From 0d9dfc3a9a7aa58d952c9c135abdeabd56864edf Mon Sep 17 00:00:00 2001
From: Simon Cox
Date: Mon, 11 Jun 2018 22:43:03 +1000
Subject: [PATCH] COmments on OBOE and QB
---
proposals/ssn-extensions/index.html | 54 ++++++++++++++++++++---------
1 file changed, 38 insertions(+), 16 deletions(-)
diff --git a/proposals/ssn-extensions/index.html b/proposals/ssn-extensions/index.html
index 1551d0bb..72f73408 100644
--- a/proposals/ssn-extensions/index.html
+++ b/proposals/ssn-extensions/index.html
@@ -199,12 +199,12 @@
Ultimate feature of interest
The relationship of the ultimate feature of interest to observations and acts of sampling is illustrated in the figures below.
-
+ Patterns for observations that relate to the ultimate feature of interest directly (top), or indirectly via one sample (middle) or a chain of samples (bottom)
-
+ Patterns for sampling that relate to the ultimate feature of interest directly (top), or indirectly via one intermediate sample (middle) or a chain of intermediate samples (bottom)
@@ -220,16 +220,32 @@
Homogeneous collection of observations
A collection of observations may share a common value for one or more of the characteristic observation properties.
- For example, an oboe:Observation is a collection of oboe:Measurements concerning a common oboe:Entity, with each oboe:Measurement reporting the value of a different oboe:Characteristic.
- A alignment module in SSN [[vocab-ssn]] provided an alignment for the atomic oboe:Measurement to sosa:Observation, but not for oboe:Observation.
- Another example is that many monitoring processes generate a stream of data in which only the (phenomenon-) time changes between consecutive results.
- Another case is where a sensor network generates a set of results at the same time for a suite of features-of-interest.
+ For example:
+
+
+
+ an oboe:Observation is a collection of oboe:Measurements concerning a common oboe:Entity, with each oboe:Measurement reporting the value of a different oboe:Characteristic.
+ An alignment module in SSN [[vocab-ssn]] provided an alignment for the atomic oboe:Measurement to sosa:Observation, but not for oboe:Observation.
+
+
+ many monitoring processes generate a stream of data in which only the (phenomenon-) time changes between consecutive results for the same property on a single feature of interest.
+
+
+ a sensor network typically generates a set of results, at the same time, for the same property(s) of a suite of features-of-interest.
+
+
+
+
+ Model for an observation-collection, in which the collection may carry one or more of the properties of its members if they have a shared value for all members
+
+
Collections may be nested.
For example, an outer observation-collection may share an observed-property, procedure and sensor, and contain inner observation-collections at different phenomenon-times, each containing a set of observations on different features-of-interest.
@@ -239,16 +255,19 @@
Homogeneous collection of observations
A formal alignment of SSN and this extension to the W3C Datacube vocabulary [[vocab-data-cube]] is provided below.
-
-
-
- Model for an observation-collection, in which the collection may carry one or more of the properties of its members if they have a shared value for all members
-
-
Note that this requirement was addressed in OM-Lite [[OM-Lite]] following the pattern described here.
+
+
+
+ The OBOE core model (left), shown alongside the SSN-ext model (right) with comparable classes and properties aligned.
+
+
+
+
+
@@ -502,7 +521,7 @@
Inheriting observation properties from a collec
The rules may be defined formally as SPARQL query expressions [[!sparql11-query]] as follows:
- Update rules to chase up a chain of observation collections.
+ Update rules to chase up a chain of observation collections.
@@ -648,9 +667,12 @@
Examples
Alignments
-
W3C Data Cube vocabulary
+
RDF Data Cube vocabulary
- TBD
+ The W3C RDF Data Cube vocabulary [[vocab-data-cube]] provides a means to describe multi-dimensional data according to a 'data cube' model inspired by SDMX. The RDF Data Cube vocabulary uses the term observation for the results, and the dimensions of a data cube indicate the features of interest and phenomenon times of the results, the measures correspond to the observed properties, and the attributes accommodate information about the procedures and sensors.
+
+
+ A slice selects a subset of the 'observations' by fixing a subset of the dimensions, and thus delivers the set of results of observation-collections with fixed features-of-interest or phenomenon-times or both.