SW
MyPCH Home News About

Use case - dataflows

Get started

Quick up-and-running

In Get started you can find a description to get quick up-and-running on a Linux machine:

  • Prerequisites
    • A machine with Ubuntu and how to install Docker
  • Tidepool
    • Create a local instans of the platform with a local Kubernetes cluster
    • Install uploader and web
  • Semantic Container
    • Install semantic container


Integration

Extract data from device using Tidepool and use in Semantic Container dataflows
ui-integration

Setup a single Linux integration demo
When finished uploading then you can do the following: (refer to the orange number in figure):

  1. Grab the data for further use in the dataflows
  2. See the data in Tidepool Web

In the dataflow Tidepool integration you can find a demonstration of these capabilities:
  • reading data from a Tidepool supported device
  • avoding duplicates on data upload into a Semantic Container
  • providing a Diabetes specific Usage Policy
  • generating a Provenance trail specific to diabetes devices


Personal data

Store data to Semantic Container and get insigths in PDS
In the dataflow Personal_Data you can find a demonstration of these capabilities:
  • converting diabetes data into FHIR-compliant RDF format
  • accessing data through a SPARQL endpoint
  • uploading data to the OwnYourData Data Vault
  • strong encryption of diabetes data
  • authentication methods for diabetes data
  • visualizing data in a Knowledge Graph
  • gain insights from personal data through individual analysis
  • access and process diabetes data with standard tools

Data donation

Share data and identify if data leak

In the dataflow Data_Donation you can find a demonstration of these capabilities:

  • avoding duplicates on data upload
  • providing a Diabetes specific Usage Policy
  • generating a Provenance trail specific to diabetes devices
  • matching Usage Policies specifically based on subsets for Data Categories
  • applying and verifying digital watermarks

Data tracing

Share data and aggreate/anonymize data

In the dataflow Data_Tracing you can find a demonstration of these capabilities:

  • PwDs upload their local diabetes data to an aggregation/anonymization service
  • aggregating/anonymizing data in a Semantic Container
  • a service provider transfers selected (aggregated) data to a 3rd party
  • each PwD traces his/her own data and compiles a usage report

Proxy

Use a proxy to handle integration

We have defined a concept of a health data store (HDS) to merit of higher protection for sensitive data as health data. For scenarios of multiple users in the same environment we have developed a template that can be used to demonstrate the workflow as swagger response.
In Proxy you can find a demonstration of these capabilities:

  • template to build a demo with Python using Flask web application framework
  • local demo and upload to a docker repository available for the kubernetes cluster
  • design and security considerations for deployment for building an online demo

proposal

Demonstrations

Dataflows

  • Get started
  • Integration
  • Personal data
  • Data donation
  • Data Tracing
  • Proxy