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</o:shapelayout></xml><![endif]--></head><body bgcolor=white lang=CS link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>Hi,<o:p></o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>Thank you, see inline.<o:p></o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>Jiri<o:p></o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'><o:p> </o:p></span></p><div style='border:none;border-left:solid blue 1.5pt;padding:0cm 0cm 0cm 4.0pt'><div><div style='border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0cm 0cm 0cm'><p class=MsoNormal><b><span style='color:windowtext'>From:</span></b><span style='color:windowtext'> François Bonnarel [mailto:francois.bonnarel@astro.unistra.fr] <br><b>Sent:</b> Thursday, July 13, 2017 11:52 PM<br><b>To:</b> Jiří Nádvorník <nadvornik.ji@gmail.com>; mireille.louys@unistra.fr; dm@ivoa.net; voevent@ivoa.net; dal@ivoa.net<br><b>Subject:</b> Re: [timeDomain: model for Time series] discussion on Timeseries Data model Note / how are ND-Cube DM and Timeseries DM connected ?<o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><p><o:p> </o:p></p><p class=MsoNormal>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.<o:p></o:p></p><div><p class=MsoNormal>Le 13/07/2017 à 15:02, Jiří Nádvorník a écrit :<o:p></o:p></p></div><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Hi Mireille,<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Thank you very much for the input. <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Your diagram is almost correct, but I believe that the relationship <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>TimeSeriesCube <is a > NDCubeDM::SparseCubeDataset<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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:<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>TimeSeriesCube <is a> NDCubeDM::SparseCube and<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>NDCubeDM::SparseCube <is collected by> NDCubeDM::SparseCubeDataset<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>As seen on the following image:<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><img width=299 height=360 style='width:3.1145in;height:3.75in' id="Obrázek_x0020_1" src="cid:image001.jpg@01D2FCA5.753819E0"><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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. 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.<o:p></o:p></p></div></blockquote><p class=MsoNormal>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 "<i>dataproduct_type</i> " of your TimeSeriesCube (or TimeSeriesDataSet) can only be inherited from ObsDataSet through SparseCube Dataset and not from DataProduct via SparseCube.<span style='color:windowtext'><o:p></o:p></span></p><p class=MsoNormal><b><i><span style='color:windowtext'>[[Jiri Nadvornik]] Ha, so this means that each row we see in ObsCore table is describing a DataSet, meaning Spectrum <is a> DataSet, Image <is a> DataSet, SED <is a> DataSet, event <is a> Dataset, etc.? I can also see a valid option dataproduct_type=cube in the ObsCore document (<a href="http://www.ivoa.net/documents/ObsCore/20111028/REC-ObsCore-v1.0-20111028.pdf"><span style='font-weight:normal;font-style:normal'>http://www.ivoa.net/documents/ObsCore/20111028/REC-ObsCore-v1.0-20111028.pdf</span></a> )<o:p></o:p></span></i></b></p><p class=MsoNormal><b><i><span style='color:windowtext'><o:p> </o:p></span></i></b></p><p class=MsoNormal><b><i><span style='color:windowtext'>If the assumptions above are correct, I concur that changing the relationship to TimeSeries <is a> Cube <is a> DataSet is not a bad idea. >From my point of view, this can be changed in the model rather easily – it will just introduce a more tight coupling between DataSet and TimeSeriesCube data model. TimeSeriesCube data model will need to import everything from the DataSet DM and if something changes in DataSet DM, it will need to change in TimeSeriesCube DM too. In other words, new major versions of DataSet DM will require new major versions of TimeSeriesCube DM to follow.<o:p></o:p></span></i></b></p><p class=MsoNormal><br><br><br>Cheers<br>François<br><br> <br><br><br><o:p></o:p></p><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'>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 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). <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>The Quantity class indeed provides the *<b>richer description*</b> 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.<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>I completely agree with the rest – we can discover TimeSeries data cubes by <i>dataproduct_type</i> and <i>target_name, s_region, s_resol, t_min, t_max, t_resol, em_min, em_max, em_resol, etc. </i>Attributes right now.<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>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.<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Cheers,<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Jiri<o:p></o:p></p><div><div><div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b>From:</b> <a href="mailto:dm-bounces@ivoa.net">dm-bounces@ivoa.net</a> [<a 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 href="mailto:dm@ivoa.net">dm@ivoa.net</a>; <a href="mailto:voevent@ivoa.net">voevent@ivoa.net</a>; <a 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 ?<o:p></o:p></p></div></div><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'> <o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>Dear DM and Time Domain followers, <br><br>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 : <o:p></o:p></p><ul type=disc><li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:0cm;mso-list:l0 level1 lfo1'><i>DataSetMetadata DM</i>, which has the main ObsDataset Class ,<o:p></o:p></li><li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:0cm;mso-list:l0 level1 lfo1'><i>ND-CubeDM</i>, which defines a SparseCubedataset<o:p></o:p></li><li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:0cm;mso-list:l0 level1 lfo1'><i>TimeSerieCubeDM</i>, which highlights the special properties of a Cube depending on a Time axis<o:p></o:p></li></ul><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'>I think this is essential to highlight the inheritance path between these 3 DM building blocks: <br>a TimeSeriesCube <is a > NDCubeDM::SparseCubeDataset<br>a NDCubeDM::SparseCubeDataset <is a > DatasetMetadaDM::ObsDataset<br><br>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<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:12.0pt'><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><o:p></o:p></p><pre>-- <o:p></o:p></pre><pre>--<o:p></o:p></pre><pre>Mireille Louys<o:p></o:p></pre><pre>CDS Laboratoire Icube <o:p></o:p></pre><pre>Observatoire de Strasbourg Telecom Physique Strasbourg<o:p></o:p></pre><pre>11 rue de l'Université 300, Bd Sebastien Brandt CS 10413 <o:p></o:p></pre><pre>F- 67000-STRASBOURG F-67412 ILLKIRCH Cedex<o:p></o:p></pre><pre>tel: +33 3 68 85 24 34<o:p></o:p></pre></div></div></blockquote><p class=MsoNormal><o:p> </o:p></p></div></div></body></html>