Abstract: Systems and methods for splitting an electronic file into sub-documents are provided. The electronic file is received. Portions of the electronic file are classified using a trained machine learning based model. The classifications represent relative positions of the portions within sub-documents of the electronic file. The electronic file is split into the sub-documents based on the relative positions of the portions. The sub-documents are output.

Abstract: A system, method and a computing device for performing a method for data augmentation allowing for document classification of a plurality of documents are disclosed. The system, method and computing device including a processor configured to convert the plurality of documents into images, a memory configured to store the images, the processor configured to obtain a vector representation for each page included in the plurality of documents, the processor configured to create a plurality of clusters from the images based on similarity, where each cluster of the plurality of clusters represents a distinct page format, the processor configured to select one image from each cluster of the plurality of clusters, the processor configured to compile the selected one image from each cluster of the plurality of clusters to create a logically complete document, the memory configured to store the logically complete document, and the processor configured to train the classification based on the complete document.

Abstract: A system and computer-based method for performing a data transaction in a network storage system is provided. In an embodiment, the method includes receiving, from a client, a first data transaction request including a first data identifier associated with a first namespace protocol; determining a storage system associated with the first data identifier and associated with a second namespace protocol; determining a second data identifier associated with the first data identifier, wherein the second data identifier conforms to the second namespace protocol; and forwarding a second data transaction request including the second data identifier to the storage system based on determining an association between the second data identifier and the first data identifier, wherein the first namespace protocol and the second namespace protocol are different.

Abstract: A method and system for grouping and compressing system logs are described herein. A generation module residing and executing on a computer system may be configured to generate system logs containing system data important for support purposes describing the computer system, and system log metadata for describing the system logs. A grouping module residing and executing on a computer system may be configured to group system logs based on the system log metadata. Each group of system logs may comprise two or more system logs having the same or similar system log metadata. A data container is produced for each group of system logs and the data container is compressed. System logs having the same or similar system log metadata are more likely to have similar data, thus resulting in a higher degree of compression when grouped and compressed together.

Abstract: In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.

Abstract: A system and computer-based method for performing a data transaction in a network storage system is provided. In an embodiment, the method includes receiving, from a client, a first data transaction request including a first data identifier associated with a first namespace protocol; determining a storage system associated with the first data identifier and associated with a second namespace protocol; determining a second data identifier associated with the first data identifier, wherein the second data identifier conforms to the second namespace protocol; and forwarding a second data transaction request including the second data identifier to the storage system based on determining an association between the second data identifier and the first data identifier, wherein the first namespace protocol and the second namespace protocol are different.

Abstract: In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.

Abstract: A system includes tracing logic to parse trace information into time varying segments and model traces based on segments of time varying I/O (input/output) and/or workload behavior. The logic can detect segments that represent statistically similar system behavior and reduce the number of segments based on detecting segments representing similar system behavior. The logic can leverage Mutual Information techniques to eliminate redundant workload dimensions and build a concise workload model. The logic can also use HAC to segregate similar workload patterns represented by multiple non-redundant workload attributes. The logic can use ePDF to regenerate distributions of workload attribute values during trace regeneration. The logic can generate segment models from the segments, which can be combined into a test trace that represents a period of system behavior for simulation.

Abstract: In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.