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Compressed inverted indices in use today are based on the idea of gap compression: documents pointers are stored in increasing order, and the gaps between successive document pointers are stored using suitable codes which represent smaller gaps using less bits. Additional data such as counts and positions is stored using similar techniques. A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specializeddoi:10.1145/2433396.2433409 dblp:conf/wsdm/Vigna13 fatcat:l3pbv5ulzzb73as2p6vxmonsna