Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: An apparatus, method, and computer program product are provided to adjust and modify input signals used in connection with predictive models by detecting events, such as changes in operating parameters of data objects and/or related systems and calculating adjusted decay rates to be applied to time-series data associated with times prior to an occurrence of an event. In some example implementations, an indication of an event associated with a given datastream is received, in a manner which indicates the change in an operating parameter and the time at which the change occurred. Based at least in part on the indication of the event associated with the datastream, a second decay rate associated with the set of time-series data is determined and applied to the set of time-series data, such that an updated future performance level can be calculated by a predictive model.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: A method, computer program product, and apparatus for determining improved data objects are provided. An example method receives a request for a renderable data object from a location source device associated with a user profile including instant location data. The method queries an explicit locations database and an implicit locations database and coranks this multisource locations data. The method compares the coranked locations with one or more object redemption locations of each renderable data object. The method determines a proximity score for each renderable data object and ranks each renderable data object based upon proximity score. The method then identifies a proximate data object having a minimum proximity score and transmits the proximate data object to the location source device.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: A method, computer program product, and apparatus for determining improved data objects are provided. An example method receives a request for a renderable data object from a location source device associated with a user profile including instant location data. The method queries an explicit locations database and an implicit locations database and coranks this multisource locations data. The method compares the coranked locations with one or more object redemption locations of each renderable data object. The method determines a proximity score for each renderable data object and ranks each renderable data object based upon proximity score. The method then identifies a proximate data object having a minimum proximity score and transmits the proximate data object to the location source device.