Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: Access to personal information is restricted when analyzing errors in computer programs. Variables are named with prefixes to identify data that must be transformed before disclosure. If a variable has a name that requires transformation, the associated data must be transformed before resolving an error.

Abstract: A graphical data type inference transparently transforms a legacy text-oriented command line interface (CLI) into a graphic-oriented or graphic-aware CLI (G-CLI). A user fuses graphical references with the legacy scripting language by identifying objects using syntax familiar to the user. The objects are presented to the user via a user interface and may be queried/selected by the user. The user issues commands on the selected objects by referencing the graphical selection in a command line within a shell. The graphical selection in short hand is translated into a form operable by a server or computer. The syntax of the legacy command language is extended with operators that operate entirely within the G-CLI. Consequently, the command line used in the shell has an existing operation set, as well as new graphic-based operations.

Abstract: A graphical data type inference transparently transforms a legacy text-oriented command line interface (CLI) into a graphic-oriented or graphic-aware CLI (G-CLI). A user fuses graphical references with the legacy scripting language by identifying objects using syntax familiar to the user. The objects are presented to the user via a user interface and may be queried/selected by the user. The user issues commands on the selected objects by referencing the graphical selection in a command line within a shell. The graphical selection in short hand is translated into a form operable by a server or computer. The syntax of the legacy command language is extended with operators that operate entirely within the G-CLI. Consequently, the command line used in the shell has an existing operation set, as well as new graphic-based operations.

Abstract: Methods, systems, and products secure access to a file system. A directory is established in a hierarchical file structure having access permission defined by a first owner. A subdirectory is established in the directory. A sub-level subdirectory is established in the subdirectory having access permissions defined by a second owner. The subdirectory is publically accessible to anyone satisfying the access permission defined by the first owner, such that a change directory system call is executed for a user in the subdirectory, even though the user has not authenticated the access permission defined by the second owner.

Abstract: Methods, systems, and products are disclosed for securing access to a file system. A directory is established in a hierarchical file structure having access permission defined by a first owner. A subdirectory is established in the directory. A sub-level subdirectory is established in the subdirectory having access permissions defined by a second owner. The subdirectory is publically accessible to anyone satisfying the access permission defined by the first owner, such that a change directory system call is executed for a user in the subdirectory, even though the user has not authenticated the access permission defined by the second owner.

Abstract: Access to personal information is restricted when analyzing errors in computer programs. Variables are named with prefixes to identify data that must be transformed before disclosure. If a variable has a name that requires transformation, the associated data must be transformed before resolving an error.

Abstract: A method of analyzing an occurrence of an error in a computer program executing on a data processing system includes receiving data that are associated with an execution leg of the computer program at the time of the error and restricting access to at least a portion of the data associated with the execution leg of the computer program based on an identification of the portion of the data associated with the execution leg of the computer program as being sensitive information.

Abstract: Database table aggregation is implemented by a method that includes encoding first and second components via a waveform definition, the first and second components specifying criteria for aggregating data, and the first component is defined to provide data aggregation at a higher granularity than the second component. The method also includes generating a complex waveform representing the components in a pattern corresponding to the waveform definition and criteria, which spans a first axis indicative of a time period for the aggregation. The method includes providing a byte count for aggregated data stored in a first table defined by the criteria, aggregating the data for the time period, and updating the complex waveform to reflect results of the aggregation that includes generating a waveform representing a second table along a second axis depicted at a location corresponding to a time in which the data aggregation for the first table was initiated.

Abstract: Database table aggregation is implemented by a method that includes encoding first and second components via a waveform definition, the first and second components specifying criteria for aggregating data, and the first component is defined to provide data aggregation at a higher granularity than the second component. The method also includes generating a complex waveform representing the components in a pattern corresponding to the waveform definition and criteria, which spans a first axis indicative of a time period for the aggregation. The method includes providing a byte count for aggregated data stored in a first table defined by the criteria, aggregating the data for the time period, and updating the complex waveform to reflect results of the aggregation that includes generating a waveform representing a second table along a second axis depicted at a location corresponding to a time in which the data aggregation for the first table was initiated.

Abstract: A method and system for monitoring one or more wireless channels of an Radio Frequency Identification (RFID) network are disclosed. For example, the method reads one or more RFID reference tags periodically to obtain RFID reference tag reading results for each time interval. The method then processes the RFID reference tag reading results for monitoring the one or more wireless channels of the RFID network.

Abstract: Database table aggregation is implemented by a method that includes encoding first and second components via a waveform definition, the first and second components specifying criteria for aggregating data. The method also includes generating a complex waveform representing the components in a pattern corresponding to the waveform definition and criteria, which spans a first axis indicative of a time period for aggregating the data. The method further includes providing a byte count for aggregated data stored in a first table defined by the criteria, aggregating the data for the time period, and creating a second table for overflow data when the count is exceeded. The method also includes updating the complex waveform to reflect results of the aggregation that includes generating a waveform representing the second table along a second axis depicted at a location corresponding to a time in which the data aggregation for the first table was initiated.

Abstract: Database table aggregation is implemented by a method that includes encoding first and second components via a waveform definition, the first and second components specifying criteria for aggregating data. The method also includes generating a complex waveform representing the components in a pattern corresponding to the waveform definition and criteria, which spans a first axis indicative of a time period for aggregating the data. The method further includes providing a byte count for aggregated data stored in a first table defined by the criteria, aggregating the data for the time period, and creating a second table for overflow data when the count is exceeded. The method also includes updating the complex waveform to reflect results of the aggregation that includes generating a waveform representing the second table along a second axis depicted at a location corresponding to a time in which the data aggregation for the first table was initiated.

Abstract: Methods, systems, and computer program products for database table aggregation are provided. A method includes encoding first and second components via a waveform definition, the first and second components specifying first and second criteria, respectively, for aggregating data. The method includes generating a complex periodic aggregation waveform (CPAW) having variable-sized square waves representing the components in a repeating pattern corresponding to the definition and the criteria, and which spans a first axis. The method includes providing a maximum byte count for aggregated data stored in a table defined by the first and/or second criteria, aggregating the data in accordance with the criteria, and creating a new table for overflow of data determined for the table when the maximum byte count is exceeded. The method includes updating the CPAW with results of the aggregation and generating a waveform representing the new table along a second axis.