Patent Application
20190108237
This disclosure relates to a method for content agnostic file referencing. The method may further relate to a method for content agnostic data compression.
File referencing techniques generally require knowledge about the kind of data being stored in order to efficiently index the data in a file referencing system. Similarly, knowledge about the data at issue is also generally used in creating improved compression approaches to reduce data size for transmission, storage, and the like.
There exists a need in the industry to improve file referencing and data compression techniques to reduce the amount of data that must be stored and/or transmitted.
According to one embodiment, this disclosure provides a method for improving computing technology with an enhanced content-agnostic file referencing system.
The disclosed method has several important advantages. For example, the disclosed method permits file referencing of any content type.
The disclosed method additionally permits a significant reduction in the amount of information or data that must be persisted or transmitted, as data may be generated at access time as opposed to persisted.
Various embodiments of the present disclosure may have none, some, or all of these advantages. Other technical advantages of the present disclosure may also be readily apparent to one skilled in the art.
For a more complete understanding of the present disclosure and its advantages, reference is now made to the following descriptions, taken in conjunction with the accompanying drawings, in which:
Similar reference numerals refer to similar parts or steps throughout the several views of the drawings.
The present disclosure relates to a method for content-agnostic indexing of data. The method may be used for a variety of computer-specific needs, including for example as a file referencing system or a compression system.
The disclosure below describes the invention in connection with compression of binary data as exemplary, but the teachings work as well with any type of data, better termed “n-ary” data. For example, the method and system also works with qubits and bits.
One embodiment of the present invention comprises a method as described in the flow chart depicted in
then the input data is 2-bits long. At step 106, all permutations of 2-bits will be generated, namely:
At step 108, the method determines the index (nf) of the input binary data file in the generated permutations. Using the example above, the index (nf) returned would be “1”. Finally, rather than storing or transmitting the input binary data (i.e. “01”), the system instead stores the length (2) and the index (1).
When the need comes to decode the original input data (for instance, a request to retrieve the original binary data from disk, or receipt of the transmitted data across a network), the method needs only a length (l(ni) and an index (nf) as input. Using the above example, the input provided would be the length (2) and the index (1). As shown in
The system would then go to the provided index (1 in the above example) and return the permutation. Again, using the above example, this would return “01” the original binary data.
The above method has been described for purposes of example in terms of a binary system (i.e. the input data is binary data). The method and system work similarly for n-ary systems. While the binary system describes above works essentially in the Euclidean plane, with n-ary data Hilbert spaces conceptually provide the same advantages. The method and process can be generalized for n-ary data per below:
d̂n=p(i)
(d̂n)n=p(f)
d=order of the system
n=length in appropriate n-ary units respective to the order of the system
p(i)=initial index
p(f)=final index
It should be noted that given two alternative ordered systems with the same input file, the system with the higher order will have a higher n-ary density relative to the alternative with a lesser ordered system.
An example of the method is disclosed in the following Ruby code snippets. The below snippet demonstrates a method as disclosed in
The below snippet demonstrates a method as disclosed in
The method and system may preferably be implemented in a computing system, which can include a personal computer, a workstation, a network computer, a hand held computer, or any other computing system. Further, the system may be written as a software program in any appropriate computer language.
The system includes one or more processing devices, which may be any computer processing unit, and could be a single central processing unit, or a number of processing units configured to operate either in sequence or in parallel. The processing device can be configured to execute software processes which implement the steps disclosed herein. The system may also include a memory capable of storing the steps necessary for a processing device to implement the steps disclosed herein. This memory could be in the form of memory resident within the processing device or in the form of standalone memory coupled to the processing unit via a communication path, such as a bus or a network.
Although this disclosure has been described in terms of certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure.
