## Intro
Pryv.io is a comprehensive solution for managing this particularly sensitive type of data stream, focusing both on data privacy and decentralization.
SemPryv is a system that provides semantization mechanism for enriching Pryv.io personal data streams with standardized specialized vocabularies from third-party providers. It relies on third providers of semantic concepts, and includes rule-based mechanisms for facilitating the semantization process. This implementation is pluggable to the existing Pryv.io platforms.
This repository contains the code of the SemPryv project, developed by the AISLab group with Pryv.
This development is the result of a research project "Semi-automatic semantic enrichment of personal data streams" published online.
SemPryv can be tested online at: https://sempryv.ehealth.hevs.ch/en/auth
SemPryv aims to stream data enrichment by providing semantic annotation capabilities on the Pryv.io middleware. The semantic annotation process associates high-level ontology concepts to the stream events. It can be done in two ways:
- Manually by searching well-known ontology providers (such as bioportal), or
- Semi-automated where annotation suggestions are provided to the users. These suggestions are derived by predefined rules that experts can modify and save them in the system’s knowledge graph.
- Fully automated suggestions: They derived by machine learning models that have been trained on synthetic data from mobile apps combined by users existing annotations.
The architecture of SemPryv is depicted in the picture below. SemPryv has two main components, a web User Interface for end users and experts and a back end that exposes the core services as a REST API to external applications. It also connects to a series of providers for semantic vocabularies and also includes endpoints dedicated for the import/export of HL7 FHIR - compliant data streams, represented as bundle collections of observations. Since the annotations are ready, streams can be exposed to the Pryv again with all of their metadata.
Figure 1: Sempryv Architecture
Every time a user connects with SemPryv she accesses the hierarchical structure of her streams and the corresponding events. The user must first declare the annotation type in the corresponding field and then she can search for a semantic annotation into existing ontologies or look for any possible suggestions. A detailed semantization example of two streams is described below:
-
User accesses her streams and events through login process and providing her authorization token. The data consist of two streams: Body Temperature (BT) and Heart (H). Heart has also a child-stream named Heart Rate (HR). BT and H consist of one text/note event each, while HR has two events, a note/txt and one with type position/wgs84. Sempryv proposes the already declared types that inherits from Pryv events (note/txt and position/wgs84) or users can write the type of the annotation by their own as well (figure 2)
-
Search for annotation: Given the annotation type “note/txt” the user now can add the actual annotation for the specific type by clicking the ADD button in order to search available by the system ontologies and add a semantic code. In our example the user searches for an annotation type for “temperature” and a full list of the proposed semantics is provided, queried from him (figure 3).
User checks and confirms two of the suggestions: SNOMEDCT | 722490005 and SNOMEDCT | 56342008 and so the final annotation is depicted in figure 4. The similar is done for the heart and heart rate stream.
Figure 2: proposed terms based on stream events
Figure 3: query suggestions
Figure 4: Annotated streams
SemPryv includes the possibility of using predefined rules expressed in its knowledge graph. The rules are defined by administrators or experts in a json format like this:
"graph": [{
"@id": "pryv:bodyTemperature",
"@type": "skos:Concept",
"skos:notation": "note/txt",
"skos:broader": "pryv:temperature",
"skos:closeMatch": "snomed-ct:386725007"
},
{
"@id": "pryv:heart",
"@type": "skos:Concept",
"skos:notation": "note/txt",
"skos:closeMatch": "snomed-ct:36407500"
},
{
"@id": "someRuleSet1",
"pryv:pathExpression": "body temperature/",
"pryv:mapping": ["pryv:temperature"]
},
{
"@id": "someRuleSet2",
"pryv:pathExpression": "heart/",
"pryv:mapping": ["pryv:heart"]
}]These rules essentially allow the definition of close terms from different ontologies. We observe that if the kind of annotation type is note/txt, the knowledge graph matches Pryv heart streams to a SNOMED-CT code identified as: snomed-ct:364075005. Then, the system matches these rules to heart stream path heart/ and thus provide the final suggestions.
SemPryv also provides automated suggestions to the users based on a machine learning pipeline. When a user wants to annotate a stream, suggestions are provided by pressing the suggestions button. Two predictive models have been trained and they provide combined suggestions: 1. A user model which uses the already annotated streams by all users and 2. a synthetic model which has been trained on data from two mobile applications.
Finally, when one makes an API call to get the streams, he gets the structure of her annotated data where the annotations have also been saved.
"annotated_streams": [
{
"name": "Body Temperature",
"created": 1563181161.839,
"clientData": {
"sempryv:codes": {
"note/txt": [
{
"system_name": "SNOMEDCT",
"code": "722490005",
"display": "Temperature (property) (qualifier value)",
"system": "http://snomed.info/sct"
},
{
"system_name": "SNOMEDCT",
"code": "56342008",
"display": "Temperature taking (procedure)",
"system": "http://snomed.info/sct"
},
{
"system_name": "SNOMEDCT",
"code": "386725007",
"display": "Body temperature (observable entity)",
"system": "http://snomed.info/sct"
}
]
},
"pryv-browser:bgColor": "#3498db",
"sempryv:recursive": false
},
"modified": 1564756907.893,
"children": [
],
"modifiedBy": "cjxa7szlr00471id30j8dtxpd",
"createdBy": "cjx3edqz8001k1hd33kitvxb8",
"parentId": null,
"id": "mass"
},
{
"name": "Heart",
"created": 1564496540.827,
"clientData": {
"sempryv:codes": {
"note/txt": [
{
"system_name": "SNOMEDCT",
"code": "467178001",
"display": "Bedside heart rate monitor (physical object)",
"system": "http://snomed.info/sct"
},
{
"system_name": "SNOMEDCT",
"code": "364075005",
"display": "Heart rate (observable entity)",
"system": "http://snomed.info/sct"
}
]
},
"pryv-browser:bgColor": "#e81034",
"sempryv:recursive": true
},
"modified": 1564741665.221,
"children": [
{
"name": "Heart Rate",
"created": 1564497209.826,
"clientData": {
"sempryv:codes": {
"note/txt": [
{
"system_name": "SNOMEDCT",
"code": "233916004",
"display": "Heart block (disorder)",
"system": "http://snomed.info/sct"
}
]
},
"sempryv:recursive": false
},
"modified": 1564585383.596,
"children": [
],
"modifiedBy": "cjxa7szlr00471id30j8dtxpd",
"createdBy": "cjxa7szlr00471id30j8dtxpd",
"parentId": "heart",
"id": "heartRate"
}
],
"modifiedBy": "cjxa7szlr00471id30j8dtxpd",
"createdBy": "cjxa7szlr00471id30j8dtxpd",
"parentId": null,
"id": "heart"
}
]The rest of this documentation is assuming the following prerequisites:
-
A working and recent linux environment.
-
A
.envfile in thebackend/folder containing the following content:BIOPORTAL_API_KEY=...
The
BIOPORTAL_API_KEYcan be obtained by creating an account on the bioportal website. -
A
.envfile in thefrontend/folder containing the following content:VUE_APP_BACKEND=...
The
VUE_APP_BACKENDshould contain the root URL where the backend service can be accessed. For instance for development setup it can be set to:http://localhost:8000.
Warning: Building the image the frontend image requires at least 2GB RAM.
To run SemPryv locally in production mode, the following steps are required:
-
Install
docker(tested with version18.05.0-ce, buildf150324782) -
Ensure the
.envfiles exist as described in the requirements section. -
Build and deploy the containers with:
docker-compose up
If successful, the website should then be accessible at http://localhost.
For a production deployment on a server, the steps are similar, but don't forget
to modify the docker-compose.yml file to your need (ports, volumes, …).
For the development setup, two terminals will be needed, one for running the backend and one for running the frontend. The procedures are described below:
Backend
-
Go to the backend directory:
cd backend -
Create a virtual environment:
python -m venv venv
-
Activate the virtual environment:
. ./venv/bin/activate -
Install python dependencies for the development:
pip install -r dev-requirements.txt
-
Start the backend development server:
python -m sempryv
If successful, you should be able to access a page at http://localhost:8000
returning a 404 Not Found error.
Frontend
-
Go to the frontend directory:
cd frontend -
Install dependencies with
npm:npm install
-
Start the frontend development server:
npm run serve
If successful, you should be able to access the website at http://localhost:8080
Copyright (c) 2020 Pryv S.A. https://pryv.com & AIS Lab - HES VALAIS/WALLIS https://www.hevs.ch/en/minisites/projects-products/aislab/
This file is part of Open-Pryv.io and released under BSD-Clause-3 License
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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SPDX-License-Identifier: BSD-3-Clause