Abstract: A system and method for accelerating applications in closed networks through intelligent compression and routing. The system includes a flow analysis system that classifies network traffic as latency-critical or bandwidth-critical, enabling optimized handling of different flow types. Latency-critical flows are transmitted directly to minimize delay, while bandwidth-critical flows undergo compression using dynamically selected methods including Huffman, alphabetic, and Tunstall coding. The system maintains synchronized codebooks across network nodes while enabling node-specific optimizations based on local traffic patterns. A network topology manager maintains comprehensive network state awareness, enabling intelligent route selection based on flow classification and current conditions. The system continuously monitors performance and adapts compression and routing strategies in real-time.

Abstract: A system and method for random-access manipulation of compacted data files, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes by transforming the codebook into a hierarchical representation and then traversing the representation scanning for specific codewords associated with a data query request. In an embodiment, an estimator module is present and configured to utilize cardinality estimation to determine a starting codeword to begin searching the compacted data file for the data associated with the data query. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

Abstract: This invention is a system and method for encrypted video stream data compaction. The system integrates video processing techniques with data compaction methods, achieving compressed outputs that maintain data security. A video stream processor analyzes frames, estimates motion, and eliminates redundancies, preparing the data for further compression. The processed data is then divided into blocks, mapped to codewords, and subjected to additional compression and encryption rules. This multi-layered approach results in reduced data size and bandwidth requirements for video streaming, while ensuring data integrity and security. The system's adaptability makes it suitable for various video streaming applications, from high-definition content delivery to secure video communications.

Abstract: A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security.

Abstract: A system and method for enhancing lossy compressed data. The system receives a compressed data stream, decompresses it, and enhances the decompressed data using adaptive neural network models. Key features include data characteristic analysis, dynamic model selection from multiple specialized neural networks, and quality estimation with feedback-driven optimization. The system adapts to various data types and compression levels, recovering lost information without detailed knowledge of the compression process. It implements online learning for continuous improvement and includes security measures to ensure data integrity. The method is applicable to diverse data types, including financial time-series, images, and audio.

Abstract: A system and method for simultaneous compression and encryption of data. The system analyzes input data to determine its properties and creates a transformation matrix based on these properties. Using this matrix, the input data is transformed into a modified distribution, generating a main data stream of transformed data and a secondary stream of transformation information. The main data stream is compressed, and both streams are combined into a single output. The system implements security measures to protect against various attacks, including side-channel vulnerabilities. By using a dyadic distribution algorithm, the system achieves both compression and encryption in a single pass over the data, offering significant efficiency gains. The system can operate in both lossless and lossy modes, providing flexibility for different application requirements. This approach offers a unique solution for data transmission and storage scenarios where both data reduction and security are critical concerns.

Abstract: A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements.

Abstract: A system and method for multi-layer data processing with selective encryption and transformation. The system generates a multi-layer data structure comprising reference elements derived from input data separated into multiple layers. Data processors receive and process input data using this structure, correlating input elements with reference elements. The system selectively encrypts one or more layers based on predefined encryption policies and applies transformation rules to processed data elements as specified in the structure. The output is a sequence of transformed, untransformed, and selectively encrypted elements. The system includes an encryption policy subsystem for managing layer-specific policies, a key management subsystem for handling encryption keys, and an enhanced decoder capable of processing both encrypted and non-encrypted data streams.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.

Abstract: Codebook data compaction using a universal codebook and mismatch probability estimations to improve entropy encoding methods. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook.

Abstract: A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

Abstract: A system and methods for multi-type data compression or decompression with a virtual management layer in a distributed computing environment, comprising. It incorporates a virtual management layer to organize incoming data types and allocate compression or decompression tasks across multiple computing devices, selecting techniques best suited for particular data types. Associated data sets may be flagged prior to processing, ensuring preservation of relationships even when compressed or decompressed on different devices. This distributed approach allows efficient parallel processing of multiple data types, improving scalability and performance. A load balancing module optimizes task distribution based on available resources and processing requirements. The system enables each data type to be processed using the most efficient technique while maintaining associations between related data sets, effectively handling larger volumes of diverse data.

Abstract: A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time.

Abstract: A system and method for data compaction utilizing distributed codebook encoding to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted, allow for distributed encoding and decoding capabilities, and allow for parametrized codebook encoding methods. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Further, a codebook and a behavior codebook may both be maintained or altered in a distributed fashion across multiple devices or services, for widespread, or permission-based, or parametrized codebook encoding.

Abstract: A system and method for encrypted data compression, which uses frequency analysis on data blocks within an input data stream to produce a prefix table, representing a first layer of transformation, and which applies a Burrow's-Wheeler transform (BWT) to the data inside the prefix table, representing a second layer of transformation, and which compresses the transformed data. In some implementations, the system and method may further include applying the BWT to a conditioned stream of genomic data, wherein the conditioned stream of data is accompanied by an error stream comprising the differences between the original data and the encrypted data.

Abstract: A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

Abstract: A system and methods for multi-type data compression or decompression with a virtual management layer, comprising. It incorporates a virtual management layer to organize incoming data types and select a compression or decompression system that utilizes a technique best suited for a particular data type. Associated data sets may be flagged prior to compression or decompression so that associated types may be preserved together after the compression or decompression process is complete. This approach allows each data type to be compressed or decompressed using a technique that is the most efficient for a particular data type. Additionally, the approach allows all information associated with a particular data set to be compressed or decompressed in some way.

Abstract: A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

Abstract: A system and method for lossy precompression for data compaction using automated model monitoring and training, wherein statistical analyses of test datasets are used to determine if the probability distribution of two datasets are within a pre-determined range, and responsive to that determination new encoding and decoding algorithms may be retrained in order to produce new data sourceblocks, and pre-compression of data prior to processing and statistical analysis allows for the compaction of already compressed data into highly dense formats. The new data sourceblocks may then be processed and assigned new codewords which are compiled into an updated codebook which may be distributed back to encoding and decoding systems and devices.

Abstract: A system and method for highly efficient encoding of data that includes extended functionality for asymmetric encoding/decoding and network policy enforcement. In the case of asymmetric encoding/decoding the original data is encoded by an encoder according to a codebook and sent to a decoder, but the output of the decoder depends on data manipulation rules applied at the decoding stage to transform the decoded data into a different data set from the original data. In the case of network policy enforcement, a behavior appendix into the codebook, such that the encoder and/or decoder at each node of the network comply with network behavioral rules, limits, and policies during encoding and decoding.