Abstract: Methods and apparatus involve two keys to decode data that are generated during original encoding of the data. The keys are stored on computing devices separate from one another, and the encrypted data, which maintains security until such time as the original data requires decoding. Because the keys can be relatively large, its stored form may have padding bits to align with the file form of the encoded data. Representative keys include a dictionary corresponding to symbols representing the data and a weighted path decoder that correlates the symbols of the dictionary to underlying original bits. A “fast approximation” of compression of current data involves using information obtained from an earlier compression of similar data. Creating the two keys for the original data can also include creating a master key for decoding a plurality of later-encoded files. A second key also works in conjunction with the master key during decoding.

Abstract: Methods and apparatus involve an original data stream arranged as a plurality of symbols. Of those symbols, all possible tuples are identified and the highest or most frequently occurring tuple is determined. A new symbol is created and substituted for each instance of the highest occurring tuple, which results in a new data stream. The new data stream is encoded and its size determined. Also, a size of a dictionary carrying all the original and new symbols is determined. The encoding size, the size of the dictionary and sizes of any other attendant overhead is compared to a size of the original data to see if compression has occurred, and by how much. Upon reaching pre-defined objectives, compression ceases. Decompression occurs oppositely. Other features include resolving ties between equally occurring tuples, path weighted Huffman coding, storing files, decoding structures, and computing arrangements and program products, to name a few.

Abstract: Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position. This position relative to another file's position reveals distances between the files. Representatively, files have a plurality of symbols representing an underlying data stream of original bits of data. The number of occurrences of each symbol in each file is compared to like symbols in other files. This can occur via algorithms, mapping, or both. In certain instances, comparison reveals a difference in counts between the symbols of the files. This difference is then squared, added together, and a square root taken. Comparing “distance values” reveals file adjacency, grouping, or the like. Also, normalizing, weighting, filtering functions and/or other statistical computations are applied in certain instances.

Abstract: A digital spectrum defines and communicates a file's informational characteristics. A file's informational position may be represented as a vector in an N-dimensional space, where each dimension is defined by a symbol. A position along the axis of any given dimension is described by the frequency of occurrence of that symbol. Relative to the origin of the space, the file's position can be computed. Comparing positions reveals similarity, or not, of the files. Another method uses the digital spectrum to define a line graph. Each symbol and its frequency define points on the line. A distance function between two spectra line graphs is computed. Comparing values from the distance functions reveals similarity, or not, of the files. Also, total numbers of bits in the files are extracted by knowing the lengths of the original bits corresponding to every symbol. A symbol bit length spectrum is also defined.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together to define a set. Overlapping members in the groups may be further differentiated from one another by partitioning. An optimized partition of set S of N overlapping groups yields a maximum strength for groups and members in that partition. Among other things, the optimized partition includes relative strengths of every individual member in every possible partition and weighting functions applied to the relative strengths and to subgroups of files within the partitions.

Abstract: Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position in a multi-dimensional space. This position relative to another file's position reveals distances between the files. Closest files can be grouped together. When contemplating voluminous numbers of files for digital spectrums, various methods include: concatenating all such files together to get a single key useful for creating a file's spectrum; or compressing files individually and combining their collective dictionaries into a single dictionary that defines the digital spectrum. Each provides advantage over the other. The latter consumes considerably less run time because each compression event can be distributed to a separate processor. Method two provides better spectrums because it is more “informationally” valid than is method one.

Abstract: A “fast approximation” of compression of current data involves using information obtained from an earlier compression of similar data. It overcomes the iterative process of discovering a unique set of optimal symbols. Representatively, a dictionary of symbols corresponding to original data from an earlier compressed file is extracted. Original bits are then obtained from the symbols. Sequences of the original bits are identified in the current data of a current file under consideration. A new bit stream for the current file is created from the original bits and according to the symbols they represent. Every occurrence of the symbols is counted in the new bit stream and a path-weighted Huffman tree is created from the counted occurrences. A coding from the Huffman tree ensues, along with an end-of-file marker. The latter is stored in a new compression file, including the dictionary earlier extracted from the earlier compressed file.

Abstract: Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are arranged, first “patterns” of data are created that further define digital spectrums of new files. It is within this sorted new data that emergent relationships or second “patterns” are examined, according to the techniques for its underlying files, or “patterns of patterns.” Representatively, original files are stored on computing devices. If encoded, they have pluralities of symbols representing an underlying data stream of original bits of data. The original files are examined for relationships between each of the files. The original relationships are converted to new files. The new files are representatively encoded and examined for other relationships.