Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.

Abstract: A storage device for adaptive wear leveling within a data storage system is provided. The storage device includes a host interface configured to receive storage operations for storage and retrieval of data on storage media, a media interface configured to read and write data to the storage media, and a storage controller configured to provide wear leveling for the storage media using a plurality of partitions within the storage media.

Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.

Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

Abstract: A method, apparatus and computer program product are provided for mobile location based sales lead identification. Sales lead information may be provided based on a reference location, such as a sales resource real time location. Additional location information, demand information, competitive information, activity history, follow-up tasks, sales resource ownership information and past promotions relating to a particular sales lead may be additionally provided. The mobile location based sales lead information may allow a sales resource to efficiently manage their time during and in between sales visits, and prepare effective sales pitches to sales leads.

Abstract: A method, apparatus and computer program product are provided for mobile location based sales lead identification. Sales lead information may be provided based on a reference location, such as a sales resource real time location. Additional location information, demand information, competitive information, activity history, follow-up tasks, sales resource ownership information and past promotions relating to a particular sales lead may be additionally provided. The mobile location based sales lead information may allow a sales resource to efficiently manage their time during and in between sales visits, and prepare effective sales pitches to sales leads.

Abstract: A visualization is presented while tuning a machine learning model. A model tuning process writes tuning data to a history table. The model tuning process is repeatedly training and scoring a model type with different sets of values of hyperparameters defined based on the model type. An objective function value is computed for each set of values of the hyperparameters. Data stored in the history table is accessed and used to identify the hyperparameters. (A) A page template is selected from page templates that describe graphical objects presented in the display. (B) The page template is updated with the accessed data. (C) The display is updated using the updated page template. (D) At the end of a refresh time period, new data stored in the history table by the model tuning process is accessed. (E) (B) through (D) are repeated with the accessed data replaced with the accessed new data.

Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration.

Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration.

Abstract: A method of operating a telemetry capture system within a data storage system comprising storage devices is provided. The method includes generating a telemetry packet, and providing the telemetry packet to one or more taps via a telemetry path independent of data and control paths within the storage devices. The method also includes capturing the telemetry packet in one or more of the taps, and generating real-time telemetry data based at least on the telemetry packet.

Abstract: A storage device for adaptive wear leveling within a data storage system is provided. The storage device includes a host interface configured to receive storage operations for storage and retrieval of data on storage media, a media interface configured to read and write data to the storage media, and a storage controller configured to provide wear leveling for the storage media using a plurality of partitions within the storage media.

Abstract: A method of operating a telemetry capture system within a data storage system comprising storage devices is provided. The method includes generating a telemetry packet, and providing the telemetry packet to one or more taps via a telemetry path independent of data and control paths within the storage devices. The method also includes capturing the telemetry packet in one or more of the taps, and generating real-time telemetry data based at least on the telemetry packet.

Abstract: A method of operating a storage controller is provided. The method includes receiving data transferred by a host for storage in a target partition of a storage media, and detecting properties of the data. The method also includes establishing one or more inferred partitions on the storage media based at least on the properties of the data, and based at least on the properties of the data, sorting subsets of the data for storage within the target partition and the one or more inferred partitions.

Abstract: A method of operating a storage controller is provided. The method includes receiving data transferred by a host for storage in a target partition of a storage media, and detecting properties of the data. The method also includes establishing one or more inferred partitions on the storage media based at least on the properties of the data, and based at least on the properties of the data, sorting subsets of the data for storage within the target partition and the one or more inferred partitions.

Abstract: A method, apparatus and computer program product are provided for mobile location based sales lead identification. Sales lead information may be provided based on a reference location, such as a sales resource real time location. Additional location information, demand information, competitive information, activity history, follow-up tasks, sales resource ownership information and past promotions relating to a particular sales lead may be additionally provided. The mobile location based sales lead information may allow a sales resource to efficiently manage their time during and in between sales visits, and prepare effective sales pitches to sales leads.

Abstract: A method of operating a data storage system is provided. The method includes establishing a user region on a non-volatile storage media of the data storage system configured to store user data, and establishing a recovery region on the non-volatile storage media of the data storage system configured to store recovery information pertaining to at least the user region. The method also includes updating the recovery information in the recovery region responsive to at least changes to the user region, and responsive to at least a power interruption of the data storage system, rebuilding at least a portion of the user region using the recovery information retrieved from the recovery region.

Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.

Abstract: A storage system is provided. The storage system includes a workload manager with visibility to host data operations for a storage drive. The workload manager is configured to determine an operation schedule comprising the host data operations and data migration operations for storage media of the storage drive, and instruct a storage media manager to perform the data migration operations and the host data operations in accordance with the operation schedule. The storage system also includes a storage media manager configured to receive instructions from the workload manager in accordance with the operation schedule, and responsively perform the data migration operations and the host data operations.

Abstract: A storage controller is provided. The storage controller includes a host interface, a media interface, and a processing system coupled with the host interface and the media interface. The processing system is configured to configured to maintain a translation table that relates logical addressing to physical blocks of the one or more storage media using at least an extents-based scheme in the translation table to relate the logical addressing to the physical blocks, wherein the extents-based scheme comprises a starting location combined with span length of a sequential portion of data stored on the one or more storage media, and the processing system also configured to handle storage operations of the storage drive in accordance with the translation table.