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Hi Jiri,<br>
Thanks for having given a look to these emails. This is a very
important discussion and we have to all agree on these grounds
before going to more TimeSeries specific features of the model we
need.<br>
<div class="moz-cite-prefix">Le 13/07/2017 à 15:02, Jiří Nádvorník a
écrit :<br>
</div>
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type="cite">
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<p class="MsoNormal"><span>Hi Mireille,</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Thank you very much for the input. </span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Your diagram is almost correct, but I
believe that the relationship </span></p>
<p class="MsoNormal"><span>TimeSeriesCube <is a >
NDCubeDM::SparseCubeDataset</span><span></span></p>
<p class="MsoNormal"><span>Is not correct, even in the original
idea Mark Cresitello Ditmar had (please correct me here if
I’m wrong, Mark). The correct relationship is:</span></p>
<p class="MsoNormal"><span>TimeSeriesCube <is a>
NDCubeDM::SparseCube and</span></p>
<p class="MsoNormal"><span>NDCubeDM::SparseCube <is collected
by> NDCubeDM::SparseCubeDataset</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>As seen on the following image:</span></p>
<p class="MsoNormal"><img id="Obrázek_x0020_1"
src="cid:part1.D52C05FF.5FF89FF7@astro.unistra.fr"
height="360" width="299"><span></span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Meaning that the SparseCubeDataset is
describing a collection of data cubes, e.g., time series
data, e.g., light curves, *<b>not</b>* one cube, e.g., one
time series, e.g., one light curve.</span> If we agree that
we don’t need collections of time series (because they can by
themselves be multi-dimensional), we can change it to <is
a> relationship as you propose.</p>
</div>
</blockquote>
OK. This is were we differ. I don't think SparSecubedataset is made
for tackling collections. My interpretation of the ND-Cube diagram
helped by MCD' text is that SparSeCubeDataset inheritance from
ObsDataSet contains all generic metadata for a dataset which may be
COMPLEX. That is it contains : curation, provenance, identification,
characterisation.<br>
<br>
Such a SparseCubeDataset may contain one (simple dataset) or
several (complex dataset) "SparseCubes"<br>
<br>
Each of these SparseCube(s) contains ND-points where the actual data
are stored and inherits from the DataProduct class the coordinate
systems and mappings of the data to physical coordinates.<br>
<br>
As Mireille said the "<span><i>dataproduct_type</i> " of your
TimeSeriesCube (or TimeSeriesDataSet) can only be inherited from
ObsDataSet through SparseCube Dataset and not from DataProduct via
SparseCube.<br>
<br>
<br>
Cheers<br>
François<br>
</span><br>
<br>
<br>
<blockquote cite="mid:002b01d2fbd8$4b3f8f10$e1bead30$@gmail.com"
type="cite">
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<p class="MsoNormal"> </p>
<p class="MsoNormal">Now the Time series class is described in
the attached UML.pdf (please note that this one is different
from the original note, this version was last updated after
Shanghai Interop in May). Main difference is that the <span>TimeSerieCubeDM::CubeAxis
custom class was replaced just by a generic columnRef
(yellow) saying where can I find the data for this axis and
that axis is described by Quantity class (yellow). <br>
<br>
</span></p>
<p class="MsoNormal"><span>The Quantity class indeed provides
the *</span><b><span>richer description</span></b><b><span>*</span></b><span>
on the cube axis (not only the time axis). This is indeed
correlated by STC2.0::CoordMeasurement, but we got into
conflict in here, as we would like to use it not for
describing only *<b>uncertainties</b>* in the Measurement,
but for statistical distribution in the whole axis, that’s
why we are trying to create an abstraction above both
CharacterisationDM::ObservableAxis and
STC2.0::CoordMeasurement describing only the statistical
properties of both. The Quantity class is just a sketch what
could be described by it – the final solution would be to
store a mixture of gaussians in it, describing the
distribution in a generic way.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>I completely agree with the rest – we
can discover TimeSeries data cubes by </span><i><span>dataproduct_type</span></i><span>
and <i>target_name, s_region, s_resol, t_min, t_max,
t_resol, em_min, em_max, em_resol, etc. </i></span><span>Attributes
right now.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>How to extend these Obscore discovery
parameters to discover time series by more details of their
axes, we need to agree on how the distribution of values on
them will be described in the time series. From the data
point of view, a *<b>mixture of gaussian</b>* based
abstraction above measurement uncertainties and axis
statistical distributions would be perfect, but I don’t know
whether we can provide that description for any type of time
series axis.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Cheers,</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Jiri</span></p>
<p class="MsoNormal"><span> </span></p>
<div>
<div>
<div>
<p class="MsoNormal"><b><span>From:</span></b><span>
<a class="moz-txt-link-abbreviated" href="mailto:dm-bounces@ivoa.net">dm-bounces@ivoa.net</a> [<a class="moz-txt-link-freetext" href="mailto:dm-bounces@ivoa.net">mailto:dm-bounces@ivoa.net</a>] <b>On
Behalf Of </b>Mireille Louys<br>
<b>Sent:</b> Wednesday, July 12, 2017 10:57 AM<br>
<b>To:</b> <a class="moz-txt-link-abbreviated" href="mailto:dm@ivoa.net">dm@ivoa.net</a>; <a class="moz-txt-link-abbreviated" href="mailto:voevent@ivoa.net">voevent@ivoa.net</a>; <a class="moz-txt-link-abbreviated" href="mailto:dal@ivoa.net">dal@ivoa.net</a><br>
<b>Subject:</b> [timeDomain: model for Time series]
discussion on Timeseries Data model Note / how are
ND-Cube DM and Timeseries DM connected ?</span></p>
</div>
</div>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><span>Dear DM and Time Domain followers,</span>
<br>
<br>
<span>I am trying, together with my CDS colleagues, to
recap on the various DMs available in the IVOA and
understand the possible links between the future Time
Series Model ( as sketched in Jiris's Note) and existing
DMs like ND-Cube and STC 2.<br>
<br>
Here is a graph proposed by Laurent Michel to clarify the
links in 3 main parts : </span></p>
<ul type="disc">
<li class="MsoNormal"><i><span>DataSetMetadata DM</span></i><span>,
which has the main ObsDataset Class ,</span></li>
<li class="MsoNormal"><i><span>ND-CubeDM</span></i><span>,
which defines a SparseCubedataset</span></li>
<li class="MsoNormal"><i><span>TimeSerieCubeDM</span></i><span>,
which highlights the special properties of a Cube
depending on a Time axis</span></li>
</ul>
<p class="MsoNormal"><span>I think this is essential to
highlight the inheritance path between these 3 DM building
blocks: <br>
a TimeSeriesCube <is a >
NDCubeDM::SparseCubeDataset<br>
</span>a <span>NDCubeDM::SparseCubeDataset <is a >
DatasetMetadaDM::ObsDataset<br>
</span><br>
<span>ObsDataset has a <i>dataproduct_type</i> attribute
which allows to discover all dataproducts of type '
timeseries'. <br>
this provides the container object for time-dependent
data.<br>
<br>
If we need to select <i>timeseries dataproducts</i>
according to some properties extracted from their data we
can:<br>
- reuse what Obscore DM provides to explain general axes
properties<br>
target_name, s_region, s_resol, t_min, t_max, t_resol,
em_min, em_max, em_resol, etc. are the basic properties
for discovery<br>
<br>
- provide a richer description of the TimeAxis and
ObservableAxis. <br>
For that , extracting a statistical profile from the data
contained in the Cube could do the job. <br>
this means to access and analyse the Data part in ND-Cube
, i. e the ND-Points gathered in a SparseCube Object</span><span></span></p>
<p class="MsoNormal"><span><br>
<br>
I guess more properties can be exposed to qualify the axes
present in the Timeseries dataset , but for the moment , I
see some overlap of notions between <br>
CharacterisationDM::ObservableAxis,
STC2.0::CoordMeasurement (??) and
TimeSerieCubeDM::CubeAxis.<br>
<br>
This would be great if we could sort this out, <br>
but currently , I would appreciate your feedback on the
attached diagram , in order to proceed on the data model
structure. <br>
<br>
Cheers, Mireille ( after discussions together with
Laurent, François, Ada) <br>
<br>
<br>
</span></p>
<pre>-- </pre>
<pre>--</pre>
<pre>Mireille Louys</pre>
<pre>CDS Laboratoire Icube </pre>
<pre>Observatoire de Strasbourg Telecom Physique Strasbourg</pre>
<pre>11 rue de l'Université 300, Bd Sebastien Brandt CS 10413 </pre>
<pre>F- 67000-STRASBOURG F-67412 ILLKIRCH Cedex</pre>
<pre>tel: +33 3 68 85 24 34<span></span></pre>
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