Abstract: The systems and methods described herein receive a fitness goal or target (e.g., a user wants to be stronger, or run faster, or bike longer within a certain heart rate zone) as seed input into a guidance system, which generates a personalized plan of recommended classes/activities for the user based on their goal/target. At different points along the plan, the guidance system may modify or update its recommendations with different or enhanced classes/activities, in order to keep the user on their target or goal.

Abstract: Systems and methods that facilitate and/or enable a connected fitness platform to dynamically rank and select rows (or groups) of content to display to a user are described. For example, a platform may dynamically rank and present rows of exercise classes to a user of an exercise machine via a home screen or other portal into the content provided by the platform.

Abstract: A computer-implemented method for detecting a set of inconsistent data records in a database including multiple records, comprises selecting a data quality rule representing a functional dependency for the database, transforming the data quality rule into at least one rule vector with hashed components, selecting a set of attributes of the database, transforming at least one record of the database selected on the basis of the selected attributes into a record vector with hashed components, computing a dot product of the rule and record vectors to generate a measure representing violation of the data quality rule by the record.

Abstract: A computer-implemented method for detecting a set of inconsistent data records in a database including multiple records, comprises selecting a data quality rule representing a functional dependency for the database, transforming the data quality rule into at least one rule vector with hashed components, selecting a set of attributes of the database, transforming at least one record of the database selected on the basis of the selected attributes into a record vector with hashed components, computing a dot product of the rule and record vectors to generate a measure representing violation of the data quality rule by the record.