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Core Context Management

If you want your application to be “smart”, you have to first make it “aware”. FIWARE provides you with means to produce, gather, publish and consume context information at large scale and exploit it to transform your application into a truly smart application.

Context information is represented through values assigned to attributes that characterize those entities relevant to your application.

To learn more about Core Context Management Enablers, check out the documentation

Orion

License

:octocat: Git Repository 🐳 Docker Hub 📚 Documentation 🎓 Academy 🎯 Roadmap

What is Orion?

The Orion Context Broker is an implementation of the Publish/Subscribe Context Broker GE, providing the NGSI interfaces. Using these interfaces, clients can do several operations:

  • Register context producer applications, e.g. a temperature sensor within a room
  • Update context information, e.g. send updates of temperature
  • Being notified when changes on context information take place (e.g. the temperature has changed) or with a given frequency (e.g. get the temperature each minute)
  • Query context information. The Orion Context Broker stores context information updated from applications, so queries are resolved based on that information.

Why Use Orion?

If you are developing a Data/Context scenario, a broker like the Orion Context Broker is a must. You would need a component in the architecture able to mediate between consumer producers (e.g. sensors) and the context consumer applications (e.g. an mobile phone applications taking advantage of the context information provided by the sensors). The Orion Context Broker fulfils this functionality in your architecture.

Orion is an implementation of the FIWARE Publish/Subscribe Context Broker Generic Enabler. More specifically, Orion implements the following API and Open Specification:

For context data management, NGSI and the Orion context broker have been accepted as standards or recommendations by a variety of independent standards bodies, for example, GSMA recommends NSGI as a standard for relevant parts of their IoT Big Data architecture and promotes the Orion Context Broker as the primary example of the standard and NGSI specification has been selected by the European Commission as a CEF Building Block for the implementation of new smart applications and Public Administration.

The use of the Orion context broker is mandatory for any platform or solution to be labelled as “Powered by FIWARE” within the FIWARE marketplace

Quality Assurance

The Orion project is part of FIWARE and has been rated as follows:

  • Version Tested:
  • Documentation:
  • Responsiveness:
  • FIWARE Testing:

Cygnus

License

:octocat: Git Repository 🐳 Docker Hub 📚 Documentation 🎓 Academy 🎯 Roadmap

What is Cygnus?

Cygnus is a connector in charge of persisting context data sources into other third-party databases and storage systems, creating a historical view of the context. Internally, Cygnus is based on Apache Flume, Flume is a data flow system based on the concepts of flow-based programming. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. It was built to automate the flow of data between systems. While the term 'dataflow' can be used in a variety of contexts, we use it here to mean the automated and managed flow of information between systems.

Each data persistence agent within Cygnus is composed of three parts - a listener or source in charge of receiving the data, a channel where the source puts the data once it has been transformed into a Flume event, and a sink, which takes Flume events from the channel in order to persist the data within its body into a third-party storage.

Why Use Cygnus?

Persisting historical context data is useful for big data analysis - it can be used to discover trends, or data can be sampled and aggregated to remove the influence of outlying data measurements. However within each Smart Solution, the significance of each entity type will differ and entities and attributes may need to be sampled at different rates.

Since the business requirements for using context data differ from application to application, there is no one standard use case for historical data persistence. Therefore rather than overloading the context broker with the job of historical context data persistence, this role has been separated out into a separate, highly configurable component - Cygnus.

Cygnus plays the role of a connector between the Orion Context Broker (which is an NGSI source of data) and a wide range of external systems such as MySQL, MongoDB etc. You should use Cygnus if you need to process and persist context data so that you can keep a historical record. Cygnus can also be used the filter and repost context data back into Orion.

Quality Assurance

The Cygnus project is part of FIWARE and has been rated as follows:

  • Version Tested:
  • Documentation:
  • Responsiveness:
  • FIWARE Testing:

STH Comet

License

:octocat: Git Repository 🐳 Docker Hub 📚 Documentation 🎓 Academy 🎯 Roadmap

What is STH Comet?

Short Time Historic (STH) - Comet is a component of the FIWARE ecosystem in charge of managing (storing and retrieving) historical raw and aggregated time series context information about the evolution in time of context data (i.e., entity attribute values) registered in an Orion Context Broker instance.

Why use STH Comet?

The creation and analysis of trend data is a common requirement of context-driven systems - therefore the FIWARE platform offers a generic enabler (STH-Comet) specifically to deal with the issue of persisting and interpreting time series data.

Within the FIWARE platform, historical context data can be persisted to a database - this results in a series of data points. Each time-stamped data point represents the state of context entities at a given moment in time. The individual data points are relatively meaningless on their own, it is only through combining a series data points that meaningful statistics such as maxima, minima and trends can be observed.

Quality Assurance

The STH-Comet project is part of FIWARE and has been rated as follows:

  • Version Tested:
  • Documentation:
  • Responsiveness:
  • FIWARE Testing:

🌱 QuantumLeap (Incubated)

License

:octocat: Git Repository 🐳 Docker Hub 📚 Documentation 🎓 Academy 🎯 Roadmap

What is QuantumLeap?

The QuantumLeap Generic Enabler focuses on persisting historical context data into time-series databases such as CrateDB with reference to maintaining a scalable architecture and compatibility with visualization tools such as Grafana

Why use QuantumLeap?

The appropriate use of time series data analysis will depend on your use case and the reliability of the data measurements you receive. Time series data analysis can be used to answer questions such as:

  • What was the maximum measurement of a device within a given time period?
  • What was the average measurement of a device within a given time period?
  • What was the sum of the measurements sent by a device within a given time period?

QuantumLeap offers great flexibility in measuring and monitoring time-series data and leverages existing time-series-based databases to be able to support complex queries such as cross-entity queries (e.g. an average of averages)

The QuantumLeap project is part of FIWARE and will be rated as part of the next release.

🌱 Draco (Incubated)

License

:octocat: Git Repository 🐳 Docker Hub 📚 Documentation 🎓 Academy 🎯 Roadmap

What is Draco?

Draco is a connector in charge of persisting context data sources into other third-party databases and storage systems, creating a historical view of the context. Internally, Draco is based on Apache NiFi. NiFi is a popular framework for data management and processing from multiple sources.

Draco plays the role of a connector between the Orion Context Broker (which is an NGSI source of data) source of data) and a wide range of external systems such as MySQL, MongoDB etc. You can use Draco if you need to process and persist context data so that you can keep a historical record. Draco can also be used to filter and repost context data back into Orion.

Why Use Draco?

Persisting historical context data is useful for big data analysis - it can be used to discover trends, or data can be sampled and aggregated to remove the influence of outlying data measurements. However within each Smart Solution, the significance of each entity type will differ and entities and attributes may need to be sampled at different rates.

Since the business requirements for using context data differ from application to application, there is no one standard use case for historical data persistence. Therefore rather than overloading the context broker with the job of historical context data persistence, this role has been separated out into another enabler. Amongst the advantages of the component, Draco offers a flexible graphical interface so it is possible to amend your data flows according to your current business needs.

Quality Assurance

The Draco project is part of FIWARE and has been rated as follows:

  • Version Tested:
  • Documentation:
  • Responsiveness:
  • FIWARE Testing:
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