Metadata grouping

Another feature that becomes important for metadata management is metadata grouping. As an example, a blood sample is taken from a mouse and a new metabolite found. We can assign the metadata Mouse, Whole Blood, and In Vivo to this metabolite. We now do a second investigation where we unfortunately have to kill the mouse, take a brain sample, and find the same metabolite. We would now assign Mouse, Brain, and In Vitro to this structure. If we would look now at the metabolite metadata, it would list the meta-keys and its values ... 

If each datument has sufficient high-quality metadata, there may be no essential need for a knowledge base to be centralized. By collecting those publications of interest, any reader or group can create their own personal base. In turn, such metadata can be exported to a wider community using new mechanisms such as RDF or RSS (RDF Site Summary or Rich Site Summary, a way of collecting metadata of interest to an individual reader). 

The M D Group also develops the CERA-2 data model for the World Climate Data Centre, proposing a description of geo-referenced climate data (model output) and containing information for the detection, browse and use of data. An important collaboration is going on with the PAE Metadata and other international initiatives for the development and implementation of metadata standards for the description of model configuration and numerical grids. 

We would not be able to distinguish, for instance, whether the metabolite was found in the blood of the living or the dead mouse. At this point we need a mechanism that allows us to group the entered metadata so that the context becomes clear. 

The tools for developing data warehouses can be grouped into three categories, based on their activities acquisition tools (for inflow), storage tools (for upflow and downflow), and access products (for outflow) (Mattison 1996). Acquisition tools are necessary to perform tasks such as modeling, designing, and populating data warehouses. These tools extract data from various sources (operational databases and external sources) and transform it (i.e., condition it, clean it up, and denormalize it) to make the data usable in the data warehouse. They also establish the metadata, where information about the data in the warehouse is stored (Francett 1994). 

Conceptually, queries in AQL comprise a combination of properties of both the semantic and the syntactic information in the argument structure. As described earlier (Section 2.1), metadata, i.e., attributes on nodes, provide a way to access the semantic information. To access the structure, AQL queries contain expressions referencing GSN syntax. The language (described subsequently in Section 3) itself consists of a selection of atomic queries that can be grouped with the usual logical connectives (and, or, xor, not), as well as the path quantifiers [ ] (all), and () (some), to specify query relations. The language also contains constructs to access structure in terms of the relative arrangement of nodes, e.g., above, below, directly above, etc. 

Accelerometer a class A accelerometer (Working Group on Insfrumentation, Siting, Installation, and Site Metadata 2008), e.g.. Nanometrics Titan... 

ANSS Working Group rai Instrumentation, Siting, Installation, and Site Metadata of the Advanced National Seismic System Technical Integration Committee (2008) Instnunentatimi guidelines for the advanced national seismic system, U.S. Geological Survey. Open-File Report 2008-1262,41 pp... 

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