Abstract: A drive shaft includes a male cylindrical body, a first splined section, and a second splined section. The first section is arranged coaxially around the male cylindrical body and has first longitudinal splines formed by alternating first teeth and first grooves. The first section is rotationally secured to the male cylindrical body. The second splined section is arranged coaxially around the male cylindrical body and has second longitudinal splines formed by alternating second teeth and second grooves. The second section is biased by an elastic element from a first position to a second position in which the second teeth are angularly offset relative to the first teeth.

Abstract: Systems and methods of the present disclosure enable anomaly detection based on data lineage by using at least one processor to receive initial lineage records that record initial changes in data items at each data processor of a lineage stream. The processor(s) generates a historical distribution as a data processor baseline for a particular data processor based on each change in each data item at the particular data processor. For lineage records in a subsequent time period, the processor(s) identifies subsequent changes in data items at the particular data processor and generates a real-time dynamic distribution to represent a current data processor behavior for the particular data processor based on each change for the subsequent lineage records. The processor(s) determines a deviation between the data processor baseline and the current data processor behavior based on the historical distribution and the real-time dynamic distribution to identify a data processor anomaly.

Abstract: Systems and methods of the present disclosure enable anomaly detection based on data lineage by using at least one processor to receive initial lineage records that record initial changes in data items at each data processor of a lineage stream. The processor(s) generates a historical distribution as a data processor baseline for a particular data processor based on each change in each data item at the particular data processor. For lineage records in a subsequent time period, the processor(s) identifies subsequent changes in data items at the particular data processor and generates a real-time dynamic distribution to represent a current data processor behavior for the particular data processor based on each change for the subsequent lineage records. The processor(s) determines a deviation between the data processor baseline and the current data processor behavior based on the historical distribution and the real-time dynamic distribution to identify a data processor anomaly.

Abstract: In certain embodiments, machine learning and lineage data may be used to manage data. In some embodiments, a computing system may use lineage data to identify two datasets that may be related. The computing system may determine that a user has access to a derivative dataset but does not have access to an original dataset that was used to create the derivative dataset. In response, the computing system may use a machine learning model to generate a similarity score indicating a level of similarity between the original dataset and the derivative dataset. If the similarity score satisfies a threshold score, the computing system may modify access rights of the user so that the user is unable to access a portion of the data in the derivative dataset.

Abstract: In certain embodiments, machine learning and lineage data may be used to manage data. In some embodiments, a computing system may use lineage data to identify two datasets that may be related. The computing system may determine that a user has access to a derivative dataset but does not have access to an original dataset that was used to create the derivative dataset. In response, the computing system may use a machine learning model to generate a similarity score indicating a level of similarity between the original dataset and the derivative dataset. If the similarity score satisfies a threshold score, the computing system may modify access rights of the user so that the user is unable to access a portion of the data in the derivative dataset.

Abstract: In certain embodiments, machine learning and lineage data may be used to manage data. In some embodiments, a computing system may use lineage data to identify two datasets that may be related. The computing system may determine that a user has access to a derivative dataset but does not have access to an original dataset that was used to create the derivative dataset. In response, the computing system may use a machine learning model to generate a similarity score indicating a level of similarity between the original dataset and the derivative dataset. If the similarity score satisfies a threshold score, the computing system may modify access rights of the user so that the user is unable to access a portion of the data in the derivative dataset.

Abstract: In certain embodiments, machine learning and lineage data may be used to manage data. In some embodiments, a computing system may use lineage data to identify two datasets that may be related. The computing system may determine that a user has access to a derivative dataset but does not have access to an original dataset that was used to create the derivative dataset. In response, the computing system may use a machine learning model to generate a similarity score indicating a level of similarity between the original dataset and the derivative dataset. If the similarity score satisfies a threshold score, the computing system may modify access rights of the user so that the user is unable to access a portion of the data in the derivative dataset.

Abstract: A method for causing GSM location information and UMA billing information to be encapsulated in a single CGI structural element during a standalone UMA network access session comprises a plurality of operations. An operation is provided for applying at least one algorithm to location information of a Mobile Station (MS) serving a subscriber device during a standalone UMA network access session for causing at least one of compressing, re-aligning and repackaging the MS location information whereby an amount of MS location information holding space of a GSM Edge Radio Access Network (GERAN) Cell Global Identity (CGI) structural element occupied by the MS location information is reduced such that a portion of the MS location information holding space becomes unused. An operation is performed for inserting UMA billing information into at least a portion of the unused portion of the MS location information holding space.

Abstract: A method for causing GSM location information and UMA billing information to be encapsulated in a single CGI structural element during a standalone UMA network access session comprises a plurality of operations. An operation is provided for applying at least one algorithm to location information of a Mobile Station (MS) serving a subscriber device during a standalone UMA network access session for causing at least one of compressing, re-aligning and repackaging the MS location information whereby an amount of MS location information holding space of a GSM Edge Radio Access Network (GERAN) Cell Global Identity (CGI) structural element occupied by the MS location information is reduced such that a portion of the MS location information holding space becomes unused. An operation is performed for inserting UMA billing information into at least a portion of the unused portion of the MS location information holding space.