This paper introduces a binary encoding that supports arbitrarily large, small and precise decimals. It completely preserves information and order. It does not rely on any arbitrary use-case-based choice of calibration and is readily implementable and usable, as is. Finally, it is also simple to explain and understand.
@cite_7: This paper proposes a new method of encoding numbers by variable-length byte-strings. The primary property of the encoding is that the lexicographic comparison of the encoded numbers corresponds correctly to the order of the real numbers. The encoding is space-efficient. Further, unlike the fixed-length representations of numbers (fixed-point, floating-point, etc.,) the encoded numbers are not limited in their magnitude or the number of their significant digits. The paper also elaborates the application of the encoding method to the storage of numeric data in databases. The proposed application for databases is a uniform format for all the numbers, regardless of their types and attributes (fields). All the numbers are represented in a form of lexicographically-comparable byte-strings. This form simplifies the data management software (only one format to deal with at the physical database level) and hardware (when associative memory and storage devices etc. are used); makes the applications more flexible (by removing limitations on the sizes of numbers); and is space-efficient for all numbers while being especially concise for those numbers that are used more frequently in databases.
The Rishe Encoding @cite_7 is the closest match to decimalInfinite we found in literature in terms of problem solving. It supports arbitrarily large, small and precise decimals, is compact, and is also compatible with a bitwise lexicographic comparison. However, it relies on an arbitrary choice of intervals (128) based on the use case. decimalInfinite does not rely on such a choice and scales up continuously, regardless of how large, small or precise decimals are.