Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: A system, method, and computer-program product includes receiving an input comprising a plurality of pre-defined factor matrices and an implicit feedback dataset partitioned into a plurality of implicit feedback data subsets; distributing the input across a controller node and a plurality of worker nodes implemented in a distributed computing environment; and training a model using the controller node and the plurality of worker nodes, wherein training the model includes: initializing, by the controller node, a controller-specific user parameters matrix and a controller-specific item parameters matrix, broadcasting, by the controller node, the controller-specific user parameters matrix and the controller-specific item parameters matrix to each worker node of the plurality of worker nodes, and concurrently executing an aggregation model training algorithm at the controller node and a plurality of localized model training algorithms across the plurality of worker nodes until a training termination condition is

Abstract: In one example, a system can receive information about a data structure including a set of data entries. The system can generate a proxy data table including a set of columns. The system can use a data access layer to generate a mapping from the data entries to the columns. The system can receive an input to cause an operation to be performed on the data structure by performing the operation on the data structure. Generating a result can involve issuing read commands to the data access layer to perform the operation on the data structure such that the data access layer obtains the associated data entries and provides them as responses to the read commands by performing a translation between the data entries and the columns based on the mapping. The system can then output the result of the operation.

Abstract: Parameter values in source code can be automatically validated using the techniques described herein. For example, a system can receive source code that includes a call to an action. The action can have a parameter that is set to a selected value in the source code. The parameter can be defined in definition data. The system can also receive a file that separate from the source code and includes metadata for the parameter. The system can extract the metadata from the file and modify the definition data to include the metadata. The system can then execute a validation process on the selected value for the parameter. The validation process can involve retrieving the metadata from the modified definition data, evaluating the selected value using the metadata to determine whether the selected value is invalid, and if it is invalid, outputting an error notification indicating that the selected value is invalid.

Abstract: A method and controller for operating a memory system in communication with a host. The method and controller logically arrange a sequence of reclaim sub-groups within a memory device. The method and controller process the reclaim sub-groups according to the sequence to control the memory device to perform garbage collection on the reclaim sub-groups in the memory device. In the sequence, the reclaim sub-groups are processed during the garbage collection such that at least one re-ordered data sequence in the sequence of the reclaim sub-groups being processed has re-ordered valid data that is not clumped.

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 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.