FIELD OF THE INVENTION

The invention relates to classifying user files in a database into states by introducing time dependency into time-homogeneous probability models.

BACKGROUND

In recent years, the amount and types of data handled by data processing networks has been exponentially increasing. The efficiency of data processing, particularly in parallel processing environments, for exponentially increasing dataset sizes, using cloud distributed computing architectures, is fundamentally limited by the efficiency of the algorithm used to partition that data for distributed storage and processing. For example, properly classifying database records allows for more efficient balancing of domain loads (e.g., by distributing network resources accordingly) by ensuring that each domain is capable of processing its load. Moreover, minimizing the number of network domains may also reduce the chances for network and routing contentions as well as the errors related to misclassification. However, as the number of network domains decreases, the need for properly classifying data into its respective domains increases.

SUMMARY

Methods and systems are described herein for improving data processing efficiency of classifying user files in a database. More particularly, methods and systems are described herein for improving data processing efficiency of classifying user files in a database in which the user files have a temporal element. The methods and system described herein accomplish these improvements by introducing time dependency into time-homogeneous probability models. Once time dependency has been introduced into the time-homogeneous probability models, these models may be used to improve the data processing efficiency of classifying the user files that feature a temporal element.

For example, the methods and systems described herein may use probabilistic forecasting to determine an estimation of the respective probabilities that a given user file may be classified into a given state. To perform the probabilistic forecasting, the methods and systems may use a time-homogeneous stochastic model with the Markov property (e.g., a Markov chain), which describes a sequence of possible events in which the probability of each future event depends only on the current state irrespective of time. Conventional systems apply time-homogeneous stochastic models to datasets in which temporally based trends affect the respective probabilities that the given user file may be classified into the given state. For example, datasets in which the number of, and results of, a series of status checks on a user file may affect the respective probabilities that the given user file may be classified into the given state are ill-suited for time-homogeneous stochastic models. That is, conventional systems do not allow for this time-inhomogeneity in their models.

The methods and systems described herein overcome the technical problems of conventional systems in applying stochastic models to datasets in which the number of, and results of, a series of status checks on a user file may affect the respective probabilities that the given user file may be classified into the given state. Specifically, the methods and systems described herein introduce a time dependency into time-homogeneous probability models. That is, the methods and systems described a time-inhomogeneous Markov chain. Moreover, the manner in which the time dependency is introduced is dependent on criteria related to the user files to be classified, and the states into which the user files are to be classified. For example, as described herein, the methods and systems may introduce the time dependency into the stochastic models by retrieving a user file history for a user file, wherein the user file history comprises periodic status checks. The system may then generate a feature input for the user file history based on a respective user file status at each of the periodic status checks. The system may then determine a number of periodic status checks in the user file history. The system may then select a first stochastic probability model of a plurality of stochastic probability models based on whether or not the number of the periodic status checks in the user file history correspond to a first range. In such a case, the first range may be selected based on criteria related to the user files to be classified, and the states into which the user files are to be classified.

For example, if the states relate to a cycle or series of events, the system may select the range based on whether or not the total number of states in the cycle or series has been met. If the number has been met, the system may select a first stochastic model, whereas if the number has not been met, the system may select a second stochastic model. For example, if the total number of states is ten, the system may select a first stochastic model if the total number of periodic status checks is eleven or more. Likewise, if the total number of periodic status checks is six to ten, the system may select a second stochastic model. Finally, if the total number of periodic status checks is under six, the system may apply a third stochastic model (or apply another model for determining an output).

In addition to selecting the range based on whether or not the total number of states in the cycle or series has been met, the system may select the range based on criteria related to the result of the status check. For example, the system may retrieve a result for the status check and select a model based on the result. Accordingly, the system may determine trend data and apply the stochastic models to the trend data. For example, the methods and systems described herein are particularly efficient in classifying user files in a database into states by introducing time dependency into time-homogeneous probability models when the user files may include user files exhibiting “straight-roller” characteristics. For example, a straight-roller account describes a delinquent user files that spirals downward over the course of periodic status checks from an initial state (e.g., upon creation of the file) to a delinquent state (e.g., ultimately ending with the closing of the file) with no attempt to address the downward spiral. For example, a straight-roller account may correspond to an account with a diabolical nature as described inFIG. 2below. By selecting the range based on whether or not the total number of states in the cycle or series has been met, and based on criteria related to the result of the status check, the system may exclude straight-roller files from affecting the classification of user files in a database.

In some aspects, methods and systems of improving data processing efficiency of classifying user files in a database into disparate states by introducing time dependency into time-homogeneous probability models are described. For example, the system may receive a request to determine respective probabilities that a user record for a user belongs to each of a plurality of user record states, wherein the plurality of user record states comprises user record states defined by a time period for which the user record has been delinquent. The system may then retrieve a user record history for the user record, wherein the user record history comprises periodic status checks. The system may then generate a feature input for the user record history based on a respective user record status at each of the periodic status checks. The system may then determine a number of the periodic status checks in the user record history. The system may then select a first stochastic probability model of a plurality of stochastic probability models based on the number of the periodic status checks in the user record history corresponding to a first range. The system may then process the feature input using the first stochastic probability model. The system may receive a first output of the first stochastic probability model indicating the respective probabilities that the user record for the user belongs to each of the plurality of user record states. The system may then generate a recommendation based on the first output.

Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples and not restrictive of the scope of the invention. As used in the specification, and in the claims, the singular forms of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. Additionally, as used in the specification, “a portion” refers to a part of, or the entirety of (i.e., the entire portion), a given item (e.g., data) unless the context clearly dictates otherwise.

