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</o:shapelayout></xml><![endif]--></head><body bgcolor=white lang=CS link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>Hi Mireille,<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 very much for the input. <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'>Your diagram is almost correct, but I believe that the relationship <o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Times New Roman",serif'>TimeSeriesCube <is a > NDCubeDM::SparseCubeDataset</span><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'>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></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>TimeSeriesCube <is a> NDCubeDM::SparseCube and<o:p></o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>NDCubeDM::SparseCube <is collected by> NDCubeDM::SparseCubeDataset<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'>As seen on the following image:<o:p></o:p></span></p><p class=MsoNormal><img width=299 height=360 style='width:3.1145in;height:3.75in' id="Obrázek_x0020_1" src="cid:image002.jpg@01D2FBE9.0E0E84F0"><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'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>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.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></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 style='font-family:"Times New Roman",serif'>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><o:p></o:p></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>The Quantity class indeed provides the *</span><b><span style='font-family:"Times New Roman",serif'>richer description</span></b><b><span style='font-family:"Times New Roman",serif'>*</span></b><span style='font-family:"Times New Roman",serif'> 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></span></p><p class=MsoNormal><span style='font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:windowtext;mso-fareast-language:EN-US'>I completely agree with the rest – we can discover TimeSeries data cubes by </span><i><span style='font-family:"Times New Roman",serif'>dataproduct_type</span></i><span style='font-family:"Times New Roman",serif'> and <i>target_name, s_region, s_resol, t_min, t_max, t_resol, em_min, em_max, em_resol, etc. </i></span><span style='color:windowtext;mso-fareast-language:EN-US'>Attributes right now.<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'>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></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'>Cheers,<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'> dm-bounces@ivoa.net [mailto:dm-bounces@ivoa.net] <b>On Behalf Of </b>Mireille Louys<br><b>Sent:</b> Wednesday, July 12, 2017 10:57 AM<br><b>To:</b> dm@ivoa.net; voevent@ivoa.net; dal@ivoa.net<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></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><span style='font-family:"Times New Roman",serif'>Dear DM and Time Domain followers,</span> <br><br><span style='font-family:"Times New Roman",serif'>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><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><span style='font-family:"Times New Roman",serif'>DataSetMetadata DM</span></i><span style='font-family:"Times New Roman",serif'>, which has the main ObsDataset Class ,</span><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><span style='font-family:"Times New Roman",serif'>ND-CubeDM</span></i><span style='font-family:"Times New Roman",serif'>, which defines a SparseCubedataset</span><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><span style='font-family:"Times New Roman",serif'>TimeSerieCubeDM</span></i><span style='font-family:"Times New Roman",serif'>, which highlights the special properties of a Cube depending on a Time axis</span><o:p></o:p></li></ul><p class=MsoNormal><span style='font-family:"Times New Roman",serif'>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 style='font-family:"Times New Roman",serif'>NDCubeDM::SparseCubeDataset <is a > DatasetMetadaDM::ObsDataset<br></span><br><span style='font-family:"Times New Roman",serif'>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 style='font-family:"Times New Roman",serif;color:windowtext'><o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Times New Roman",serif'><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><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<span style='color:windowtext'><o:p></o:p></span></pre></div></div></body></html>