings (v3)
- Account
- Billing
- Deployments
- Workspaces
- Modules
- Plugins
- Templates
- (To come)
- Users
"Cerification" looks like typo.
ReplyDeleteGood spotting Alex. Thanks.
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