Consume IoT Offerings

Assuming, you want to create a new parking app for the city of Barcelona that is supposed to display parking data on a web dashboard for different places in the city. Using the BIG IoT Consumer Lib, it is very easy to make your service query the BIG IoT Marketplace relevant data sources and access them.

The Consumer Lib offers the following main functionalities:

  • Discovering offerings at the Marketplace according to a search specification
  • Subscribing to offerings
  • Accessing offerings in per request or in a continuous fashion

Authentication on a BIG IoT Marketplace

To get started with the Consumer Lib, you have to create an instance of the Consumer class. The constructor requires your specific consumer ID and a marketplace URI. To authenticate with the marketplace, you have to use the authenticate method provided by the consumer object. This method requires the marketplace API key and a consumer certificate, which you both received after registration on the BIG IoT Marketplace portal.

Consumer consumer = new Consumer("Barcelona_Parking", "");
// Authenticate on the marketplace 

If you want to connect to a different marketplace, just repeat the previous steps and create another Consumer object.

Creating a Query for Offering Descriptions

Now, that you are authenticated at the marketplace, you can search for relevant parking sensor data to feed into your service.

To do that, you query the marketplace using the OfferingQuery object. The query gets constructed using a builder pattern which first, creates the empty OfferingQuery object that is completed with additional query filters, such as a specific search region, a desired accounting type, a maximum price, etc. The marketplace will later retrieve all matching offerings for this query. In this example, the query is quite simple however it can be more complex in other situations.

OfferingQuery query = Consumer.createOfferingQuery()
	.withInformation(new Information("Parking Information Query", ""))

First we create a query. The type of offerings returned should be of type The types of offerings, which are available can be evaluated using the online documentation of the BIG IoT Marketplace.
The query is using a region filter that selects only offerings that are provided in the city of Barcelona. Also the pricing mode should be based on the number of accesses and not, for example, on a monthly fee. The price should not exceed 0.1 cents per access and the data license should be the Open Data License. The Consumer Lib offers you Enum classes that you can consult to see, which other licenses or accounting types are available.

Querying the Marketplace

To execute the query on the marketplace, the Consumer object provides multiple options.
The dedicated method for this is discover, which has different signatures to take different programming preferences into account.

CompletableFuture<List<SubscribableOfferingDescription>> discover(IOfferingQuery query);
void discover(IOfferingQuery query, DiscoverResponseHandler onSuccess, DiscoverResponseErrorHandler onFailure);
void discover(IOfferingQuery query, DiscoverResponseHandler onSuccess);

The first version uses a CompletableFuture as a return type, which is a promise on a list of OfferingDescriptions, which is part of the functional programming styles introduced in Java 8. The other two variants are using callback mechanisms. The following code shows how to discover offerings getting them as a CompletableFuture on the list of OfferingDescriptions:

CompletableFuture<List<SubscribableOfferingDescription>> listFuture =;
List<SubscribableOfferingDescription> offerings = listFuture.get();
Offering offering = offerings.get(0).subscribe().get();

The discover call is actually non-blocking. So, you could do something in between, e.g. handing over the CompletableFuture object to your management thread. Or alternatively, you can directly receive the list by calling the get method. This call is blocking and will wait on the list of OfferingDescriptions. The motivation of using CompletableFuture here is, that you can come easily from an asynchronous behavior to a blocking behavior and further you can utilize reactive concepts if you want. For example by calling thenApply as a monad on the CompletableFuture object allows you to apply functions once the list of offering descriptions is received.


Before you can utilize an offering, you have to subscribe to the OfferingDescription object. Subscription is done through the correspondent subscribe method which returns an Offering object. The offering object provides different access methods as described later. You can alternatively also use callbacks for discovering offerings. Here is an example how to achieve that:,l) -> { 

The callback function in this example again just prints the returned offering descriptions, however usually you would provide your offering selection logic here, that selects the appropriate offerings for your use case. The example utilizes the functional programming features introduced in Java 8. With lambdas you can express functionality without much boilerplate code. Alternatively every other instance of DiscoverResponseHandler is accepted by discover.

As a side note: You can reuse your query object for subsequent queries. Only if you want to change something regarding the query you have to create a new OfferingQuery object.

Using SelectionCriteria

By using the SelectionCriteria class, you can specify a rule which is used by the BIG IoT Lib to filter offerings. You can define the selection criteria based on you service logic needs, by creating a new SelectionCriteria instance. To create a new selection, you use the OfferingSelector class, which accepts an arbitrary amount of SelectionCriteria objects. In the example below, we discover a list of offerings and apply an OfferingSelector, that selects based on cheapest offerings that have the most permissive license.
.thenApply((list) -> OfferingSelector

Accessing Offerings

Before we describe how to access an offering that was retrieved from the marketplace, it makes sense that you look at the different access concepts provided. The IOffering interface provides the following signatures for access:

void accessOneTime(AccessParameters parameters, AccessResponseSuccessHandler onSuccess);  
void accessOneTime(AccessParameters parameters, AccessResponseSuccessHandler onSuccess, AccessResponseFailureHandler onFailure);   
CompletableFuture<AccessResponse> accessOneTime(AccessParameters parameters);
IAccessFeed accessContinuously(AccessParameters parameters, Duration lifetime, FeedNotificationSuccessHandler onSuccess, FeedNotificationFailureHandler onFailure);

To access offerings, we distinguish between two types: one-time access and continuous access. One-time access means that you execute an access request every time you want to get new data. Continuous access refers to the reception of data as a feed. For one-time access, the Consumer Lib supports again different programming styles. You can either use callback functions for pure asynchronous access or you can use a CompletableFuture to do reactive programming or even having a blocking call.

In either case, you have to provide an AccessParameters object for the access call. In includes the parameters, which will be passed on to the provider. Typically they are needed to filter the output or configure the access.

Here is an example, how to access the parking offering we retrieved earlier:

/* Create a hashmap to hold parameters for filtering access*/
AccessParameters accessParameters = AccessParameters.create()
.addRdfTypeValue(new RDFType("schema:longitude"), 12.3)
.addRdfTypeValue(new RDFType("schema:latitude"), 42.73);

/* Execute one time access and print the result */
CompletableFuture<AccessResponse> response = offering.accessOneTime(accessParameters);
response.thenAccept((r) -> log("One time Offering access: " + r.asJsonNode().size() + " elements received. "));

As you can see, accessing an offering can be that simple. We use the accessOneTime method and pass the parameters object that restricts the access to the specified longitude and latitude coordinates. Since we use accessOneTime returning a CompletableFuture, we can apply a function on the result. Here we simply output the response content to the console. Note that the response object is of the type JsonNode, which already includes the parsed response message and provides functionality for traversing the response.

Continuous Access of Offerings

Since we want to show the returned parking data in real time , it would be even nicer if we could access the parking data continuously. Here we describe how this can be done:

Duration feedDuration = Duration.standardHours(1);
Duration feedInterval = Duration.standardSeconds(2);

AccessFeed accessFeed = offering
	(f,r)->log("Incoming feed data: "+ r.asJsonNode().size() + " elements received. "),
	(f,r)->log("Feed operation failed")

You notice that the procedure is very similar to the access on a per-request base. We use the accessContinuous method of the OfferingQuery object which requires the accessParameters object, a duration and feed interval, a success callback and a failure callback in case something went wrong. accessContinuous creates a feed, which requires a lifecycle management. The accessFeed object has functionality for stopping (stop), resuming (resume) and getting the status of a feed subscription (status), which we don’t want to use now.

If you want to stop accessing an offering, you can unsubscribe accordingly.


Make sure to always call the terminate method of the consumer object before stopping your application in order to terminate any open network connections.

Automated mapping to POJO

The following example will show you, how you can access offerings and let the BIG IoT Lib automatically match the output parameters to a Parking POJO.

CompletableFuture<AccessResponse> response = offering.accessOneTime(accessParameters);
//Mapping the response automatically to your POJO
List<ParkingResultAnnotated> parkingResult = response.get().map(MyParkingResultPojoAnnotated.class);

For the access request, you use the map method, which accepts an annotated POJO class. The lib will now map the response data from the parking provider to the POJO MyParkingResultAnnotated.

public class MyParkingResultAnnotated {
	public static class Coordinate {
		public double latitude;
		public double longitude;
	public MyParkingResultPojoAnnotated.Coordinate coordinate;
	public double distance;
	public String status;	

In order to map semantic types to your POJO’s types, you can use the ResponseMappingType class, which is parameterized with the semantic type you want to map. In this case, we would map the complex type geoCoordinates from the Complex Parking Offering to the Coordinate class.

Another option, instead of using an automated mapping approach is to do the mapping manually. You can see how this works in the next example (note that we use the non-annotated version of the ParkingResult).

CompletableFuture<AccessResponse> response = offering.accessOneTime(accessParameters);
List parkingResult = response.get()
  .map(MyParkingResultPojo.class, OutputMapping
  .addTypeMapping("schema:geoCoordinates", "coordinate")
  .addTypeMapping("datex:distanceFromParkingSpace", "distance")
  .addTypeMapping("datex:parkingSpaceStatus", "status"));

To provide the mapping manually, you use the addTypeMapping method, for each semantic type from the provider’s output data elements so that the lib can match it to your POJO.

A third option is to provide your own mapping which means to cherry-pick the required fields from the access response. In the example, we map latitude from the complex type geoCoordinates to the field coordinate of our parking result POJO. Also, we map the field distance to the POJO field meters.

CompletableFuture<AccessResponse> response = offering.accessOneTime(accessParameters);
List parkingResult3 = response.get().map(AlternativeParkingPojo.class, OutputMapping.create()
	.addNameMapping("geoCoordinates.latitude", "coordinates.latitude")
	.addNameMapping("geoCoordinates.longitude", "coordinates.longitude")
	.addNameMapping("distance", "meters"));

That’s it! You have just learned how to use the BIG IoT Library as a data provider as well as a data consumer.