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An important element of the VO is a data model that can unambiguously represent the relationships between data values and physical properties. At the CfA we are developing a data model design that can support the representation, analysis and display of data collected on different types of instruments. This model is a common, high-level framework of general-purpose components for fusion of heterogeneous data sources. From this framework, we have focused on a subset of components required to meet selected science objectives on spectral and image data.
The Relative Observational Phase Space Volume & Observable component specifies the region of physical space being observed ( Phase Space, which may have dimensions of space, time, wavelength, etc.) and the quantity being measured ( Observable) relative to the observatory location. These values can be translated to an Absolute reference by using data in the Observatory Location.
The Mapping component provides the translation from pixel elements to volumes in the phase space. It also specifies the relationship between the pixel values and physical values.
The Generic Mapping component provides a framework for organizing standard data transformations. It can be thought of as a library of transformations that may be used to define the specific mappings needed in a dataset. This library includes the usual astronomical spherical projections as well as mappings between units, between coordinate systems and between data values that are denoted using interchangeable properties such as frequency and wavelength.
To support generality, the Data Container methods always allow the list of indexes to be obtained and used to iterate through the data cells. The data value and/or metadata can be obtained for each cell, in essence using heavyweight objects for each data item. A data consumer (i.e., application software) can fall back on this form to process the data if it does not recognize the Index Set's structure.
To support efficiency, the Index Set conforms to one of a small set of archetypal structures such as array, array with bad cell mask, sparse array, or event list. Application software can then be designed to take advantage of the structure to organize processing.
Metadata describing the correspondence between the data cells and
locations in detector or observational space is represented as a
collection of pixel mappings ,
,
into coordinate spaces
,
,
Similarly, interpretation of the data cell values is handled by a
value mappings
,
,
into
coordinate spaces
,
,
Depending
on the need, the VMs may depend on the cell location as specified by
its index.
These mappings are not simply computable functions, but also have the type and parameters of the transformation encoded, such as constant, linear, piecewise linear or tangent projection. Thus, the application program can inspect this information to best organize its processing.
The Index Set is not constrained to be rectangular. Using this feature, another Data Container can be defined describing just the Wide Field Camera, as shown in the accompanying figure below. This object uses a different Index Set and correspondingly different mappings to access the same data. The data provider (i.e., archive) defines the Data Container(s) and Index Set(s). This gives the provider the flexibility to create an organization natural for its data, while at the same time define alternate views for different audiences or purposes.
In a fiberoptic spectrometer, 1-D spectra are measured at a number of
irregularly-arranged sky positions. As seen in the next figure, the
data may be stored as a 2-D
array, each row holding the spectrum for a single position.
Consequently, each array element maps to a location in the
3-D domain sky wavelength.
Our next steps in moving the data model development forward are: