3.1.  Terms and definitions

3.1.1. Application Programming Interface (API)

an interface that is defined in terms of a set of functions and procedures, and enables a program to gain access to facilities within an application [7].

3.1.2. Façade Service

a component that fetches data from a specific data source and makes it available through its own interface [9]. The main reason for building this type of service is the difficulty or inability to modify the original data source with the intent of modifying either:

• the underlying structure of the API; or

• the format in which the data is made available.

3.1.3. Standardized API

an API that is intended to be deployed by multiple API providers with the same API definition.

3.1.4. Standards-based API

an API that conforms to one or more conformance classes specified in one or more standards.

3.1.5. SWIM Data

any data provided through the SWIM System.

3.1.6. Web API

an API using an architectural style that is founded on the technologies of the Web [8].

3.2.  Abbreviated terms

AIXM

Aeronautical Information Exchange Model

API

Application Programming Interface

CRS

Coordinate Reference System

ER

Engineering Report

FAA

Federal Aviation Administration

FIXM

Flight Information Exchange Model

NAS

National Airspace System

NOTAM

Notice to Airmen

OGC

Open Geospatial Consortium

SCDS

SWIM Cloud Distribution Service

SFDPS

SWIM Flight Data Publication Service

SWIM

System Wide Information Management

TB

Testbed

TFMS

Traffic Flow Management System

TIE

Technology Integration Experiment

WFS

Web Feature Service

WXXM

Weather Information Exchange Model

4.1.  Background

4.1.3.  Exploration of OGC API Standards by SWIM

Over the years, the FAA and OGC have jointly explored making SWIM data more easily accessible and more valuable. As part of these past efforts, Testbed-16 brought together previous work on the development of OGC APIs, the use of semantics to enrich data, and SWIM data processing. The objectives were to deliver the first demonstration of an OpenAPI-based API serving SWIM data, a component generating aviation Linked Data, and two client applications querying and displaying that data [14].

Two of TB-16 recommendations were to integrate OGC API requirement classes within SWIM Data Services and to demonstrate interoperability between diverse Aviation APIs [14]. In order to advance these recommendations, TB-17 focused on the development of eleven APIs based on OGC API Standards, and the completion of Technology Integration Experiments (TIEs) between these APIs.

During TB-16, the development of the API serving aviation data resulted in numerous lessons learned and recommendations [14]. TB-16 saw the development of one aviation-related API based on an OGC API Standard (OGC API — Features). The APIs developed during TB-17 ([4]) addressed many of those lessons learned and implemented additional OGC API Standards (draft and approved) which have been maturing since. This process is reflected in Figure 2.

Figure 2 — History of OGC experiments to enhance SWIM

4.2.  Requirements Statement

Testbed-18 required investigating the potential of filtering using OGC API Standards in the context of the SWIM Program.

The original goals of the TB-18 Advanced Filtering of SWIM Feature Data Task were as follows

• Experiment with OGC API-Features filtering mechanisms.

• Explore if best practices for advertising filtering capabilities are required beyond what is already defined in the various OGC API-Features Parts.

• Demonstrate advanced filtering in situations where the data endpoints support only rudimentary filtering by introducing a new service type “Filtering Service.”

• Allow filtering rules for a specific data service to be provided at runtime in a machine-readable manner.

The research questions for the Advanced Filtering of SWIM Feature Data Task were as follows.

• How does filtering of SWIM data served by OGC API-Features endpoints work?

• Is the metadata required by the various OGC API-Features parts sufficient to allow clients to fully “understand” the filtering capabilities of a service endpoint?

• OGC API — Features — Part 3: Filtering and the Common Query Language (CQL) supports queryables that are not directly represented as resource properties in the content schema of the resource. Is it possible to identify best practices for their usage?

• Clients may know the content schema of offered resources. How to use this knowledge for advanced filtering beyond what is defined in OGC API — Features — Part 3: Filtering and the Common Query Language (CQL)?

• How does a filtering service look like that allows advanced filtering for rather simple OGC API-Features-based SWIM data endpoints?

• How does such a service work in situations where a data publisher has restricted filtering on certain properties (for example, because the backend datastore has not been configured to allow high-performance queries on those properties)?

• How can a client application support a customer who has knowledge of the content schema of an offered resource in the creation of filter statements? What are the key requirements for a developer GUI that supports visualization and management of these filtering tools?

• Is easily creating a new filtered dataset possible by creating machine readable filtering rules based on the metadata required by the OGC API-Features standards? How can these rules be provided to the Filtering Service at runtime?

4.3.  Functional Overview

As shown in Figure 3, the Advanced Filtering of SWIM Feature Data Task architecture was organized into a system of seven interconnected components. All seven components were developed simultaneously throughout the Testbed, with permanent communication and cooperation among participant organizations.

The components can be divided into three groups.

• Façades for SWIM services with simple filtering mechanisms. Retrieve aviation data from multiple SWIM services and serve these data through APIs built based on OGC API Standards featuring basic filtering mechanisms. Three Façades were built.

  • The OGC API-Features Façade 1 (identified collectively as D100): Four APIs built to serve NOTAMs, Airport Layouts, and Airspaces

  • The OGC API-Features Façade 2 (identified collectively as D101): Three APIs built to serve aeronautical, flight, and weather features.

  • An extra façade, not originally included in the Task architecture, was offered in-kind by the company Skymantics, and was named OGC API-Features Façade 3: An API built to serve flight plans from the SFDPS (FAA) Service.

• Components that serve aviation data with advanced filtering mechanisms. Two filtering services were built, each one featuring an API.

  • The Filtering Service 1 (identified as D102): Built to serve SWIM data from D100 with advanced filtering mechanisms.

  • The Filtering Service 2 (identified as D103): Built to serve SWIM data from all three façades with advanced filtering mechanisms.

• Client components to demonstrate consumption of filtered data, and configuration of filtering mechanisms. Two clients were built: One meant to serve an aviation domain expert and the other to serve a developer of aviation software applications.

  • The Business User Client (identified as D104): A client built to query filtering services and demonstrate the usage of advanced filtering mechanisms.

  • The Developer Client (identified as D105): A client built to define filter statements that can be expressed in a machine-readable way and can be exchanged with the filtering services.

Figure 3 — Component Diagram for the Advanced Filtering of SWIM Feature Data Task

4.3.1.  Component Interactions

The following two figures illustrate the intended interactions between the components described in the Work Items & Deliverables section of this ER. The two figures illustrate the workflows for using the filtering service for data subsetting (Figure 4) from the perspective of a business client and for configuring the filtering service at runtime (Figure 6) from the perspective of the filtering rules developer.

In the first workflow, illustrated in Figure 4, an OGC API-Features façade to SWIM Data Service data service offers insufficient filtering capabilities to its customers. The Business User Client does not want to access large data sets and then perform filtering itself. Instead, the client wants to make use of a Filtering Service that can handle the filtering of the data and provide the subset of the data that the client is interested in. If the filtering service receives a data request from the client, it connects to the data service to access the necessary data, filters out everything that is not requested by the client, and eventually delivers the result to the client.

Figure 4 — Workflow from the perspective of a business user that needs filtered data

Figure 5 — First Workflow Sequence Diagram

The second workflow, illustrated in Figure 6, demonstrates how a filtering service can be configured at run time. The assumption is that the Developer Client is aware of the API characteristics of the data service as well as the content schema of the data served by the data server. Based on both, the client supports the user with a GUI in the definition of the filtering rules. The user can then register these rules with the filtering service, which is now configured to run the data service specific filtering.

Figure 6 — Workflow from the perspective of a filtering rules developer

Figure 7 — Second Workflow Sequence Diagram

5.1.  Internal Architecture

5.1.1.  Component Overview

This Façade Service features the following.

• Two data retrieval subcomponents: one for the Federal Notice to Airmen System (AIM-FNS) and another one for airspace and airport static data sources.

• A database to store the extracted data.

• ldproxy which delivers the APIs that serve the extracted data. Figure 8 describes the Façade Service subcomponents, the data sources (note that for AIM-FNS the retriever also accesses static sources on some occasions), and the Testbed-18 components consuming from the Façade APIs.

Figure 8 — D100 Component Overview

5.1.3.  PostgreSQL/PostGIS (Database)

The data available for API access are stored in a PostgreSQL/PostGIS database. That is, the data are stored in tables with the geometries stored as Simple Features geometries in Well-Known-Text format.

The following are the aeronautical datasets.

• All Controlled Airspaces of the Classes B, C, D, and E from the National Airspace System Resource (NASR) Subscription

• Airport data for the major airports in the United States provided by Hexagon (AIXM 5.1)

• Notices to Airmen (NOTAMs) received from a Federal Notice to Airmen System (AIM-FNS) subscription in FAA’s SWIM Cloud Distribution Service (SCDS), a cloud-based infrastructure dedicated to providing near real-time FAA SWIM data to the public via Solace JMS messaging

5.1.3.1.  Communication Between ldproxy and PostgreSQL

ldproxy converts API requests to SQL queries and processes the results to convert them to API responses in the GeoJSON, Features and Geometries JSON (JSON-FG), HTML, or Mapbox Vector Tile (MVT) formats.

Figure 9 — Information Flow for Data Requests

While the first two datasets are static and do not change, the NOTAMs are dynamic. New NOTAMs are added to the database as they are received from the SCDS subscription. Since a new NOTAM may change information that is cached in ldproxy for performance reasons (in particular, the spatial and temporal extent of the NOTAM dataset, but also vector tiles) a database trigger is used to notify ldproxy about a new NOTAM. For each new NOTAM, the spatial and temporal extents are evaluated and, if needed, the extents of the NOTAM feature collection are updated. Additionally, vector tiles that include the spatial extent are invalidated in the tile cache.

Figure 10 — Communicating Data Changes to ldproxy

The communication between ldproxy and PostgreSQL uses the standard PostgreSQL protocol, which is TCP/IP-based.

5.2.  Differences to the component from Testbed 17

The component was updated to the latest version of ldproxy and to the new version 1.0.1 of OGC API Features Part 1: Core.

The APIs mainly supported simple access to the data, based on OGC API Features Part 1: Core. That is, paging was supported as well as filtering using bbox, datetime, and selected feature attributes.

Some sample requests for data requests with simple filter capabilities were included in the description of the NOTAM API, by clicking on “Sample requests to filter NOTAM events” on the HTML landing page for more information. This gave clients an idea of how to construct data requests with simple but already useful filter capabilities.

Example — Example simple filtering requests in the NOTAM API

The HTML representation of the Features resource also included a simple HTML form to submit requests with these simple filters.

Figure 11 — D100 Simple Filtering in the Web Browser

In Testbed 17 the APIs supported more OGC API building blocks, in particular they supported filtering using OGC API — Features — Part 3: Filtering and the Text encoding of Common Query Language (CQL2). Since the Filtering Services D102 and D103 were used for Filtering in Testbed 18, these capabilities were removed from the Façade Service in the testbed. In addition, other capabilities were removed, like the ability to request the data in various coordinate reference systems.

5.3.  Challenges and Lessons Learned

Since the component was in large part reused for Testbed 18, there were no new challenges regarding the pre-existing APIs. See section 4.3 of the Tested-17 Aviation API Engineering Report for several challenges and lessons learned during Testbed 17.

Furthermore, there was a goal to add additional weather datasets to the component in Testbed 18. However, getting access to weather data with a coverage of the continental United States within the time frame of Testbed 18 was not possible. Therefore, no additional SWIM data was added during Testbed 18 to the component.

6.1.  Status Quo

For Testbed 16 (OGC document 20-020) the OGC API-Feature façade for the SWIM data messaging services was initially implemented as a RESTful relay Web service using Java, Spring framework, and Java Messaging Service (JMS) clients. A Testbed 18 objective was to test integrated filtering capabilities of OGC API-Features, especially with the OGC API-Features draft Part 3 filtering capability of OGC document 19-079r1. The following summarizes the main gaps or the advances to be made to the API-Features façade Web service for the SWIM implemented in the Testbed 16.

• Filtering capabilities: The API-Features façade for the SWIM messaging services implemented for Testbed 16 lacks the support of advanced filtering as specified in the OGC API-Features Part 3 (OGC 19-079r1). There is a filter parameter declared in the OpenAPI specification of the OGC API — Features Standard. The OGC API — Features service will be enhanced with the support of filtering using Filtering / Common Query Language (CQL2) languages in both text and JSON encoding.

• Additional SWIM data: The Testbed 16 API-Feature façade service does not handle any weather data. That activity focused on relaying data and caching features from messages in Aeronautical Information Exchange Model (AIXM) and Flight Information Exchange Model (FIXM). Extensions to AIXM and FIXM were handled in Testbed 16, such as the US Federal Aviation Administration Notice to Air Missions System Event (FNSE) extension and the US National Airspace System (NAS) extension. The API-Features service in Testbed 18 will add weather data provided in the Weather Information Exchange Model (WXXM) or its local extensions.

• Updates of OGC API-Features specification: The OGC API-Features Standard has evolved and been revised for several years. The core Standard has a new update officially released as version 1.0.1. The API-Features service in Testbed 18 adopted the latest revision. Related tools and software development kits (SDKs) for OpenAPI have also evolved and advanced recently from community efforts. The implementation of the API-Features service in Testbed 18 leverages the advancements of these SDKs, especially the evolutions in the open source community. The interface parsing and validation will be done with the API-first approach in the Python open source community. This would be a different experience from the previous Java development.

6.2.  Internal Architecture

The OGC API-Feature façade for SWIM services was re-implemented and enhanced using the OpenAPI stub generator (python-aiohttp) to support standard search and retrieval of SWIM messaging-accumulated feature data with filtering languages of Filtering / Common Query Language (CQL2) in text format or JSON format. In addition to the filtering capabilities, new data was added with new messaging-harvester for the Integrated Terminal Weather System (ITWS) of the System Wide Information Management (SWIM).

Figure 12 — D101 Component Overview

Filtering language support is primarily based on an extended version of the open source pygeofilter, a Python implementation of cql2-text and cql2-json parsers. There is an example backend translator for Django, SQLAlchemy, GeoPandas, and generic SQL. This implementation forked the open source pygeofilter to extend the SQL backend to work with the PostGIS backend spatiotemporal database.

The overall server-side API-Features library was implemented as a pluggable system to support extension of the server. Two parts are pluggable – backend data providers and frontend output formatters. The backend data providers provide customized interaction with the data source. Different types may require different providers. The current system implemented the data providers with a PostGIS spatiotemporal database. All collection metadata and features are managed with a set of relational entities (tables).

The frontend formatters produce the responses matching the request. In the Testbed 18 implementation, GeoJSON and rendered HTML are supported. The jinja2 templates are used to render HTML output. The rendered HTML page embedded a light-weight JavaScript client to support the interactive retrieval and rendering of results on maps in browsers.

The following figure shows the interaction and data flow between OGC API — Features façade services and SWIM messaging services. In the implementation, the data are first harvested, translated, and cached in a unified spatiotemporal database from the messaging stream of SWIM services. The ingestion of streaming messages is completed with two steps: a Java Messaging Service (JMS) client to receive the messages and a harvester for each data type to load message into the spatiotemporal database. The harvesters implemented in Java in Testbed 16 were reused for Testbed 18, including harvesters for AIM FNS, TFMS, and SFDPS. The SWIMITWS, the harvesters for ingesting ITWS Weather data from SWIM ITWS messages is newly implemented in Testbed 18. The data include 28 feature collections. Each feature collection has a specific handler to complete the necessary translation, reformatting, and loading into the intermediate spatiotemporal database.

OGCAPIUsers-> D101 : API-Features for SWIM features

D101-> SWIMServiceConsumer: Subscription requests
SWIMServiceConsumer -> SWIM: Request for services
SWIM -> SWIMAIMFNS: Subscribe to SWIM AIM FNS service
SWIMAIMFNS -> SWIM: AIM FNS notice to airmen messaging responses
SWIM -> SWIMTFMS: Subscribe to SWIM TFMS service
SWIMTFMS -> SWIM: TFMS traffic flow messaging responses
SWIM -> SWIMSFDPS: Subscribe to SWIM SFDPS service
SWIMSFDPS -> SWIM: SFDPS flight data messaging responses
SWIM -> SWIMITWS: Subscribe to SWIM ITWS service
SWIMITWS -> SWIM: ITWS Weather data messaging responses
SWIM -> SWIMServiceConsumer: Proxy service responses
SWIMServiceConsumer-> D101 : Document-based database indexing

D101->OGCAPIUsers : Resources (GeoJSON, HTML)

Figure 13 — API-Features Data Interaction with Backend SWIM Messaging Services

' hide the spot
hide circle

' avoid problems with angled crows feet
skinparam linetype ortho

entity "Collections" as e01 {
  *e1_fid : number <<generated>>
  --
  *thegeom : geometry
  *cid : varchar
  table: varchar
  title : varchar
  description : text
  bbox: varchar
  temporal: varchar
  itemtype: varchar
  crs: varchar
  links: varchar
}

entity "Collection.Queryables" as e07 {
  *e7_id : number <<generated>>
  --
  *e2_id : number <<FK>>
  *cid : varchar <<FK>>
  type : varchar
  encoding : text
}

entity "CollectionEncoding" as e02 {
  *e2_id : number <<generated>>
  --
  *e1_id : number <<FK>>
  *type: varchar
  *encoding : text
}

entity "Document" as e03 {
  *e3_id : number <<generated>>
  --
  *file : varchar
  *status : number
}

entity "Message" as e04 {
  *e4_id : number <<generated>>
  --
  *e3_id : number <<FK>>
  xpath : varchar
  type : number
  encoding : text
}

entity "Feature" as e05 {
  *e5_id : number <<generated>>
  --
  *thegeom : geometry
  *gmlid : varchar
  *gmlidtrace : varchar
  *featuretype : varchar
  *begintime : timestamp
  *endtime : timestamp
  *isinsttime : Boolean
  elevation : double
  verticalTop : double
  elem: varchar
}

entity "FeatureEncoding" as e06 {
  *e6_id : number <<generated>>
  --
  *e4_id : number <<FK>>
  *e5_id : number <<FK>>
  xpath : varchar
  type : number
  encoding : text
}



e01 ||..|{ e02
e05 ||..o{ e06
e06 ||..o{ e04
e03 ||..o{ e04

Figure 14 — Major Entity Relationship Diagram for Managed SWIM Features

Table 1 — Server Endpoints

6.3.  Feature collections

6.4.  Filtering Capabilities

The filtering capability of the API-Features supports both cql2-text and cql2-json. The parsing and translating of queries into backend SQL in PostGIS/Postgresql is achieved through a forked, revised open source library, pygeofilter. Two major revisions to pygeofilter are functional mapping and field (property) mapping. The functional mapping list enables the conversion of query functions to equivalent functions in the backend PostGIS/Postgresql database. For example, temporal comparison function “BEFORE” may have to be mapped to “<” in temporal field comparison with proper conversion (i.e., converting from string expression into internal sql timestamp).

The field mapping converts the property to field or extracted field value in a backend spatiotemporal database. The spatiotemporal database in this implementation uses a generic spatiotemporal database. The data for each feature are stored as an encoded blob/text in the encoding field with a corresponding media-type. The primary encoding is GeoJSON (i.e. application/geo+json) besides the native encoding (e.g., application/aixm+xml;version=3.0.1). The field mapping uses the JSON parsing and extraction capabilities of Postgresql database. The following list shows examples of mappings for ITWS weather data.

The parsing and validation of user requests is enabled using API-first approach. The API-first approach assures the consistency of declarations in OpenAPI YAML or JSON. The implementation of OGC API — Features also uses the open-source server stub generator, OpenAPI Generator, to generate stubs for the server-side library. Models are generated using the Python-aiohttp generator specifically for Python programs. The models are linked to the interface parser, connexion. Connexion is a framework that automatically handles HTTP requests based on OpenAPI Specification. The tight links between parameters, models, and responses enable the consistency across-board.

{
  "filter": {
    "op": "=",
    "args": [
      {
        "property": "itws_sites"
      },
      "CMH"
    ]
  }
}

6.5.  Challenges and Lessons Learned

The following summarizes challenges and lessons learned for the implementation and enablement of filtering capabilities in the API-Features services for SWIM data.

• Filtering enablement: The Filtering / Common Query Language (CQL2) standard is evolving. The open-source libraries for supporting the parsing and translation of CQL2 queries into common backend databases queries or query services such as SPARQL and GraphQL are not in sync with the changes to the draft standard. For example, the implementation of the OGC API — Features service in Testbed -18 using Python found pygeofilter as the library that supports both cql2-text and cql2-json.

  However, the predicates for spatial and temporal conditions are still in the formats with prefixes s_ and t_ respectively. This leads to the mandatory forking and revision of pygeofilter to be usable for the latest version as defined in OGC 19-079r1 (OGC API – Features – Part 3: Filtering). The open-source library pygeofilter also has limited support on the backend. In Testbed 18, pygeofilter does not support the backend with PostGIS/postgresql. The backend translator must be extensively edited to support the evaluation of CQL2 (either cql2-text or cql2-json) into valid a SQL command for PostGIS/Postgresql.

• Filtering capability description: SWIM messaging services provide different features with different information models. These schemas are not easily regenerated automatically from (for example) database schemas. The extraction and encoding of queryables were done manually with extensive study of the original data models, including AIXM, FIXM, WXXM, and specialized FAA extensions. The description of queryables is flexible, but this flexibility may lead the response to be too flexible to be used properly by clients.

  The SWIM dataset has pre-defined codes for locations, facilities, and/or status. Some may not be fully expressed with “enum” attributes. For example, the International Air Transport Association’s (IATA) Location Identifier is another standard that is widely used in SWIM messages as attributes. Verifying if the code is valid unless the client checks with a registry service of IATA is not possible. The specification may specify how to describe the queryable in such a case and give examples for describing the queryable.

• Filtering performance: Performance is an implementation-specific experience based on balancing the general applicability and the specialization of information models in the backend spatiotemporal database. This Testbed’s implementation considers managing different features generated from SWIM messages. The harvester would create aa feature collection under the same type if the service has not recorded the collection. In order to achieve this flexibility, the implementation populates partially common attributes (e.g., spatial extent, temporal extent) in defined entities and encodes all into a blob field with corresponding media type (e.g., application/geo+json or native media type – application/aixm+xml;version=3.0.1).

  Most queryables are only accessible through an encoded blob. In defining the field-mapping, we used JSON query against the blob. These properties/attributes for queries are directly managed by PostGIS/Postgresql. Therefore, the performance improvement with indexing these properties is not supported with this implementation. This setup used in Testbed 18 may work fine for SWIM data services if only a limited number of features are searchable within their valid time range.

• API-first approach for implementing services: The Testbed 18 implementation of OGC API — Features uses the API-first approach. In other words, the specification defined in YAML is used at the interface, intermediate internal information models, and response generation. This approach needs a well-defined OpenAPI specification in either JSON or YAML. The implementation starts to form a complete OpenAPI document using the examples in API-Features Core Standard GitHub and adds the filter parameters from the examples in swaggerhub. The API-Features (OGC 17-069r5 and OGC 19-079r) schema does not include paths although these examples include paths. The paths should be an integrated part of the specification.

  Having the specification schema repository including more concrete OpenAPI specification examples with fully defined paths at different levels of conformances in YAML or JSON would be helpful. Schemas can be used directly with little configuration changes with the API-first implementation approach. The internal models generated from the YAML specification or JSON have problems in dealing with “oneOf” for parameterization and Geometrycollection GeoJSON object. The generated models need to be revised before the stubs work. This may be an OpenAPI open-source tool issue. The open-source OpenAPI generator, specifically the Python-aiohttp generator, needs to be revised to support the proper generation of models.

  The API-first library, connexion, needs to be revised to support proper validation. Alternatively, the specification may avoid this complexity by leaving out the validation of certain property constraints. For example, the BBOX parameter may just be defined as a simple array with the choice of 2-D or 3-D constraints which the connexion not properly validated against the specification. The Geometrycollection may be fully defined in the schema section to enable the generator tool to create inheritable objects in the generated models.

• Advanced filtering: SWIM messaging services are streaming data with validated time periods while they are ingested into separate feature collections. For example, the wind information is delivered through three feature collections: itws_9840_start for wind start time, itws_9840_expiration for wind alter information expiration time, and itws_9840_speed for wind speed update. A filter based on wind event would need to be formed as an integrated query against all three feature collections to get the valid wind event information.

  Another case is the part-of relationship between SWIM feature collections which also needs an advanced filtering query to get valid information. For example, a Runway feature may consist of several RunwayElements, a Taxiway feature of multiple TaxiwayElements, and an Apron feature of multiple ApronElements. The classes at the highest level (e.g., Runway, Taxiway, Apron) are in different feature collections than their component classes (e.g., RunwayElement, TaxiwayElement, ApronElement). The CQL2 Standard is not designed to work against multiple feature collections even though they may be served in one OGC API — Features service.

• Light-weight client for rendering response in browsers: The implementation of OGC API — Features in Testbed 18 uses an embedded leaflet-based, light-weight JavaScript client to render the HTML response in browsers. This light-weight client helps in guiding users to input proper parameters. The jinja2 templates are used to create the templates for rendering results from different feature collections.

7.1.  Status Quo

Prior to the testbed, Ecere’s GNOSIS Map Server did not support filtering using the Common Query Language (CQL2) or cascading other map services.

7.2.  Internal Architecture

This service is a deployment of the GNOSIS Map Server implementing OGC API Standards and draft specifications. The relevant collections from the deployed endpoint used for this testbed task are found at: (https://maps.gnosis.earth/ogcapi/collections/swim.

The GNOSIS Map Server product is a certified implementation of OGC API — Features — Part 1: Core Standard. The server product also supports extensions including Part 2: Coordinate Reference Systems by reference, as well as the draft Part 3: Filtering specification. The implementation also supports returning filtered results as tiled vector feature data (“vector tiles”) through the OGC API — Tiles — Part 1: Core Standard, including support for the Mapbox Vector Tiles encoding. The D102 service also acts as an OGC API — Features client for cascading to the D100, D101, and the Skymantics services.

Experiments were performed using GeoJSON and Features & Geometry JSON to encode vector features.

7.2.1.  Filtering Capabilities

Filtering queries can be specified at request time using Features — Part 3: Filtering, using either the CMSS expression language or the OGC Common Query Language (CQL2) (new capability developed for this initiative). Filters can also be pre-defined using the OGC API — Processes — Part 3: Workflows & Chaining draft specification, supporting the creation of virtual collections.

Support for the OGC API — Features “Search” extension, discussed in more detail in the Filtering Service and Rule Set Engineering Report (ER) (OGC 22-024), is planned for future work. Ecere will be actively following the development of that extension, considering use cases relating to having common basic analytics and search capabilities across different OGC API Standards, such as planned for OGC API — Processes — Part 3: Workflows & Chaining Input and Output modifiers requirements classes, as well as OGC API — Coverages, OGC API — DGGS. Some of these aspects were previously discussed in Testbed 17 — GeoDataCube API and in the May 2022 Space Partition Code Sprint.

7.2.1.1.  CQL2

The Ecere implementation supports specifying filters using CQL2, including support for the Basic, Property-Property, and Arithmetic Expressions conformance classes, and the cql2-text encoding. Support for additional conformance classes, as well as the cql2-json encoding, is planned for future work.

Test collections, using Natural Earth data, for some of the CQL2 Abstract Tests are deployed at the following location.

https://maps.gnosis.earth/ogcapi/collections/cql2-test

The following are a couple of example requests using these test collections.

https://maps.gnosis.earth/ogcapi/collections/cql2-test:ne_110m_admin_0_countries/items?filter=NAME=’Luxembourg’

https://maps.gnosis.earth/ogcapi/collections/cql2-test:ne_110m_populated_places_simple/items?filter=NOT (start is NULL) AND (pop_other<1038288 OR name<’København’) AND NOT (“date” IS NOT NULL or NOT start is NULL) AND (“date” IS NOT NULL)&f=json

Figure 16 — Single feature returned from CQL2 filter query on test collection

7.2.2.  Cascading

Ecere implemented the ability to provide access to cascaded services, while also supporting filtering features returned by those cascaded services, which may not themselves provide filtering support. Pagination challenges specific to this capability were addressed to reflect the fact that not all results returned by the cascaded service will be part of the filtering service responses.

The following link is an example items request to features originating from the cascaded service D100 service provided by interactive instruments.

http://maps.gnosis.earth/ogcapi/collections/swim:d100_airspace:class_c/items

Figure 17 — Single flight plan feature cascaded from Skymantics service

7.2.3.  Pre-defining queries based on Processes — Part 3 extension

The OGC API — Features Search extension (described in detail in OGC 22-024 ER) has much in common with the idea of allowing filter, properties (for selection and derived fields/properties), and sortBy to qualify inputs in OGC API — Processes — Part 3: Workflows and Chaining (input and output modifiers requirements classes).

A well-known pass-through process (with support for collection input including filtering) could support an execution request with a syntax equivalent to the Search extension endpoint, similar to how the OGC API — Routes /routes endpoint shares a POST payload syntax with an eventual definition of a well-known routing process. Pre-defined queries could also be parameterized by deploying them as processes, as suggested in the Deployable workflows requirements class of Processes — Part 3: Workflows and Chaining.

The modifiers introduced include the same filter, properties, and sortBy parameters to qualify inputs originating from a data collection or process, whether they are local or remote, as well as outputs resulting from a process. In addition to the ability to select specific fields/properties, the properties parameter can also be used to derive new fields, for example using CQL2 arithmetic expressions.

Processes — Part 3 also defines a Collection output requirements class where the output of the workflow execution is either a dataset landing page (which can contain multiple collections), or a single collection.

For example, the following parameterized query addressing similar use case as the one described further in section 8.3.5 — Support for the Search resources‘s Example 2 taking two parameters, a string enumeration named composition, and a string array named airports, could be expressed in a Part 3-extended OGC API — Processes execution request (using Collection input and Output modifiers) as follows.

Example 1 — Example parameterizable query as a Part 3-extended execution request

{
 "process" : "PassThrough",
 "inputs" : {
  "data" : [ {
    "collection" : "apronelement",
    "filter": {
      "op": "and",
      "args": [
        {
          "op": "=",
          "args": [
            {"property": "composition"},
            {
              "$input": {
                "composition": { "type": "string", "enum": ["CONC", "..."] }
              }
            }
          ]
        },
        {
          "op": "in",
          "args": [
            {"property": "airport"},
            {
              "$input": {
                "airports": {
                  "type": "array",
                  "items": { "type": "string", "enum": ["JFK", "EWR", "LGA", "..."] }
                }
              }
            }
          ]
        }
      ]
    },
    "properties": ["geometry", "airport", "type"],
    "sortBy": ["airport"]
  } ]
 }
}

In this example, data is an input defined in the PassThrough well-known process with multiplicity 1..* which is returned as the process output.

The collection property is defined in the Collection input requirements class of OGC API — Processes — Part 3, whereas the filter, properties, and sortby elements specifying a cql2-json filtering expression, selected properties, and an ascending sort order by airport, are defined in Part 3’s Input modifiers requirements class.

These field modifiers can also be used in the context of the Output modifiers requirements class together with the Collection output requirement class or with regular process execution outputs. In the context of a feature collection output, these query parameter building blocks also correspond to the functionality provided by OGC API — Features — Part 3: Filtering, as well as a planned extensions for Coverages and Discrete Global Grid Systems.

This execution request could be deployed as a new process, e.g., ApronFiltering, using the Processes — Part 2: Deploy, Replace, Undeploy extension’s POST operation to /processes together with the Deployable workflows requirements class of Part 3. The resulting process would get listed at /processes with a process description including the input parameters (composition and airports) and could itself be executed by POSTing to /processes/ApronFiltering/execution as follows.

Example 2 — Example execution request of parameterized query deployed as a process

{
  "inputs" : {
    "composition" : "CONC",
    "airports": [ "JFK", "LGA" ]
  }
}

Posting this execution request to the execution endpoint without a Prefer: header would result in a synchronous execution that returns the features. With support for the Collection output requirements class, specifying as parameter response=collection would instead return a collection description, as for a GET request to /collections/{collectionId} in Common — Part 2 and Features — Part 1. With response=landingPage, a landing page would be returned for the filtered dataset, allowing retrieval of multiple collections.

Such virtual collections could also be published to a dataset API as a persistent collection, e.g., as /collections/concApronsJFKLGA, using a non-parameterizable execution request as the payload of a POST operation to /collections to create the dynamic collection, with a Processes execution request content media type to be registered, e.g., application/ogcexecreq+json as suggested in Part 3 — Section 14. Media Types.

During this Testbed-18 advanced filtering task, Ecere successfully demonstrated the use of Part 3 extensions to pre-define filtering queries to the cascading service, including the deployment of a sample PassThrough process with support for the filter and properties modifiers, as well as for the Collection Input and Collection output requirements class, with the following limitations:

• the filters were expressed using the cql2-text encoding rather than cql2-json;

• the input and output were limited to a single collection;

• the sortBy modifier (and sorted feature collections in general) remained to be implemented; and

• support for parameterized queries and deployable workflows was not yet implemented.

A sample pre-defined query is available from the endpoint:

https://maps.gnosis.earth/ogcapi/processes/PassThrough/execution?response=collection

including the following default pre-defined filter execution request.

Example 3 — Working execution request of filtering query from D100 cascaded collection

{
   "process" : "https://maps.gnosis.earth/ogcapi/processes/PassThrough",
   "inputs" : {
      "data" : [
         {
            "collection" : "https://maps.gnosis.earth/ogcapi/collections/swim:d100_airports:apronelement",
            "filter" : "composition = 'CONC' and airport in ('JFK', 'EWR', 'LGA')",
            "properties" : [ "geometry", "airport", "type" ]
         }
      ]
   }
}

Figure 18 — Paging through an output collection resulting from the above filter query pre-defined using OGC API - Processes - Part 3

GET /collections/apronelement/items?
   collections=apron&
   properties=*,apron.otherProperty&
   filter=associatedApron=apron.id and airport in (JFK, EWR, LGA)&
   sortby=airport&
   limit=1000

GET /collections/flightRoutes/items?
   collections=https://weather.com/ogcapi/collections/winds&
   filter=winds.speed > 100 and s_intersects(geometry, winds.geometry) and departingAirport in (JFK, EWR, LGA)&
   sortby=-winds.speed,departingAirport&
   limit=1000

7.3.  Challenges and Lessons Learned

Building next and prev links that reflect both cascaded and filtered features, while respecting the limits of the original queries, in order to page features properly proved to be an important challenge. Issues with some of the cascaded implementations themselves also made the task more difficult, including the following issues that were filed upstream with pygeoapi:

• support for describing object feature properties (#1090);

• use of invalid JSON Schema types in queryables resource (#1091); and

• 500 server error returned when using application/geo+json as the media type for Accept: header (#1108).

The use of complex objects and arrays in feature properties, as well as the occurrence of null geometry and of mixed geometry types by the cascaded services, were other major difficulties that required significant changes and improvements to Ecere’s GNOSIS Map Server implementation.

While implementing support for CQL2, Ecere reviewed the draft CQL2 specification and detected several important issues and suggested additional improvements such as simplifying the grammar to facilitate parser implementations. Several of the issues were resolved during the testbed, including the following.

• CQL2 division real or integer? data type dependent? (#711)

• /functions — out of scope for CQL2? (in Part 3: Filtering) (#715)

• POLYGON\(…​ in CQL2 examples (#716)

• CQL2: Permission 3 (binary comparison operators for time instants) targeting clients? (#719)

• CQL2: Clarify that DATE and TIMESTAMP literals are in basic, but not INTERVAL (#720)

• CQL2: Requirement 2C is a requirement on the client; 2B “literal value” (#721)

• CQL2: Basic Spatial Operators issues (#733)

• CQL2: Spatial Operators Issues (#734)

• CQL2 (AT): A.12 CQL2 Text A.13 CQL2 JSON (#750)

• CQL2: Remove — from list of valid identifier characters? (#766)

It was agreed that the following issues are to be addressed in a future revision of CQL2.

• Simplify the cql2-text grammar (future version improvements?) (#705)

• CQL2 as an expression language (not only boolean predicates) (#723)

Other issues were still pending completion at the time of writing this report.

• CQL2 Escaping (#717)

• CQL2: Thoughts on arrays and IN (#718)

• CQL2/AT1.6: {p2} and {p3} contribute nothing to result (#768)

Ecere also worked on a UML conceptual model for expressions, operators, and standardized functions as part of the Styles & Symbology SWG activities with the intent for it to be compatible with the CQL2 conformance classes and support a similar modularity, and potentially provide the foundations of an OGC conceptual basis for expressions and filtering.

Figure 21 — Expressions UML Conceptual Model, covering CQL2 capabilities

Figure 22 — Operators UML Conceptual Model, covering CQL2 capabilities

Figure 23 — Standard functions UML Conceptual Model, covering CQL2 capabilities

An important lesson to take away from the Testbed-18 initiative is that while a filtering service can be implemented separately from a distinct OGC API — Features façade service, in many ways this architecture really defeats the purpose of the filtering capabilities. This is a result of a request returning the full unfiltered set of features must still be made to the cascaded service. A more efficient architecture would be required to perform the filtering as close to the data source as possible.

7.4.  Recommendations and Future Work

• Filter close to the data: In this task, the deliverables architecture called for one component to perform the filtering while another acts as an OGC API façade. The initiative highlighted that this architecture is not ideal, since the filtering service would still need to request the entire dataset prior to performing the filtering. Instead, for a practical application, the filtering should be performed as close to the source of data as possible, within the same component.

• Explore the potential of spatial joins: In the context of queries spanning multiple collections, participants briefly discussed the capability to perform queries that not only filter and combine the results from multiple collections, but also perform join operations, including spatiotemporal joins. A typical scenario that is relevant to aviation use cases is to query flight paths which would encounter bad weather. This could be achieved by correlating the geometry of the flight paths with the location of severe weather patterns.

  The dataset used for such correlation would not necessarily need to be on the same server. Instead, the client could possibly direct a filtering service (e.g., one with flight paths) to retrieve data from a particular OGC API instance (e.g., one with weather data). It may in fact may faster for the two servers to talk to each other directly, for example if they are both hosted on the same cloud infrastructure, as opposed to the client first retrieving the data, then submitting it as part of its filtering query.

• First-Filtering: In addition, the filtering service may be able to perform a first filtering pass that requires retrieving less data from the other service. This capability could be explored in greater depth in a future activity.

8.1.  Internal Architecture

The component consists of the following three filter APIs that were deployed.

• Airports

• Airspaces

• Federal Notice to Airmen System (FNS)

These filter APIs provide access to the same feature data as the corresponding façade, but in addition also provide support for Part 2: Coordinate Reference Systems by Reference, Part 3: Filtering and Common Query Language (CQL2).

Responses with feature data could be requested in the following representations/formats:

• GeoJSON (media type application/geo+json, query parameter f=json)

• JSON-FG (media type application/vnd.ogc.fg+json, query parameter f=jsonfg)

• HTML (media type text/html, query parameter f=html)

• FlatGeobuf (media type application/flatgeobuf, query parameter f=fgb)

8.2.  ldproxy

Like Clause 5, the APIs are provided by ldproxy. To support the requirements for advanced filtering of SWIM data, the following enhancements were been implemented in ldproxy during the testbed:

• updated to the latest draft version of OGC API — Features — Part 3: Filtering and Common Query Language (CQL2);

• support for the CQL2-JSON encoding of the Common Query Language (CQL2);

• support for all API building blocks specified in Clauses 4 and 5 of the Testbed-18 Filtering Service and Rule Set Engineering Report (D002); and

• support for JSON-FG was updated to the latest draft (version 0.1).

At the time of writing this ER, the updated code is in the branch “multi-queries” of ldproxy and the master branches of XtraPlatform and XtraPlatform Spatial.

8.3.  Filtering Capabilities

{
  "links": [
    {
      "rel": "self",
      "type": "application/json",
      "title": "Dieses Dokument",
      "href": "https://t18.ldproxy.net/d103_fns/conformance?f=json"
    },
    {
      "rel": "alternate",
      "type": "text/html",
      "title": "Dieses Dokument als HTML",
      "href": "https://t18.ldproxy.net/d103_fns/conformance?f=html"
    }
  ],
  "conformsTo": [
    "http://www.opengis.net/spec/cql2/0.0/conf/advanced-comparison-operators",
    "http://www.opengis.net/spec/cql2/0.0/conf/array-operators",
    "http://www.opengis.net/spec/cql2/0.0/conf/basic-cql2",
    "http://www.opengis.net/spec/cql2/0.0/conf/basic-spatial-operators",
    "http://www.opengis.net/spec/cql2/0.0/conf/case-insensitive-comparison",
    "http://www.opengis.net/spec/cql2/0.0/conf/cql2-json",
    "http://www.opengis.net/spec/cql2/0.0/conf/cql2-text",
    "http://www.opengis.net/spec/cql2/0.0/conf/property-property",
    "http://www.opengis.net/spec/cql2/0.0/conf/spatial-operators",
    "http://www.opengis.net/spec/cql2/0.0/conf/temporal-operators",
    "http://www.opengis.net/spec/ogcapi-common-1/1.0/conf/core",
    "http://www.opengis.net/spec/ogcapi-common-1/1.0/conf/html",
    "http://www.opengis.net/spec/ogcapi-common-1/1.0/conf/json",
    "http://www.opengis.net/spec/ogcapi-common-1/1.0/conf/oas30",
    "http://www.opengis.net/spec/ogcapi-common-2/0.0/conf/collections",
    "http://www.opengis.net/spec/ogcapi-common-2/0.0/conf/html",
    "http://www.opengis.net/spec/ogcapi-common-2/0.0/conf/json",
    "http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core",
    "http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson",
    "http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/html",
    "http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30",
    "http://www.opengis.net/spec/ogcapi-features-2/1.0/conf/crs",
    "http://www.opengis.net/spec/ogcapi-features-3/0.0/conf/features-filter",
    "http://www.opengis.net/spec/ogcapi-features-3/0.0/conf/filter",
    "http://www.opengis.net/spec/ogcapi-features-n/0.0/conf/ad-hoc-queries",
    "http://www.opengis.net/spec/ogcapi-features-n/0.0/conf/core",
    "http://www.opengis.net/spec/ogcapi-features-n/0.0/conf/manage-stored-queries"
  ]
}

{
  "title" : "NOTAMs",
  "description" : "Notice to Airmen",
  "properties" : {
    "notam_keyword" : {
      "title" : "NOTAM Keyword",
      "description" : "Keyword associated with the NOTAM",
      "type" : "string",
      "enum" : [ "AD", "APRON", "AIRSPACE", "CHART", "COM", "IAP", "NAV", "OBST", "ODP", "ROUTE", "RWY", "SECURITY", "SID", "SPECIAL", "STAR", "SVC", "TWY", "VFP", "CONSTRUCTION", "LTA" ]
    },
    "notam_function" : {
      "title" : "NOTAM Function",
      "description" : "Function of the NOTAM (New, Replacement, Cancelled)",
      "type" : "string",
      "enum" : [ "NOTAMN", "NOTAMR", "NOTAMC" ]
    },
    "valid_time_begin" : {
      "title" : "Valid time (begin)",
      "format" : "date-time",
      "type" : "string"
    },
    "valid_time_end" : {
      "title" : "Valid time (end)",
      "format" : "date-time",
      "type" : "string"
    },
    "text" : {
      "title" : "Text",
      "description" : "NOTAM condition text.",
      "type" : "string"
    },
    "selection_code" : {
      "title" : "Q Code",
      "description" : "Q Code value for the NOTAM.",
      "type" : "string"
    },
    "year" : {
      "title" : "Year",
      "description" : "NOTAM year values per ICAO Annex-15.",
      "type" : "string"
    },
    "number" : {
      "title" : "Number",
      "description" : "NOTAM number value per ICAO Annex-15.",
      "type" : "integer"
    },
    "scenario" : {
      "title" : "Scenario",
      "description" : "Identifier of the event scenario used for digital encoding. The mapping can be found in the Event Scenario documents.",
      "type" : "string"
    },
    "affected_fir" : {
      "title" : "Flight Information Region",
      "description" : "Flight Information Region (FIR) that is impacted by the NOTAM.",
      "type" : "string"
    },
    "location" : {
      "title" : "Location designator",
      "description" : "NOTAM location designator of the affected airport/heliport or facility.",
      "type" : "string"
    },
    "icao_location" : {
      "title" : "ICAO location designator",
      "description" : "ICAO location designator, if published.",
      "type" : "string"
    },
    "series" : {
      "title" : "Series",
      "description" : "NOTAM series value per International Civil Aviation Organization (ICAO) Annex-15.",
      "type" : "string"
    },
    "type" : {
      "title" : "Type",
      "description" : "NOTAM type value per ICAO Annex-15. Accepted values are: New (N), Replace (R), Cancel (C).",
      "type" : "string",
      "enum" : [ "N", "R", "C" ]
    },
    "issued" : {
      "title" : "Issued",
      "description" : "Issue date/time of the NOTAM.",
      "format" : "date-time",
      "type" : "string"
    },
    "traffic" : {
      "title" : "Traffic",
      "description" : "NOTAM traffic value per ICAO Annex-15.",
      "type" : "string"
    },
    "purpose" : {
      "title" : "Purpose",
      "description" : "NOTAM purpose value per ICAO Annex-15.",
      "type" : "string"
    },
    "scope" : {
      "title" : "Scope",
      "description" : "NOTAM scope value per ICAO Annex-15.",
      "type" : "string"
    },
    "minimum_fl" : {
      "title" : "Minimum flight level",
      "description" : "NOTAM minimum flight level value per ICAO Annex-15.",
      "type" : "string"
    },
    "maximum_fl" : {
      "title" : "Maximum flight level",
      "description" : "NOTAM maximum flight level value per ICAO Annex-15.",
      "type" : "string"
    },
    "coordinates" : {
      "title" : "Coordinates",
      "type" : "string"
    },
    "radius" : {
      "title" : "Radius",
      "type" : "string"
    },
    "schedule" : {
      "title" : "Schedule",
      "description" : "Contains a schedule of activity/outage if the hours of effect are less than 24 hours a day.",
      "type" : "string"
    },
    "lower_limit" : {
      "title" : "Lower limit",
      "description" : "Specifies the lower height restriction of the NOTAM.",
      "type" : "string"
    },
    "upper_limit" : {
      "title" : "Upper limit",
      "description" : "Specifies the upper height restriction of the NOTAM.",
      "type" : "string"
    },
    "geometry" : {
      "$ref" : "https://geojson.org/schema/Geometry.json"
    }
  },
  "additionalProperties" : false,
  "type" : "object",
  "$schema" : "https://json-schema.org/draft/2019-09/schema",
  "$id" : "https://t18.ldproxy.net/d103_fns/collections/notam/queryables"
}

https://t18.ldproxy.net/d103_fns/collections/notam/items?filter=
S_INTERSECTS(geometry,POLYGON((-79.53576165455738 25.18569533817705, -79.50845450086926 25.091558846018916, -79.50003716505086 24.993903798755063, -79.51083312059717 24.896483025086265, -79.54042748499097 24.803040350710415, -79.58768296340534 24.71716672520052, -79.65078355430599 24.64216222382644, -79.7273043373718 24.580909227512972, -79.81430466182294 24.53576165455737, -79.90844115398109 24.508454500869263, -80.00609620124493 24.50003716505086, -80.10351697491373 24.510833120597162, -80.19695964928958 24.54042748499097, -80.28283327479947 24.58768296340534, -80.35783777617355 24.650783554305985, -80.41909077248702 24.72730433737181, -80.46423834544262 24.81430466182295, -82.46423834544262 29.81430466182295, -82.49154549913074 29.908441153981084, -82.49996283494914 30.006096201244937, -82.48916687940283 30.103516974913735, -82.45957251500903 30.196959649289585, -82.41231703659466 30.28283327479948, -82.34921644569401 30.35783777617356, -82.2726956626282 30.419090772487028, -82.18569533817706 30.46423834544263, -82.09155884601891 30.491545499130737, -81.99390379875507 30.49996283494914, -81.89648302508627 30.489166879402838, -81.80304035071042 30.45957251500903, -81.71716672520051 30.41231703659466, -81.64216222382645 30.349216445694015, -81.58090922751298 30.27269566262819, -81.53576165455738 30.18569533817705, -79.53576165455738 25.18569533817705))) AND
notam_keyword IN ('RWY, 'TWY', 'AIRSPACE') AND
T_INTERSECTS(INTERVAL(valid_time_begin,valid_time_end), INTERVAL('2021-07-12T18:00:00Z','2021-07-12T22:00:00Z'))

{
  "title" : "NOTAMs",
  "description" : "Notice to Airmen",
  "properties" : {
    "notam_keyword" : {
      "title" : "NOTAM Keyword",
      "description" : "Keyword associated with the NOTAM",
      "type" : "string",
      "enum" : [ "AD", "APRON", "AIRSPACE", "CHART", "COM", "IAP", "NAV", "OBST", "ODP", "ROUTE", "RWY", "SECURITY", "SID", "SPECIAL", "STAR", "SVC", "TWY", "VFP", "CONSTRUCTION", "LTA" ]
    },
    "notam_function" : {
      "title" : "NOTAM Function",
      "description" : "Function of the NOTAM (New, Replacement, Cancelled)",
      "type" : "string",
      "enum" : [ "NOTAMN", "NOTAMR", "NOTAMC" ]
    },
    "valid_time_begin" : {
      "title" : "Valid time (begin)",
      "format" : "date-time",
      "type" : "string"
    },
    "valid_time_end" : {
      "title" : "Valid time (end)",
      "format" : "date-time",
      "type" : "string"
    },
    "text" : {
      "title" : "Text",
      "description" : "NOTAM condition text.",
      "type" : "string"
    },
    "selection_code" : {
      "title" : "Q Code",
      "description" : "Q Code value for the NOTAM.",
      "type" : "string"
    },
    "year" : {
      "title" : "Year",
      "description" : "NOTAM year values per ICAO Annex-15.",
      "type" : "string"
    },
    "number" : {
      "title" : "Number",
      "description" : "NOTAM number value per ICAO Annex-15.",
      "type" : "integer"
    },
    "scenario" : {
      "title" : "Scenario",
      "description" : "Identifier of the event scenario used for digital encoding. The mapping can be found in the Event Scenario documents.",
      "type" : "string"
    },
    "affected_fir" : {
      "title" : "Flight Information Region",
      "description" : "Flight Information Region (FIR) that is impacted by the NOTAM.",
      "type" : "string"
    },
    "location" : {
      "title" : "Location designator",
      "description" : "NOTAM location designator of the affected airport/heliport or facility.",
      "type" : "string"
    },
    "icao_location" : {
      "title" : "ICAO location designator",
      "description" : "ICAO location designator, if published.",
      "type" : "string"
    },
    "series" : {
      "title" : "Series",
      "description" : "NOTAM series value per International Civil Aviation Organization (ICAO) Annex-15.",
      "type" : "string"
    },
    "type" : {
      "title" : "Type",
      "description" : "NOTAM type value per ICAO Annex-15. Accepted values are: New (N), Replace (R), Cancel (C).",
      "type" : "string",
      "enum" : [ "N", "R", "C" ]
    },
    "issued" : {
      "title" : "Issued",
      "description" : "Issue date/time of the NOTAM.",
      "format" : "date-time",
      "type" : "string"
    },
    "traffic" : {
      "title" : "Traffic",
      "description" : "NOTAM traffic value per ICAO Annex-15.",
      "type" : "string"
    },
    "purpose" : {
      "title" : "Purpose",
      "description" : "NOTAM purpose value per ICAO Annex-15.",
      "type" : "string"
    },
    "scope" : {
      "title" : "Scope",
      "description" : "NOTAM scope value per ICAO Annex-15.",
      "type" : "string"
    },
    "minimum_fl" : {
      "title" : "Minimum flight level",
      "description" : "NOTAM minimum flight level value per ICAO Annex-15.",
      "type" : "string"
    },
    "maximum_fl" : {
      "title" : "Maximum flight level",
      "description" : "NOTAM maximum flight level value per ICAO Annex-15.",
      "type" : "string"
    },
    "coordinates" : {
      "title" : "Coordinates",
      "type" : "string"
    },
    "radius" : {
      "title" : "Radius",
      "type" : "string"
    },
    "schedule" : {
      "title" : "Schedule",
      "description" : "Contains a schedule of activity/outage if the hours of effect are less than 24 hours a day.",
      "type" : "string"
    },
    "lower_limit" : {
      "title" : "Lower limit",
      "description" : "Specifies the lower height restriction of the NOTAM.",
      "type" : "string"
    },
    "upper_limit" : {
      "title" : "Upper limit",
      "description" : "Specifies the upper height restriction of the NOTAM.",
      "type" : "string"
    }
  },
  "additionalProperties" : false,
  "type" : "object",
  "$schema" : "https://json-schema.org/draft/2019-09/schema",
  "$id" : "https://t18.ldproxy.net/d103_fns/collections/notam/sortables"
}

{
  "id": "nyc-concrete-surface-areas",
  "title": "Concrete apron, runway and taxiway elements at NYC area airports",
  "queries": [
    {"collections":["apronelement"]},
    {"collections":["runwayelement"]},
    {"collections":["taxiwayelement"]}
  ],
  "filter": {
    "op": "and",
    "args": [
      { "op": "=", "args": [{ "property": "composition" }, "CONC"] },
      { "op": "in", "args": [{ "property": "airport" }, ["JFK", "EWR", "LGA"]] }
    ]
  },
  "properties": ["geometry", "airport", "type"],
  "limit": 1000
}

{
  "id": "surface-areas",
  "title": "Apron, runway and taxiway elements of a specific type at one or more airports.",
  "queries": [
    { "collections": ["apronelement"]   },
    { "collections": ["runwayelement"]  },
    { "collections": ["taxiwayelement"] }
  ],
  "filter": {
    "op": "and",
    "args": [
      {
        "op": "=",
        "args": [
          {"property": "type"},
          {
            "$parameter": {
              "type": {
                "title": "Type of the apron, runway or taxiway element",
                "description": "The following types are distinguished: normal use, parking, shoulder, intersection.",
                "type": "string",
                "enum": ["NORMAL", "PARKING", "SHOULD", "INTERS"],
                "default": "NORMAL"
              }
            }
          }
        ]
      },
      {
        "op": "in",
        "args": [
          {"property": "airport"},
          {
            "$parameter": {
              "airports": {
                "title": "Airports",
                "description": "The 3-letter IATA airport codes or the airports to filter. Specify multiple values as a comma-separated list.",
                "type": "array",
                "items": {
                  "type": "string",
                  "enum": ["JFK", "EWR", "LGA", "BOS", "PIT", "PHL", "DCA", "BWI", "IAD"]
                },
                "default": ["JFK", "EWR", "LGA"]
              }
            }
          }
        ]
      }
    ]
  },
  "properties": ["geometry", "airport", "type", "composition"],
  "limit": 1000
}

{
  "id": "generic-notam-query",
  "title": "filter NOTAMS",
  "description": "NOTAMs filtered by location, by keyword and by time interval.",
  "collections": ["notam"],
  "filter": {
    "op": "and",
    "args": [
      {
        "op": "s_intersects",
        "args": [
          {"property": "geometry"},
          {
            "$parameter": {
              "geometry": {
                "title": "NOTAM geometry",
                "description": "NOTAMs with a location that intersects the geometry are selected.",
                "type": "object",
                "required": ["type", "coordinates"],
                "properties": { "type": {"type": "string"}, "coordinates": {"type": "array"} },
                "default": {
                  "type": "Polygon",
                  "coordinates": [
                    [
                      [ -74.07517, 41.03269 ],
                      [ -73.96881, 41.06885 ],
                      [ -73.85509, 41.08851 ],
                      [ -73.73847, 41.09087 ],
                      [ -73.62351, 41.07587 ],
                      [ -73.51473, 41.04408 ],
                      [ -73.41637, 40.99675 ],
                      [ -73.33224, 40.93573 ],
                      [ -73.26558, 40.86340 ],
                      [ -73.21892, 40.78257 ],
                      [ -73.19399, 40.69637 ],
                      [ -73.19167, 40.60812 ],
                      [ -73.21195, 40.52123 ],
                      [ -73.25399, 40.43900 ],
                      [ -73.31608, 40.36459 ],
                      [ -73.39583, 40.30082 ],
                      [ -73.49016, 40.25010 ],
                      [ -77.14409, 38.57116 ],
                      [ -77.24494, 38.53223 ],
                      [ -77.35344, 38.50940 ],
                      [ -77.46549, 38.50353 ],
                      [ -77.57685, 38.51484 ],
                      [ -77.68334, 38.54291 ],
                      [ -77.78091, 38.58666 ],
                      [ -77.86586, 38.64445 ],
                      [ -77.93495, 38.71408 ],
                      [ -77.98551, 38.79290 ],
                      [ -78.01555, 38.87792 ],
                      [ -78.02384, 38.96588 ],
                      [ -78.01000, 39.05341 ],
                      [ -77.97446, 39.13715 ],
                      [ -77.91854, 39.21387 ],
                      [ -77.84433, 39.28058 ],
                      [ -77.75467, 39.33469 ],
                      [ -74.07517, 41.03269 ]
                    ]
                  ]
                }
              }
            }
          }
        ]
      },
      {
        "op": "in",
        "args": [
          {"property": "notam_keyword"},
          {
            "$parameter": {
              "notamKeywords": {
                "title": "NOTAM keywords",
                "description": "A list of NOTAM keywords to filter only NOTAMs with the selected keywords. For more information about the NOTAM keywords see https://www.faa.gov/air_traffic/publications/atpubs/notam_html/chap1_section_2.html.",
                "type": "array",
                "items": {
                  "type": "string",
                  "enum": [
                    "AD", "APRON", "AIRSPACE", "CHART", "COM", "IAP", "NAV", "OBST", "ODP",
                    "ROUTE", "RWY", "SECURITY", "SID", "SPECIAL", "STAR", "SVC", "TWY", "VFP",
                    "CONSTRUCTION", "LTA"
                  ]
                },
                "default": ["AIRSPACE"]
              }
            }
          }
        ]
      },
      {
        "op": "t_intersects",
        "args": [
          {
            "interval": [ {"property": "valid_time_begin"}, {"property": "valid_time_end"} ]
          },
          {
            "interval": [
              {
                "$parameter": {
                  "begin": {
                    "title": "Begin of time interval",
                    "description": "UTC timestamp for the begin of time interval written according to RFC 3339. Example: '2022-08-18T15:05:00Z'. Only NOTAMs with temporal validity in the time interval are selected.",
                    "type": "string",
                    "format": "date-time",
                    "default": "2022-08-18T15:05:00Z"
                  }
                }
              },
              {
                "$parameter": {
                  "end": {
                    "title": "End of time interval",
                    "description": "UTC timestamp for the end of time interval written according to RFC 3339. Example: '2022-08-18T16:30:00Z'. Only NOTAMs with temporal validity in the time interval are selected.",
                    "type": "string",
                    "format": "date-time",
                    "default": "2022-08-18T16:30:00Z"
                  }
                }
              }
            ]
          }
        ]
      }
    ]
  },
  "limit": 10
}

{
    "id": "features-yb-airport",
    "title": "All features of an airport",
    "queries": [
        { "collections": ["airportheliport"      ] },
        { "collections": ["apron"                ] },
        { "collections": ["apronelement"         ] },
        { "collections": ["guidanceline"         ] },
        { "collections": ["runway"               ] },
        { "collections": ["runwayblastpad"       ] },
        { "collections": ["runwaycentrelinepoint"] },
        { "collections": ["runwayelement"        ] },
        { "collections": ["runwaymarking"        ] },
        { "collections": ["surveycontrolpoint"   ] },
        { "collections": ["taxiholdingposition"  ] },
        { "collections": ["taxiway"              ] },
        { "collections": ["taxiwayelement"       ] },
        { "collections": ["taxiwaymarking"       ] },
        { "collections": ["verticalstructure"    ] }
    ],
    "filter": {
        "op": "=",
        "args": [
            {"property": "airport"},
            {
                "$parameter": {
                    "airport": {
                        "title": "Airport",
                        "description": "The 3-letter IATA airport codes of available airports.",
                        "type": "string",
                        "enum": ["JFK", "EWR", "LGA", "BOS", "PIT", "PHL", "DCA", "BWI", "IAD"],
                        "default": "JFK"
                    }
                }
            }
        ]
    },
    "limit": 10000
}

8.4.  HTML Support

Support for HTML in the OGC API – Features Standard is recommended. A server that supports HTML will support browsing the data with a web browser and will enable search engines to crawl and index the dataset.

For these reasons, support for both the HTML and the JSON resources were implemented during Testbed 18. As a result, the site itself already provides a Business Client.

The HTML representation of the Stored Queries resource included an HTML form for each stored query. The form includes:

• the descriptive elements (title and descriptions);

• a drop-down list for the desired response format for the feature collection; and

• For each parameter:

  • the descriptive elements (title, description and data type); and

  • a field, drop-down list or multi-selection list depending on the schema of the parameter.