| |
 |
| Paper: |
Chapter 11: Web-based Tools—VO Region Inventory Service |
| Volume: |
382, The National Virtual Observatory: Tools and Techniques for Astronomical Research |
| Page: |
99 |
| Authors: |
Good, J.C. |
| Abstract: |
As the size and number of datasets available through the VO grows, it becomes increasingly critical to have services that aid in locating and characterizing data pertinent to a particular scientific problem. At the same time, this same increase makes that goal more and more difficult to achieve. With a small number of datasets, it is feasible to simply retrieve the data itself (as the NVO DataScope service does). At intermediate scales, “count” DBMS searches (searches of the actual datasets which return record counts rather than full data subsets) sent to each data provider will work. However, neither of these approaches scale as the number of datasets expands into the hundreds or thousands. Dealing with the same problem internally, IRSA developed a compact and extremely fast scheme for determining source counts for positional catalogs (and in some cases image metadata) over arbitrarily large regions for multiple catalogs in a fraction of a second. To show applicability to the VO in general, this service has been extended with indices for all 4000+ catalogs in CDS Vizier (essentially all published catalogs and source tables). In this chapter, we will briefly describe the architecture of this service, and then describe how this can be used in a distributed system to retrieve rapid inventories of all VO holdings in a way that places an insignificant load on any data supplier. Further, we show and this tool can be used in conjunction with VO Registries and catalog services to zero in on those datasets that are appropriate to the user‛s needs. The initial implementation of this service consolidates custom binary index file structures (external to any DBMS and therefore portable) at a single site to minimize search times and implements the search interface as a simple CGI program. However, the architecture is amenable to distribution. The next phase of development will focus on metadata harvesting from data archives through a standard program interface and distribution of the search processing across multiple service providers for redundancy and parallelization. |
|
|
|
|
|
