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  1. Model Repository

    Model Repository is a REST service that provide storage and lookup operations for domain model documents (XMI, JSON etc). The domain model is represented … :9090/repo/mr/xmi/ curl i X GET localhost:9090/repo/mr/latest/json/ curl i X GET localhost:9090/repo/mr/latest/xmi/ Model Repository Distribution From our artifact
  2. Model Repository Home

    Wiki Domain and Deployment Models Serialization Format Model Repository Recent space activity Space contributors
  3. Domain and Deployment Models

    the repository functionality we can create deployment model in JSON format. 3. Repository Functionality The Model Repository section describes how the service can … 1. Overview The goal of this service is to provide a common framework for easy creation and management of Domain and Deployment Models for LS. Almost everyone
  4. LinkSmart® Model Repository

  5. Data Archiver

    in the catalog and persists their output in the HDS. Use Cases Data Archiver can be configured to use either LinkSmart® Resource Catalog or LinkSmart® Model Repository … to data streams of all Resources that have MQTT protocol defined, unless the Resource has meta.dataarchiverignore set to true. Model Repository When configured
  6. What is LinkSmart

    and monitoring MR: Model Repository for storing and managing domain and deployment models describing devices and services integrated into a particular application context
    HomeOct 06, 2017
  7. Serialization Format Specification

    NOTE: The format described below is based on the assumption that the Deployment Model (the annotated domain model) can be serialized as a directed graph. At the moment, only elements from the LinkSmart model (the Deployment Model) are serialized/extracted, but this format can also be used to serialize classes
  8. IoT Agent Architecture (under construction)

    asynchronous messages to which the IoT Learning Agent (LA) or LAs are subscribed. On the other hand, the Models subscribe to the data, provided by the clients … process of the Model through the Learning Request. While, each Model is responsible of evaluation themselves, and this process is done by the Evaluator. The current
    LinkSmart® IoT AgentsOct 26, 2017
  9. Docker

    Clone lslcdev repository and go to deployment directory: git clone https://linksmart.eu/redmine/linksmartopensource/linksmartlocalconnect/lslcdev.git cd lslcdev/deployment To start all localconnect components together with a Mosquitto MQTT Broker https://mosquitto.org/: dockercompose up For individual components (e.g
    LinkSmart® LocalConnectJul 11, 2017
  10. Grafana Datasource Plugin

    as a service, clone the repository into Grafana's plugin directory http://docs.grafana.org/plugins/installation/grafanaplugindirectory and restart Grafana. git