Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: for each respective record in a plurality of records, assigning the respective record: (1) to a first database cluster on a first database server; or (2) to a second database cluster on a second database server; receiving a search request from a requester; executing the search request in the first database cluster to retrieve a first set of results; when the first set of results is greater than a threshold number of results, presenting the first set of results to the requester; and when the first set of results is less than the minimum number of results: executing the search request in the second database cluster to create a second set of results; and presenting the second set of results to the requester, wherein: the threshold number of results is configured such that a probability that the first set of results is great

Abstract: Various embodiments can comprise a system, which can comprise one or more processing modules; and/or 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: receiving a request from a requesting party to produce a feature vector for a record, the feature vector being used to aid in placing the record in one of a first database cluster H or a second database cluster L; retrieving a first access history for the record at a first time period, the first access history indicating a number of times the record was accessed within the first time period that is a predetermined time period away from a current day; repeating retrieving one or more second access histories for the record for one or more additional time periods, each second access history of the one or more second access histories indicating a number of times the record was accessed within the one or more additional time periods, wherein each of the one or

Abstract: 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.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: for each respective record in a plurality of records, assigning the respective record: (1) to a first database cluster on a first database server; or (2) to a second database cluster on a second database server; receiving a search request from a requester; executing the search request in the first database cluster to retrieve a first set of results; when the first set of results is greater than a threshold number of results, presenting the first set of results to the requester; and when the first set of results is less than the minimum number of results: executing the search request in the second database cluster to create a second set of results; and presenting the second set of results to the requester, wherein: the threshold number of results is configured such that a probability that the first set of results is great

Abstract: Various embodiments include a system that can comprise 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: determining a popularity factor for each record in a plurality of records using historical data that can comprise previous search data for each record in the plurality of records; assigning each record in the plurality of records to a first database cluster H on a first database server when the popularity factor for the record in the plurality of records is greater than a threshold value or to a second database cluster L on a second database server when the popularity factor for the record in the plurality of records is less than the threshold value, wherein the first database cluster H has a greater processing power than the second database cluster L; receiving a search request from a requester; executing the search in the first database cluster H to retrieve a fi

Abstract: Various embodiments can include a system that can comprise: 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.

Abstract: A system and method for integrating business logic into a database system is disclosed herein.

Abstract: Various embodiments include a system for grouping a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L. In many embodiments, the system can comprise 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. In some embodiments, the computer instructions can be configured to perform acts of determining, for each distinct record (i) of the set of distinct records, whether the record is a first priority or a second priority; for each distinct record (i) of the set of distinct records which is determined to be the first priority, storing the record in the first database cluster H, wherein the first database cluster H comprises a first computer server; and for each record (i) of the set of distinct records which is determined to be the second priority, storing the record in the second database cluster L.

Abstract: Various embodiments can comprise a system, which can comprise one or more processing modules; and/or 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: receiving a request from a requesting party to produce a feature vector for a record, the feature vector being used to aid in placing the record in one of a first database cluster H or a second database cluster L; retrieving a first access history for the record at a first time period, the first access history indicating a number of times the record was accessed within the first time period that is a predetermined time period away from a current day; repeating retrieving one or more second access histories for the record for one or more additional time periods, each second access history of the one or more second access histories indicating a number of times the record was accessed within the one or more additional time periods, wherein each of the one or

Abstract: Various embodiments include a system that can comprise 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: determining a popularity factor for each record in a plurality of records using historical data that can comprise previous search data for each record in the plurality of records; assigning each record in the plurality of records to a first database cluster H on a first database server when the popularity factor for the record in the plurality of records is greater than a threshold value or to a second database cluster L on a second database server when the popularity factor for the record in the plurality of records is less than the threshold value, wherein the first database cluster H has a greater processing power than the second database cluster L; receiving a search request from a requester; executing the search in the first database cluster H to retrieve a fi

Abstract: Various embodiments can include a system that can comprise: 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.

Abstract: A system and method for integrating business logic into a database system is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. After records are separated into the first database cluster H and the second database cluster L, the total number of records in the second database cluster L is compared to a target number of records. If there are too many records in the second database cluster L, a randomly generated number is used to randomly move records from second database cluster L into first database cluster H. In a situation where the first database cluster H is faster and accessed more often, such a method results in more products being placed in the faster database cluster and become more likely to become purchased. Other embodiments are also disclosed herein.

Abstract: A system and method for integrating business logic into a database system is disclosed herein.

Abstract: Various embodiments include a system for grouping a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L. In many embodiments, the system can comprise 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. In some embodiments, the computer instructions can be configured to perform acts of determining, for each distinct record (i) of the set of distinct records, whether the record is a first priority or a second priority; for each distinct record (i) of the set of distinct records which is determined to be the first priority, storing the record in the first database cluster H, wherein the first database cluster H comprises a first computer server; and for each record (i) of the set of distinct records which is determined to be the second priority, storing the record in the second database cluster L.

Abstract: Various embodiments can include a system comprising one or more processing modules; a database system that can comprise a first database cluster H and a second database cluster L; and one or more non-transitory memory storage modules storing computing instructions.

Abstract: A system and method for searching databases is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. After receiving a search request from a requester, the search request is executed in the first database cluster H to retrieve a first set of results. If the first set of results is greater than a minimum number of results, then the first set of results are presented to the requester. Otherwise, the second database cluster L is searched to retrieve a second set of results. Then the first set of results and the second set of results are presented to the requester. The first database cluster H is stored on a first database server; and the second database cluster L is stored on a second database server apart from the first database server. Other embodiments are also disclosed herein.

Abstract: A system and method for using past information for future search results is disclosed. A database system may comprise a first database cluster H and a second database cluster L. Social media mentions are mapped to products in the database system. Thereafter, each record in the database system is tracked to determine if mentions on social media platforms are increasing or decreasing, using a social media popularity trend signal. The social media popularity trend signal is created by comparing social media mentions during one time period to social media mentions in another time period. Other embodiments are also disclosed herein.

Abstract: A quitting-intention estimating system facilitates detecting an entity's intention to quit an organization. During operation, the system obtains recent electronic-communication information associated with the entity under investigation, such as an employee of a corporation, or a volunteer of a non-profit organization. The system can determine the entity's quitting intention by determining a plurality of recent communication features from the electronic-communication information, for example, by using a variety of feature-extracting modules that extract the features from the entity's electronic communications. The system then computes a quitting-intention value for the entity under investigation, using a quitting-intention estimating function that takes the recent communication features as input.

Abstract: 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.

Abstract: 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.