Update swj2022 folder for revision of Semantic Web Journal paper

swj2022/README.md

@@ -1,6 +1,6 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains all supportive material related to the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains all supportive material related to the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 
@@ -12,6 +12,9 @@ This folder contains three subfolders:
 * [`evaluations`](evaluations): This folder contains supportive material related to the evaluations performed in the paper.
 * [`eye-implementation`](eye-implementation): This folder contains some more details concerning the implementation of the initialization and query derivation of DIVIDE with the EYE reasoner.
 
+Moreover, this folder contains the different versions of the paper that have been submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+* [paper_v1_submitted_2022-05-01.pdf](paper_v1_submitted_2022-05-01.pdf): This PDF represents the original version of the paper, that was submitted on 1 May 2022. It contains additional details about the DIVIDE methodology and the use case scenario, that have been removed in the first revision of the paper.
+
 ## Contact
 
 The main contact person directly involved with this research is [Mathias De Brouwer](https://www.linkedin.com/in/mathiasdebrouwer/). In case of any remarks or questions, you can email [[email protected]](mailto:[email protected]) or [create a GitHub issue](../../../issues/new). 

swj2022/evaluations/README.md

@@ -1,6 +1,6 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains supportive material for the evaluations in the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains supportive material for the evaluations in the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 
@@ -9,7 +9,11 @@ The folder contains supportive material for the following evaluations:
 * [`divide-performance`](divide-performance): The material in this folder is related to the performance evaluation of DIVIDE. It corresponds to the evaluation set-up and results in the Sections 8.1 and 9.1 of the paper ("Performance evaluation of DIVIDE").
 * [`real-time-comparison`](real-time-comparison): The material in this folder is related to the real-time evaluation of the DIVIDE approach, compared to other alternative approaches that use real-time semantic reasoning. It corresponds to the evaluation set-up and results in the Sections 8.3 and 9.2-3 of the paper ("Real-time evaluation of derived DIVIDE queries").
 
-The context used for the evaluations is represented by the [`context.ttl`](context.ttl) file. This file contains all context triples in RDF/Turtle syntax. 
+The context used for the evaluations is represented by the [`context.ttl`](context.ttl) file. This file contains all context triples in RDF/Turtle syntax.
+
+The [`divide-server-1.0-jar-with-dependencies.jar`](divide-server-1.0-jar-with-dependencies.jar) file represents the compiled Java JAR of the DIVIDE server module used for the evaluations in the paper. The corresponding source code can be found in the [`src/divide-central`](../../src/divide-central) folder of this repository. The version of the source code to build the given Java JAR (and thus the version used for the evaluations) is tagged with the 'swj-2022' tag (see [tag page](../../../../tags)).
+
+The realistic dataset, collected in the imec-UGent HomeLab and used in the paper to extract the activity rules for this evaluation and to create the simulation dataset, is publicly available on the DAHCC ontology website via [this link](https://dahcc.idlab.ugent.be/dataset.html).
 
 ## Contact
 

swj2022/evaluations/divide-performance/README.md

@@ -1,13 +1,15 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains supportive material for the evaluations in the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains supportive material for the evaluations in the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 
 The material in this folder is related to the performance evaluation of the DIVIDE system itself. It corresponds to the evaluation set-up and results in the Sections 8.1 and 9.1 of the paper ("Performance evaluation of DIVIDE").
 
 The [`divide-queries`](divide-queries) subfolder contains the configuration details of the DIVIDE query definitions that are being used in this evaluation. These include the DIVIDE queries corresponding to the toileting and brushing teeth activity rules. For both activities, the root folder of the corresponding DIVIDE query contains the internal representation of this DIVIDE query. For the toileting query, the end-user definition (as a series of SPARQL queries) is also included in the `sparql` subfolder.
 
+Note that the DIVIDE query for the showering activity (used in the real-time comparison evaluation) is the same DIVIDE query as the one for the toileting query (as explained in the paper).
+
 ## Contact
 
 The main contact person directly involved with this research is [Mathias De Brouwer](https://www.linkedin.com/in/mathiasdebrouwer/). In case of any remarks or questions, you can email [[email protected]](mailto:[email protected]) or [create a GitHub issue](../../../../../issues/new).

swj2022/evaluations/real-time-comparison/README.md

@@ -1,6 +1,6 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains supportive material for the evaluations in the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains supportive material for the evaluations in the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 

swj2022/eye-implementation/README.md

@@ -1,6 +1,6 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains DIVIDE implementation details related to the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains DIVIDE implementation details related to the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 

swj2022/ontology/README.md

@@ -1,15 +1,15 @@
-# Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE
+# Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
 
-This folder contains the ontology data related to the paper "Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
+This folder contains the ontology data related to the paper "Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design", which is submitted to the Special Issue on Semantic Web Meets Health Data Management of the Semantic Web Journal.
 
 ## Contents
 
 In the paper, DIVIDE is explained through a running homecare monitoring example. This folder contains all files of the Activity Recognition ontology that is being used for this example.
 
 The Activity Recognition ontology contains two parts:
 
-- A snapshot of the [DAHCC ontology](https://github.com/predict-idlab/DAHCC-Sources). This ontology contains definitions to perform Data Analytics in Health and Connected Care. The files used for this paper are:
-  - The general ontology files included in the `Ontology` folder of the DAHCC GitHub repo. Note that this folder contains the RDF/Turtle representation of these ontology files.
+- A snapshot of the [DAHCC ontology](https://github.com/predict-idlab/DAHCC-Sources). This ontology contains definitions to perform Data Analytics in Health and Connected Care. More information about the DAHCC ontology is available via [this website](https://dahcc.idlab.ugent.be) The files used for this paper are:
+  - The general ontology files included in the `Ontology` folder of the DAHCC GitHub repo. Note that the current repository contains the RDF/Turtle representation of these ontology files.
   - The TBox definitions extracted from the `_Homelab.owl` and `_HomelabWearable.owl` files in the `instantiated_examples` folder of the DAHCC GitHub repo (in RDF/Turtle format).
   - All imports of the `imports` folder of the DAHCC GitHub repo that are being (indirectly) imported by any of the other included DAHCC files.
 - The additional ontology file [`KBActivityRecognition.ttl`](KBActivityRecognition.ttl) that represents all extra definitions related to the knowledge-driven activity recognition.