A system and method for predicting search term popularity is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. A machine learning algorithm is trained to create a predictive model. Thereafter, for each record in a database system, the predictive model is used to calculate a probability of the record being accessed. If the calculated probability of the record being accessed is greater than a threshold value, then the record in the first database cluster H; otherwise, the record is placed in the second database cluster L. Training the machine learning algorithm comprises inputting a training feature vector associated with the record into the machine learning algorithm, inputting a cost vector into the machine learning algorithm, and iteratively operating the machine learning algorithm on each record in the set of records to create a predictive model. Other embodiments are also disclosed herein.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more processing modules; and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: for each record in a set of distinct records in a database system: inputting a training feature vector associated with the record into a machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; and inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to train the machine learning algorithm to create a predictive model. 2. The system of claim 1, wherein: the database system comprises a first database cluster H and a second database cluster L; and the one or more non-transitory memory storage modules storing the computing instructions are further configured to run on the one or more processing modules and perform acts of: for each record of the set of distinct records: using the predictive model to calculate a probability of the record being accessed; if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in the first database cluster H; and if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in the second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request. 3. The system of claim 2, wherein: the threshold value is determined such that at least approximately 99 percent of predicted accesses will access records of the set of distinct records placed in the first database cluster H; and using the predictive model to calculate the probability of the record being accessed comprises: for each record in the set of distinct records: inputting a list of prediction feature vectors, each prediction feature vector of the list of prediction feature vectors comprising the list of characteristics of the record; and using the predictive model to analyze the list of prediction feature vectors to calculate the probability of the record being accessed. 4. The system of claim 1, wherein the machine learning algorithm comprises at least one of: a decision tree, a bagging technique, a logistic regression, a perceptron, a support vector machine, or a relevance vector machine. 5. The system of claim 1, wherein iteratively operating the machine learning algorithm to train the machine learning algorithm to create the predictive model comprises: operating the machine learning algorithm on a periodic basis; and for each record of the set of distinct records: reviewing historical access data associated with the record; and comparing a probability of the record being accessed as calculated with the historical access data associated with the record. 6. The system of claim 1, wherein: the training feature vector further comprises a label configured to indicate, for each record in the set of distinct records, if the record has been accessed within a pre-defined time period; and the cost vector associated with the record represents an estimate of cost incurred when the record is placed in an incorrect database cluster of the first database cluster H or the second database cluster L. 7. A method implemented via execution of computer instructions configured to run on one or more processing modules and configured to be stored on one or more non-transitory memory storage modules, the method comprising: for each record in a set of distinct records in a database system: inputting a training feature vector associated with the record into a machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; and inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to train the machine learning algorithm to create a predictive model. 8. The method of claim 7, wherein: the database system comprises a first database cluster H and a second database cluster L; the method further comprises: for each record of the set of distinct records: using the predictive model to calculate a probability of the record being accessed; if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in the first database cluster H; and if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in the second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request. 9. The method of claim 8, wherein: the threshold value is determined such that at least approximately 99 percent of predicted accesses will access records of the set of distinct records placed in the first database cluster H; and using the predictive model to calculate the probability of the record being accessed comprises: for each record in the set of distinct records: inputting a list of prediction feature vectors, each prediction feature vector of the list of prediction feature vectors comprising the list of characteristics of the record; and using the predictive model to analyze the list of prediction feature vectors to calculate the probability of the record being accessed. 10. The method of claim 7, wherein the machine learning algorithm comprises at least one of: a decision tree, a bagging technique, a logistic regression, a perceptron, a support vector machine, or a relevance vector machine. 11. The method of claim 7, wherein iteratively operating the machine learning algorithm to train the machine learning algorithm to create the predictive model comprises: operating the machine learning algorithm on a periodic basis; and for each record of the set of distinct records: reviewing historical access data associated with the record; and comparing a probability of the record being accessed as calculated with the historical access data associated with the record. 12. The method of claim 7, wherein: the training feature vector further comprises a label configured to indicate, for each record in the set of distinct records, if the record has been accessed within a pre-defined time period; and the cost vector associated with the record represents an estimate of cost incurred when the record is placed in an incorrect database cluster of the first database cluster H or the second database cluster L. 13. A system comprising: one or more processing modules; and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a machine learning algorithm to create a predictive model; receiving, from a requesting party, a request to analyze a probability that a record of a database will be requested within a predetermined time period; retrieving a feature vector corresponding to the record; calculating a prediction of the probability that the record will be requested within the predetermined time period, the prediction being based on the predictive model used in conjunction with the feature vector; and presenting, to the requesting user, the prediction of the probability, as calculated. 14. The system of claim 13, wherein training the machine learning algorithm to create the predictive model comprises: for each record in a set of distinct records in the database: inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector comprising a list of characteristics of the record; and inputting a cost vector associated with the record into the machine learning algorithm, the cost vector configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of records to create the predictive model. 15. The system of claim 14, wherein the machine learning algorithm uses a MetaCost algorithm and a cost-insensitive machine learning algorithm used in conjunction with the MetaCost algorithm. 16. The system of claim 13, wherein the feature vector corresponding to the record comprises a list of characteristics of the record. 17. A method implemented via execution of computer instructions configured to run on one or more processing modules and configured to be stored on one or more non-transitory memory storage modules, the method comprising: training a machine learning algorithm to create a predictive model; receiving, from a requesting party, a request to analyze a probability that a record of a database will be requested within a predetermined time period; retrieving a feature vector corresponding to the record; calculating a prediction of the probability that the record will be requested within the predetermined time period, the prediction being based on the predictive model used in conjunction with the feature vector; and presenting, to the requesting user, the prediction of the probability, as calculated. 18. The method of claim 17, wherein training the machine learning algorithm to create the predictive model comprises: for each record in a set of distinct records in the database: inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector comprising a list of characteristics of the record; and inputting a cost vector associated with the record into the machine learning algorithm, the cost vector configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of records to create the predictive model. 19. The method of claim 118, wherein the machine learning algorithm uses a MetaCost algorithm and a cost-insensitive machine learning algorithm used in conjunction with the MetaCost algorithm. 20. The method of claim 17, wherein the feature vector corresponding to the record comprises a list of characteristics of the record.