Abstract: A specially programmed machine and method generates propensity information based on inputs received from machines connected as part of an Internet of Things (IoT) environment, where the machine appends a predictive segmentation attribute to each data input received and generates a matrix based on the counts of each attribute-input combination. The attribute-input counts for each combination are converted to a statistical metric that represents propensity information for consumers fitting into the segment associated with the particular attribute. The propensity information can be relayed to a client in a variety of ways, including a direct display of the propensity information, appendage of the propensity information to the clients database, or using the information to provide an audience report to the client.

Abstract: A specially programmed machine and method generates propensity information based on inputs received from machines connected as part of an Internet of Things (IoT) environment, where the machine appends a predictive segmentation attribute to each data input received and generates a matrix based on the counts of each attribute-input combination. The attribute-input counts for each combination are converted to a statistical metric that represents propensity information for consumers fitting into the segment associated with the particular attribute. The propensity information can be relayed to a client in a variety of ways, including a direct display of the propensity information, appendage of the propensity information to the clients database, or using the information to provide an audience report to the client.

Abstract: A method for measuring a change between two values for one or more data elements over time uses a “delta” value to indicate the significance of the change. The delta value is the difference between the two values, added to the ratio between those values multiplied by an asymptote. Experimental data sets with known values may be used to create data sets to which selection criteria are applied in order to calculate an optimal asymptote for the application of the delta values. This optimal asymptote may then be used for calculating delta values across an entire population for predictive purposes.

Abstract: A method for constructing a change database reduces the footprint of a standard longitudinal data database and increases the manageability of the data for many applications. The change database concatenates representations of a time period for the change, the direction of the change, and the value of the change into a single string. The change database is constructed by creating foundation files from formatted historical data, constructing change files from these foundation files, and iteratively creating master files that encapsulate the change data.

Abstract: A method for constructing a change database reduces the footprint of a standard longitudinal data database and increases the manageability of the data for many applications. The change database concatenates representations of a time period for the change, the direction of the change, and the value of the change into a single string. The change database is constructed by creating foundation files from formatted historical data, constructing change files from these foundation files, and iteratively creating master files that encapsulate the change data.

Abstract: A method for measuring a change between two values for one or more data elements over time uses a “delta” value to indicate the significance of the change. The delta value is the difference between the two values, added to the ratio between those values multiplied by an asymptote. Experimental data sets with known values may be used to create data sets to which selection criteria are applied in order to calculate an optimal asymptote for the application of the delta values. This optimal asymptote may then be used for calculating delta values across an entire population for predictive purposes.