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This document describes a convention for compressing FITS binary tables that is modeled after the FITS tiled-image compression method (White et al. 2009) that has been in use for about a decade. The input table is first optionally subdivided into tiles, each containing an equal number of rows, then every column of data within each tile is compressed and stored as a variable-length array of bytes in the output FITS binary table. All the header keywords from the input table are copied to the header of the output table and remain uncompressed for efficient access. The output compressed table contains the same number and order of columns as in the input uncompressed binary table. There is one row in the output table corresponding to each tile of rows in the input table. In principle, each column of data can be compressed using a different algorithm that is optimized for the type of data within that column, however in the prototype implementation described here, the gzip algorithm is used to compress every column.
This document describes a convention for compressing n-dimensional images and storing the resulting byte stream in a variable-length column in a FITS binary table. The FITS file structure outlined here is independent of the specific data compression
This document describes a FITS convention developed by the IRAF Group (D. Tody, R. Seaman, and N. Zarate) at the National Optical Astronomical Observatory (NOAO). This convention is implemented by the fgread/fgwrite tasks in the IRAF fitsutil package
The checksum keywords described here provide an integrity check on the information contained in FITS HDUs. (Header and Data Units are the basic components of FITS files, consisting of header keyword records followed by optional associated data record
Many data we collect today are in tabular form, with rows as records and columns as attributes associated with each record. Understanding the structural relationship in tabular data can greatly facilitate the data science process. Traditionally, much
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aim