Playground examples

The following examples are intended to show how to use playground components and how they can be combined to build larger systems.

They are grouped by categories to help discovery of appropriate examples from different viewpoints.

Note: There is not a separate section for the VSS data model because the vast majority of the examples will be making use of it.

Getting started

Name Relationship to the category
Docker sanity test Simple sanity test for the Playground Docker deployment
Hello-world Simple “hello-world” example

Data Layer, Processing and Analysis

Topic examples: Data Reduction, Data Quality, Events, Data Streams etc.

Name Relationship to the category
vehicle-speed-downsample-iotdb Accurately down-sample a timeseries of pre-recorded high frequency VSS Vehicle.Speed data using the IoTDB Data Quality Library

Tip: well you wait for more examples consider how you could use the IoTDB data processing functions.

Knowledge Layer, Reasoning and Data Models

Topic examples: Knowledge Layer Connector, Data Layer Connector etc.

Feeders

Topic examples: Virtual signal platforms, VISSR etc.

Name Relationship to the category
RemotiveLabs feeder Example bridge that streams vehicle data from the RemotiveLabs cloud platform into the IoTDB data store

COVESA Touchpoints

Topic examples: Low level vehicle abstraction, Mobile devices, Car2Cloud / Cloud etc.

COVESA Technologies

Topic examples: vsome/ip (SOME/IP), uServices, Vehicle API, VISS etc.

Databases

Topic examples: Apache IoTDB, MongoDB Realm, Redis/SQLite/memcache etc.

Name Relationship to the category
vehicle-speed-downsample-iotdb Using the IoTDB Data Quality Library for advanced (VSS) timeseries data processing
RemotiveLabs feeder Example of streaming (writing) southbound (VSS) timeseries data into IoTDB

Frameworks / Protocols

Topic examples: vsome/ip (SOME/IP), VISS, uServices/uProtocol/Capabilities, Vehicle API, MQTT, Kafka, Apache Zeppelin etc.

Big data

Topic examples: Hadoop, Flink, Spark, Cloud DB, Nifi etc.

Other examples

Topics that do not fit into the groups above.