Patent ID: 7398276
Filing Date: 2008-07-08
Classification: G06F,Y10S

Abstract:
1. A method for decompressing segments of data as needed and without requiring sequential selection or decompression of the data even though the compression was performed using predictive models, the method comprising the following: an act of accessing a compressed version of a first segment of the data, the first segment being represented by a sequence of symbols; an act of decompressing the first segment of the data; an act of accessing a first predictive data decompression model which operates by predicting characters or symbols based on sequential processing of immediately preceding characters or symbols, wherein the first predictive data decompression model was generated using the sequence of symbols that represents the first segment; an act of determining that a second segment of the data is desirable, wherein the second segment does not appear sequentially with the first segment in the data, wherein the act of determining that a second segment of the data is desirable comprises the following: an act of executing an executable list within the first segment; while executing within the first segment, detecting that an instruction is needed from the second segment, wherein the act of detecting that an instruction is needed from the second segment comprises the following: an act of accessing a compressed version of the second segment of the data; and an act of decompressing the second segment of the data after decompressing the first segment, and using the first predictive data decompression model to predict characters or symbols in the second segment of data, wherein the first predictive data compression model acts as a predictive starting point such that when decompression of the second segment of data begins, the first predictive model is based only on the first segment of data, such that when decompression of the second segment of data begins, predictions of characters or symbols of the second segment of data, which is not sequential with the first segment, are based entirely on the first predictive model of the first segment of data, and exclude consideration of all intervening segments of data between the first segment of data and the second segment of data.