N-Dimensional Cube model version 1.0GoalThe Cube Data Model, currently under development, will describe multdimensional image and cube dataset instances. Much of the initial work for the cube model was done under the title of ImageDM. Please see the <a target="_self" href="ImageDM" title="ImageDM twiki">ImageDM twiki</a> for information on that effort. Since Nov. 2013, the work proceeded under this title. The primary difference being to express the Cube model as an extension of a more generic observation dataset. As such, this model is dependent on supporting component models DatasetMetadata, and STC2.Participantsdomain experts: DougTody (retired) data modelers: MarkCresitelloDittmar contributors: MarkCresitelloDittmar (editor); FrancoisBonnarel, JoseEnriqueRuiz, OmarLaurino, MireilleLouys, ArnoldRots, JesusSalgado, DougTodyUse CasesThe Cube model includes the following use cases: 1) Simple 2-D pixelated image. (the most common astronomical image type) 2) A single image cube which is sparse in either the spectral, spatial, or temporal plane. eg: JCMT spectral data cubes are sparse in the spatial plane. 3) Multiple sub-array cubes are often produced by instruments having multiple detectors. These cubes contain multiple cube segments which combine to form a complete, potentially sparse, cube. eg: spectral data cube with multiple spectral bands. eg: CCD mosaic where detectors may not be aligned (requiring different WCS) 4) Very large cubes. Cube data may be dispersed among multiple storage containers. The user-facing instance must hide this underlying complexity, providing the appearance of a single cube. 5) Interactive cube visualization and analysis. This application use case involves interpreting the cube instance, visualizing the content (possibly at various resolutions), interactively performing various operations on the cube ( slice, dice, 2-D projections, spectral extraction, etc )Cube Classifications:The above use cases outline the following classifications of cube data which are to be covered by this model: 1) Simple image: A single filled or mostly filled N-Dimensional image array with associated metadta describing the dataset, its WCS coordinate systems, etc. 2) Simple sparse image: A single N-Dimensional array as in #1, however, large portions of the image may be sparse (have no data samples) 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. 4) Large cube: Very large cubes may be represented as either a simple image or multiple subarray type. The cube model does not impose size limitations, so only important factor here, is that the cube description must be independent of the physical storage structure. 5) Wide-field survey: This is essentially the same as #3, except no subarrays (no explicitly pixelated data). Only automated virtual data generation techniques may be used to access the data, with sub-regions being computed on-the-fly and returned to a client. The mechanism for generating the cube is not relevant to its description, so this class is covered by #1 or #3. 6) Sparse data cube: Sparse data are commonly used for higher-dimensional cubes, and are typically sparse along one or more axes. eg; a multi-band image with only a few spectral coordinates (bands). Event data is an example of a data cube which is sparse in all measurement axes.Requirements
DocumentsLatest document: The latest released document can be found on the IVOA Documents and Standards page. Volute: The volute repository holds all revisions of the document, as well as the source open office document, diagrams, etc. here UML Model: The model is being developed in UML, using Modelio-3.0. The model project can be obtained as a zip file in the volute repository, here. We also provide an export of the UML specification in XMI format (version 2.4.1), which is compatible with the vo-dml xslt scripts for generating the vo-dml XML representation. vo-dml: VO-DML XML serialization of the model and corresponding HTML page are hereDiscussionsMuch of the initial work for the cube model was done under the title of ImageDM. These discussion threads are recorded in the ImageDM twiki. Since 2014, the Cube model work has proceded under this title. Relevant discussion threads are recorded below: | ||||||||
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ImplementationsInstances of Cube require the use of several contributing models. The list below provides pointers to the relevant model documentation for the models used in the example serialization set. These files will be transferred to the ivoa repository as the models become REC.
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N-Dimensional Cube model version 1.0GoalThe Cube Data Model, currently under development, will describe multdimensional image and cube dataset instances. Much of the initial work for the cube model was done under the title of ImageDM. Please see the <a target="_self" href="ImageDM" title="ImageDM twiki">ImageDM twiki</a> for information on that effort. Since Nov. 2013, the work proceeded under this title. The primary difference being to express the Cube model as an extension of a more generic observation dataset. As such, this model is dependent on supporting component models DatasetMetadata, and STC2.Participantsdomain experts: DougTody (retired) data modelers: MarkCresitelloDittmar contributors: MarkCresitelloDittmar (editor); FrancoisBonnarel, JoseEnriqueRuiz, OmarLaurino, MireilleLouys, ArnoldRots, JesusSalgado, DougTodyUse CasesThe Cube model includes the following use cases: 1) Simple 2-D pixelated image. (the most common astronomical image type) 2) A single image cube which is sparse in either the spectral, spatial, or temporal plane. eg: JCMT spectral data cubes are sparse in the spatial plane. 3) Multiple sub-array cubes are often produced by instruments having multiple detectors. These cubes contain multiple cube segments which combine to form a complete, potentially sparse, cube. eg: spectral data cube with multiple spectral bands. eg: CCD mosaic where detectors may not be aligned (requiring different WCS) 4) Very large cubes. Cube data may be dispersed among multiple storage containers. The user-facing instance must hide this underlying complexity, providing the appearance of a single cube. 5) Interactive cube visualization and analysis. This application use case involves interpreting the cube instance, visualizing the content (possibly at various resolutions), interactively performing various operations on the cube ( slice, dice, 2-D projections, spectral extraction, etc )Cube Classifications:The above use cases outline the following classifications of cube data which are to be covered by this model: 1) Simple image: A single filled or mostly filled N-Dimensional image array with associated metadta describing the dataset, its WCS coordinate systems, etc. 2) Simple sparse image: A single N-Dimensional array as in #1, however, large portions of the image may be sparse (have no data samples) 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. 4) Large cube: Very large cubes may be represented as either a simple image or multiple subarray type. The cube model does not impose size limitations, so only important factor here, is that the cube description must be independent of the physical storage structure. 5) Wide-field survey: This is essentially the same as #3, except no subarrays (no explicitly pixelated data). Only automated virtual data generation techniques may be used to access the data, with sub-regions being computed on-the-fly and returned to a client. The mechanism for generating the cube is not relevant to its description, so this class is covered by #1 or #3. 6) Sparse data cube: Sparse data are commonly used for higher-dimensional cubes, and are typically sparse along one or more axes. eg; a multi-band image with only a few spectral coordinates (bands). Event data is an example of a data cube which is sparse in all measurement axes.Requirements
DocumentsLatest document: The latest released document can be found on the IVOA Documents and Standards page. Volute: The volute repository holds all revisions of the document, as well as the source open office document, diagrams, etc. here UML Model: The model is being developed in UML, using Modelio-3.0. The model project can be obtained as a zip file in the volute repository, here. We also provide an export of the UML specification in XMI format (version 2.4.1), which is compatible with the vo-dml xslt scripts for generating the vo-dml XML representation. vo-dml: VO-DML XML serialization of the model and corresponding HTML page are hereDiscussionsMuch of the initial work for the cube model was done under the title of ImageDM. These discussion threads are recorded in the ImageDM twiki. Since 2014, the Cube model work has proceded under this title. Relevant discussion threads are recorded below: | ||||||||
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N-Dimensional Cube model version 1.0GoalThe Cube Data Model, currently under development, will describe multdimensional image and cube dataset instances. Much of the initial work for the cube model was done under the title of ImageDM. Please see the <a target="_self" href="ImageDM" title="ImageDM twiki">ImageDM twiki</a> for information on that effort. Since Nov. 2013, the work proceeded under this title. The primary difference being to express the Cube model as an extension of a more generic observation dataset. As such, this model is dependent on supporting component models DatasetMetadata, and STC2.Participantsdomain experts: DougTody (retired) data modelers: MarkCresitelloDittmar contributors: MarkCresitelloDittmar (editor); FrancoisBonnarel, JoseEnriqueRuiz, OmarLaurino, MireilleLouys, ArnoldRots, JesusSalgado, DougTodyUse CasesThe Cube model includes the following use cases: 1) Simple 2-D pixelated image. (the most common astronomical image type) 2) A single image cube which is sparse in either the spectral, spatial, or temporal plane. eg: JCMT spectral data cubes are sparse in the spatial plane. 3) Multiple sub-array cubes are often produced by instruments having multiple detectors. These cubes contain multiple cube segments which combine to form a complete, potentially sparse, cube. eg: spectral data cube with multiple spectral bands. eg: CCD mosaic where detectors may not be aligned (requiring different WCS) 4) Very large cubes. Cube data may be dispersed among multiple storage containers. The user-facing instance must hide this underlying complexity, providing the appearance of a single cube. 5) Interactive cube visualization and analysis. This application use case involves interpreting the cube instance, visualizing the content (possibly at various resolutions), interactively performing various operations on the cube ( slice, dice, 2-D projections, spectral extraction, etc )Cube Classifications:The above use cases outline the following classifications of cube data which are to be covered by this model: 1) Simple image: A single filled or mostly filled N-Dimensional image array with associated metadta describing the dataset, its WCS coordinate systems, etc. 2) Simple sparse image: A single N-Dimensional array as in #1, however, large portions of the image may be sparse (have no data samples) 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. 4) Large cube: Very large cubes may be represented as either a simple image or multiple subarray type. The cube model does not impose size limitations, so only important factor here, is that the cube description must be independent of the physical storage structure. 5) Wide-field survey: This is essentially the same as #3, except no subarrays (no explicitly pixelated data). Only automated virtual data generation techniques may be used to access the data, with sub-regions being computed on-the-fly and returned to a client. The mechanism for generating the cube is not relevant to its description, so this class is covered by #1 or #3. 6) Sparse data cube: Sparse data are commonly used for higher-dimensional cubes, and are typically sparse along one or more axes. eg; a multi-band image with only a few spectral coordinates (bands). Event data is an example of a data cube which is sparse in all measurement axes.Requirements
DocumentsLatest document: The latest released document can be found on the IVOA Documents and Standards page. Volute: | ||||||||
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We also provide an export of the UML specification in XMI format (version 2.4.1), which is compatible with the vo-dml xslt scripts for generating the vo-dml XML representation. vo-dml: | ||||||||
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DiscussionsMuch of the initial work for the cube model was done under the title of ImageDM. These discussion threads are recorded in the ImageDM twiki. Since 2014, the Cube model work has proceded under this title. Relevant discussion threads are recorded below:ImplementationsInstances of Cube require the use of several contributing models. The list below provides pointers to the relevant model documentation for the models used in the example serialization set. These files will be transferred to the ivoa repository as the models become REC.
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Example Files: We have generated a series of example serialization files for this model work. Four are real-world data files, and others which are focused on certain model elements or components not represented by the real-world files. We provide the link to the files here.. there you can find a 00README file which describes the content. | ||||||||
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N-Dimensional Cube model version 1.0GoalThe Cube Data Model, currently under development, will describe multdimensional image and cube dataset instances. Much of the initial work for the cube model was done under the title of ImageDM. Please see the <a target="_self" href="ImageDM" title="ImageDM twiki">ImageDM twiki</a> for information on that effort. Since Nov. 2013, the work proceeded under this title. The primary difference being to express the Cube model as an extension of a more generic observation dataset. As such, this model is dependent on supporting component models DatasetMetadata, and STC2.Participants | ||||||||
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> > | contributors: MarkCresitelloDittmar (editor); FrancoisBonnarel, JoseEnriqueRuiz, OmarLaurino, MireilleLouys, ArnoldRots, JesusSalgado, DougTody | |||||||
Use CasesThe Cube model includes the following use cases: 1) Simple 2-D pixelated image. (the most common astronomical image type) 2) A single image cube which is sparse in either the spectral, spatial, or temporal plane. eg: JCMT spectral data cubes are sparse in the spatial plane. 3) Multiple sub-array cubes are often produced by instruments having multiple detectors. These cubes contain multiple cube segments which combine to form a complete, potentially sparse, cube. eg: spectral data cube with multiple spectral bands. eg: CCD mosaic where detectors may not be aligned (requiring different WCS) 4) Very large cubes. Cube data may be dispersed among multiple storage containers. The user-facing instance must hide this underlying complexity, providing the appearance of a single cube. 5) Interactive cube visualization and analysis. This application use case involves interpreting the cube instance, visualizing the content (possibly at various resolutions), interactively performing various operations on the cube ( slice, dice, 2-D projections, spectral extraction, etc )Cube Classifications:The above use cases outline the following classifications of cube data which are to be covered by this model: 1) Simple image: A single filled or mostly filled N-Dimensional image array with associated metadta describing the dataset, its WCS coordinate systems, etc. 2) Simple sparse image: A single N-Dimensional array as in #1, however, large portions of the image may be sparse (have no data samples) | ||||||||
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< < | 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. | |||||||
> > | 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. | |||||||
4) Large cube: Very large cubes may be represented as either a simple image or multiple subarray type. The cube model does not impose size limitations, so only important factor here, is that the cube description must be independent of the physical storage structure.
5) Wide-field survey: This is essentially the same as #3, except no subarrays (no explicitly pixelated data). Only automated virtual data generation techniques may be used to access the data, with sub-regions being computed on-the-fly and returned to a client. The mechanism for generating the cube is not relevant to its description, so this class is covered by #1 or #3.
6) Sparse data cube: Sparse data are commonly used for higher-dimensional cubes, and are typically sparse along one or more axes. eg; a multi-band image with only a few spectral coordinates (bands). Event data is an example of a data cube which is sparse in all measurement axes.
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DocumentsLatest document: The latest released document can be found on the IVOA Documents and Standards page. Volute: The volute repository holds all revisions of the document, as well as the source open office document, diagrams, etc. here UML Model: The model is being developed in UML, using Modelio-3.0. The model project can be obtained as a zip file in the volute repository, here. We also provide an export of the UML specification in XMI format (version 2.4.1), which is compatible with the vo-dml xslt scripts for generating the vo-dml XML representation. vo-dml: VO-DML XML serialization of the model and corresponding HTML page are hereDiscussionsMuch of the initial work for the cube model was done under the title of ImageDM. These discussion threads are recorded in the ImageDM twiki. Since 2014, the Cube model work has proceded under this title. Relevant discussion threads are recorded below:Implementations | ||||||||
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Instances of Cube require the use of several contributing models. The list below provides pointers to the relevant model documentation for the models used in the example serialization set. These files will be transferred to the ivoa repository as the models become REC.
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< < | The model documents (Modelio save set and XMI exports), along with generated UML diagrams are stored in the Volute repository . | |||||||
> > | Much of the initial work for the cube model was done under the title of ImageDM. Please see the <a target="_self" href="ImageDM" title="ImageDM twiki">ImageDM twiki</a> for information on that effort. Since Nov. 2013, the work proceeded under this title. The primary difference being to express the Cube model as an extension of a more generic observation dataset. As such, this model is dependent on supporting component models DatasetMetadata, and STC2. | |||||||
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contributors: MarkCresitelloDittmar (editor); FrancoisBonnarel, JoseEnriqueRuiz, OmarLaurino, MireilleLouys, ArnoldRots, JesusSalgado, DougTody
Use CasesThe Cube model includes the following use cases: 1) Simple 2-D pixelated image. (the most common astronomical image type) 2) A single image cube which is sparse in either the spectral, spatial, or temporal plane. eg: JCMT spectral data cubes are sparse in the spatial plane. 3) Multiple sub-array cubes are often produced by instruments having multiple detectors. These cubes contain multiple cube segments which combine to form a complete, potentially sparse, cube. eg: spectral data cube with multiple spectral bands. eg: CCD mosaic where detectors may not be aligned (requiring different WCS) 4) Very large cubes. Cube data may be dispersed among multiple storage containers. The user-facing instance must hide this underlying complexity, providing the appearance of a single cube. 5) Interactive cube visualization and analysis. This application use case involves interpreting the cube instance, visualizing the content (possibly at various resolutions), interactively performing various operations on the cube ( slice, dice, 2-D projections, spectral extraction, etc )Cube Classifications:The above use cases outline the following classifications of cube data which are to be covered by this model: 1) Simple image: A single filled or mostly filled N-Dimensional image array with associated metadta describing the dataset, its WCS coordinate systems, etc. 2) Simple sparse image: A single N-Dimensional array as in #1, however, large portions of the image may be sparse (have no data samples) 3) Multiple subarray image: An image dataset containing multiple subarrays, ie: N-Dimensional image data arrays within the coverage of the overall image dataset. The subarrays may differ in size, resolution, coverage, or other characteristics. The overall dataset containing the subarrays may be sparse. The overall dataset does not have an explicit geometry or sampling, only coverage. 4) Large cube: Very large cubes may be represented as either a simple image or multiple subarray type. The cube model does not impose size limitations, so only important factor here, is that the cube description must be independent of the physical storage structure. 5) Wide-field survey: This is essentially the same as #3, except no subarrays (no explicitly pixelated data). Only automated virtual data generation techniques may be used to access the data, with sub-regions being computed on-the-fly and returned to a client. The mechanism for generating the cube is not relevant to its description, so this class is covered by #1 or #3. 6) Sparse data cube: Sparse data are commonly used for higher-dimensional cubes, and are typically sparse along one or more axes. eg; a multi-band image with only a few spectral coordinates (bands). Event data is an example of a data cube which is sparse in all measurement axes.Requirements
DocumentsLatest document: The latest released document can be found on the IVOA Documents and Standards page. Volute: The volute repository holds all revisions of the document, as well as the source open office document, diagrams, etc. here UML Model: The model is being developed in UML, using Modelio-3.0. The model project can be obtained as a zip file in the volute repository, here. We also provide an export of the UML specification in XMI format (version 2.4.1), which is compatible with the vo-dml xslt scripts for generating the vo-dml XML representation. vo-dml: VO-DML XML serialization of the model and corresponding HTML page are hereDiscussionsMuch of the initial work for the cube model was done under the title of ImageDM. These discussion threads are recorded in the ImageDM twiki. Since 2014, the Cube model work has proceded under this title. Relevant discussion threads are recorded below:Implementations |
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Cube Data Model EffortThe Cube Data Model, currently under development, will describe multdimensional image and cube datasets and instances.SVN Repository:The model documents (Modelio save set and XMI exports), along with generated UML diagrams are stored in the Volute repository .Ongoing Discussions:See ImageDM twiki for discussion thread information to date. |
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SVN Repository:The model documents (Modelio save set and XMI exports), along with generated UML diagrams are stored in the Volute repository .Ongoing Discussions:See ImageDM twiki for discussion thread information to date. |
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