Parallel algorithms for on-line dynamic data compression

The authors consider parallel algorithms for online (real-time) data compression that use dynamic (adaptive) learning by textual substitution. The author´s model of computation is a systolic pipe, in which processors are arranged in a linear array (each processor connected only to its left and right neighbors). He first considers an array of processors that is large enough to accommodate one dictionary entry per processor. He then discusses using an existing parallel machine where the number of available processors might be limited