Novell, Inc.

Micro Focus Software Inc.

Craig N Teerlink

Scott Alan Isaacson

Nadeem Ahmad Nazeer

Paul James Taysom

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 with or without the use of tree mechanisms 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.

Generating and merging keys for grouping and differentiating volumes of files

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.

Fast approximation to optimal compression of digital data

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.

Stopping functions for grouping and differentiating files based on content

Methods and apparatus teach a digital spectrum of a file for use in determining positional relationships between groups of files. The digital spectrum is used to reveal distances between the files to sort into groups of related files. Representatively, files determined by a nearest neighbor method to be related are sorted into groups of related files. In turn, the groups of related files are determined to be related, or not, according to positional location in N-dimensional space. A centroid location for each group of related files is determined, and a distance value from that centroid location to each member file of the group of related files is determined. In turn, centroid-to-centroid distance values are determined between each group of related files and rank-ordered. Comparing the centroid-to-centroid distance values and the distance values between centroid locations and files of the groups of related files reveals relatedness, or not, of groups of files.

Positional relationships between groups of files

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.

Optimal sequential (de)compression of digital data

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.

Grouping and differentiating files based on content

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.

Optimized partitions for grouping and differentiating files of data

A plurality of original files represent underlying original bits of data from which a key is created in a multi-dimensional mapping space for a relevancy topic. If new data is sufficiently close to this relevancy topic key, it is related to the topic and presented to users. Various closeness measures are defined as are methods for key creation. Still other features contemplate computing arrangements and program products.

Relevancy filter for new data based on underlying files

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.

Encryption/decryption of digital data using related, but independent keys

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.

Grouping and differentiating volumes of files

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 &#x201c;patterns of patterns.&#x201d; 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. The new files are then grouped or differentiated from one another based these new relationships yielding insight into how the original files can be grouped or differentiated.

Grouping and differentiating files based on underlying grouped and differentiated files

Methods and apparatus identify groups of files based on symbols corresponding to an underlying data stream of original bits of data that are determined to be informationally important. The resulting symbols of a selected group are ordered according to how effectively each symbol characterizes the selected group of interest. The subset of symbols is used to find similar files from a general population of files to the files in the group of interest. Additionally, groups of common files can be identified from a general population of files and a group selected therefrom for use in identifying a subset of symbols which characterize the selected group for use as a filter to identify further like files.

Growing community of inventors

Cedar Hills, UT, United States of America

Craig N Teerlink