Documentation for the machine-readable version of the Stellar Spectrophotometric Atlas, 3130 A lamda 10800 A of Gunn and Stryker (1983)

A detailed description of the machine-readable version of the atlas as it is currently beng distributed from the Astronomical Data Center is given. The data were obtained with the Oke multichannel scanner on the 5-meter Hale reflector for purposes of synthesizing galaxy spectra, and the digitized atlas contains normalized spectral energy distributions, computed colors, scan line and continuum indices for 175 selected stars covering the complete ranges of spectral type and luminosity class. The documentation includes a byte-by-byte format description, a table of the indigenous characteristics of the magnetic tape file, and a sample listing of logical records exactly as they are recorded on the tape

Documentation for the machine-readable version of the Stellar Spectrophotometric Atlas, 3130 A lambda 10800 A of Gunn and Stryker (1983)

The machine-readable version of the Atlas as it is currently being distributed from the Astronomical Data Center is described. The data were obtained with the Oke multichannel scanner on the 5-meter Hale reflector for purposes of synthesizing galaxy spectra, and the digitized Atlas contains normalized spectral energy distributions, computed colors, scan line and continuum indices for 175 selected stars covering the complete ranges of spectral type and luminosity class. The documentation includes a byte-by-byte format description, a table of the indigenous characteristics of the magnetic tape file, and a sample listing of logical records exactly as they are recorded on the tape

An Automatic Intelligent System for Document Processing and Fruition

With the increasing amount of documents available on-line, the need for intelligent digital libraries, that allow to automatize the document processing tasks and to suitably organize and make available the documents so as to provide personalized and focused access, becomes more and more pressing. This paper proposes an integrated system that merges intelligent modules covering all the phases involved in a document lifecycle, from acquisition, to processing, to information extraction, to personalized fruition for final users. The role and possible cooperation of Machine Learning and Data Mining techniques in the system is highlighted and discussed, along with their importance to provide effective support to both the building and the fruition of the Digital Library and the underlying knowledge base