Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system. A plurality of IO features are generated using the plurality of IO requests. The plurality of IO features are processed using a machine learning model. A ransomware attack on the storage system may be monitored for in real-time based upon, at least in part, the processing of the plurality of IO features using the machine learning model.

Abstract: A method, computer program product, and computing system for determining a respective past activity level associated with a plurality of storage objects. The plurality of storage objects are divided into a plurality of storage object groups based upon, at least in part, the respective past activity level associated with the plurality of storage objects. Input/output (IO) performance data for a first storage object group of the plurality of storage object groups is forecasted using a first machine learning model. IO performance data for a second storage object group of the plurality of storage object groups is forecasted using a statistical method.

Abstract: One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Abstract: An information handling system instantiates a hardware configuration generator to generate platform configurations. The hardware configuration generator provides a ranked list of the platform configurations, receives platform customer service request information, provides a ranked list of part numbers associated with the platform configurations based upon the customer service request information, and modifies the ranked list of platform configurations based upon the ranked list of part numbers.

Abstract: Techniques described herein relate to a method for identifying anomalies in compensation data. The method includes identifying a compensation data anomaly detection event; in response to identifying the compensation data anomaly detection event: obtaining compensation data associated with the compensation data anomaly detection event; performing preprocessing on the compensation data to generate updated compensation data; performing feature grouping on the updated compensation data to generate grouped compensation data; performing change discovery using the grouped compensation data to identify potential anomalies; generating a comparative anomaly prediction using the potential anomalies; and performing anomaly remediation actions based on the comparative anomaly prediction.

Abstract: Techniques described herein relate to a method for performing preprocessing of documents for data extraction. The method includes obtaining a document preprocessing request; in response to obtaining a document preprocessing request: obtaining a document associated with the document preprocessing request; performing data preparation on the document to generate an updated document; generating a document type prediction using the updated document and a document type prediction model; identifying a data extraction service of a plurality of data extraction services associated with the document type prediction; and initiating further processing of the document to perform data extraction using the identified data extraction service.

Abstract: Techniques described herein relate to a method for performing data extraction for documents. The method includes obtaining a data extraction request associated with a document; in response to obtaining the request: generating a data extraction prediction using a prediction model and the document; providing the data extraction prediction to a user; obtaining a user validation associated with the data extraction prediction; making a determination that the user validation indicates that the data extraction prediction is not correct; in response to the determination: generating an updated data extraction prediction based on the user validation; and initiating performance of additional document processing using the document based on the updated data extraction prediction.

Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) operations on a plurality of storage objects of a storage system. The plurality of storage objects may be divided into a plurality of storage object groups based upon, at least in part, the plurality of IO operations processed on the plurality of storage objects. Each storage object group may be associated with an IO machine learning model selected from a plurality of IO machine learning models, thus defining a plurality of storage object group-specific IO machine learning models. IO performance data may be forecasted for the plurality of storage objects using the plurality of storage object group-specific IO machine learning models.

Abstract: Techniques for forecasting temperatures of storage objects in a storage system using machine learning (ML). The techniques can include forecasting at least one temperature of a storage object using at least one ML model, modifying storage of the storage object based on the at least one temperature of the storage object, and, having modified storage of the storage object, obtaining at least one performance metric associated with the storage object. The techniques can further include, based on the performance metric(s), varying a frequency of forecasting the at least one temperature of the storage object, retraining the at least one ML model used in forecasting the at least one temperature, and/or adjusting at least one operational parameter of the system. The techniques provide increased accuracy over known statistical approaches to forecasting temperatures of storage objects, leading to increased performance gains in terms of IO latency, IO operations per second, and bandwidth.

Abstract: Methods, apparatus, and processor-readable storage media for automated communication data summarization using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying one or more words relevant to one or more predefined tasks by processing input communication data using a first set of one or more artificial intelligence techniques; extracting, based at least in part on the identifying of the one or more words, multiple portions of the input communication data into individual sub-portions of the input communication data; generating at least one summarization of the input communication data based at least in part on processing the sub-portions of the input communication data using a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one summarization of the input communication data.

Abstract: Methods, apparatus, and processor-readable storage media for automated address data determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes extracting address information from one or more documents using at least one artificial intelligence-based address extraction model; parsing, into one or more address components, at least a portion of the extracted address information using at least one artificial intelligence-based parsing model; comparing at least a portion of the one or more parsed address components to stored address information using at least one artificial intelligence-based scoring model; and performing one or more automated actions based at least in part on results from the comparing of at least a portion of the one or more parsed address components to stored address information.

Abstract: One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Abstract: Techniques described herein relate to a method for performing testing operations for information handling systems.