Abstract: Aspects of the present disclosure address systems, methods, and devices for tracking object dependencies in a cloud database system. An object dependency created between a referencing object and a referenced object is detected. Based on detecting the object dependency, a dependency record is generated. The dependency record includes dependency information describing the object dependency between the reference object and the referenced object. The dependency record is stored in a database of dependency records.

Abstract: Provided is a system, method, and computer program product for adjusting a model. The method includes training or receiving a model based on transaction data for a plurality of account holders, the model configured to classify each individual of the plurality of account holders, segmenting each individual of at least a portion of account holders of the plurality of account holders into at least one subgroup of a plurality of subgroups, separately executing the model for each subgroup of the plurality of subgroups by inputting, into the model, a portion of the transaction data associated with individuals in each subgroup, comparing a plurality of outputs of the model resulting from the separate execution of the model for each subgroup of the plurality of subgroups, and adjusting the model based on at least one differential between the plurality of outputs.

Abstract: Various embodiments provide for tag-based application of a masking policy, which can be used in connection with a data platform. In particular, various embodiments enable enforcement of one or more masking policies against an entity (e.g., object) of a data platform, such as a database, a table, a row, or a column, based on one or more tags associated with the entity.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: Aspects of the present disclosure address systems, methods, and devices for tracking object dependencies in a cloud database system. An object dependency created between a referencing object and a referenced object is detected. Based on detecting the object dependency, a dependency record is generated. The dependency record includes dependency information describing the object dependency between the reference object and the referenced object. The dependency record is stored in a database of dependency records.

Abstract: Capturing dependencies between variables using a variable agnostic object is disclosed. A system is configured to obtain an indication of a first dependency of a first variable to a second variable via a programming interface and depict the first dependency, the first variable, and the second variable in a first instance of a variable agnostic object in a source code. The system is also configured to obtain an indication of a second dependency of a third variable to a fourth variable via the programming interface and depict the second dependency, the third variable, and the fourth variable in a second instance of the variable agnostic object in the source code. The system is also configured to compile the source code to generate a computer-executable program capturing the first dependency and the second dependency based on the first instance and the second instance of the variable agnostic object.

Abstract: Described are a system, method, and computer program product for generating a synthetic control group. The method includes receiving transaction account data and transaction data associated with transactions completed by a first set of transaction accounts with a target merchant. The method also includes generating a synthetic control group including a subset of transaction accounts sampled from the first set of transaction accounts. The method further includes determining, for each transaction account of the synthetic control group, a propensity score. The method further includes assigning an entropy balancing weight to each transaction account of the synthetic control group. The method further includes altering, based on the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed.

Abstract: Described are a system, method, and computer program product for generating a synthetic control group. The method includes receiving transaction account data and transaction data associated with transactions completed by a first set of transaction accounts with a target merchant. The method also includes generating a synthetic control group including a subset of transaction accounts sampled from the first set of transaction accounts. The method further includes determining, for each transaction account of the synthetic control group, a propensity score. The method further includes assigning an entropy balancing weight to each transaction account of the synthetic control group. The method further includes altering, based on the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed.

Abstract: Described are a system, method, and computer program product for generating a synthetic control group. The method includes receiving transaction account data and transaction data associated with transactions completed by a first set of transaction accounts with a target merchant. The method also includes generating a synthetic control group including a subset of transaction accounts sampled from the first set of transaction accounts. The method further includes determining, for each transaction account of the synthetic control group, a propensity score. The method further includes assigning an entropy balancing weight to each transaction account of the synthetic control group. The method further includes altering, based on the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: In one embodiment, a method includes providing a request to store at least a first piece of data. The request to store the first piece of data is a request to store the first piece of data in a first encrypted form on a cloud associated with the cloud application provider. The method also includes determining whether the cloud application provider is capable of encrypting the first piece of data, and providing the first piece of data to the cloud application provider if it is determined that the cloud application provider is capable of encrypting the first piece of data. If it is determined that the cloud application provider is not capable of encrypting the first piece of data, the method further includes encrypting the first piece of data to create the first encrypted form and providing the first encrypted form to the cloud application provider.

Abstract: In particular embodiments, a method includes, from an indexer in a sensor network, accessing a set of sensor data that includes sensor data aggregated together from sensors in the sensor network, one or more time stamps for the sensor data, and metadata for the sensor data identifying one or more pre-determined attributes of the sensor data. The method includes, at the indexer, generating an index of the set of sensor data according to a multi-dimensional array configured for querying of the set of sensor data along with a plurality of other sets of sensor data. One or more first ones of the dimensions of the multi-dimensional array include time, and one or more second ones of the dimensions of the multi-dimensional array include one or more of the pre-determined sensor-data attributes. The method includes, from the indexer, communicating the index of the set of sensor data for use in responding to one or more queries of the set of sensor data along with a plurality of other sets of sensor data.

Abstract: In particular embodiments, a method includes accessing sensor data from sensor nodes in a sensor network and aggregating the sensor data for communication to an indexer in the sensor network. The aggregation of the sensor data includes deduplicating the sensor data; validating the sensor data; formatting the sensor; generating metadata for the sensor data; and time-stamping the sensor data. The metadata identifies one or more pre-determined attributes of the sensor data. The method also includes communicating the aggregated sensor data to the indexer in the sensor network. The indexer is configured to index the aggregated sensor data according to a multi-dimensional array for querying of the aggregated sensor data along with other aggregated sensor data. One or more first ones of the dimensions of the multi-dimensional array include time and one or more second ones of the dimensions of the multi-dimensional include one or more of the pre-determined sensor-data attributes.

Abstract: In one embodiment, a method includes providing a request to store at least a first piece of data. The request to store the first piece of data is a request to store the first piece of data in a first encrypted form on a cloud associated with the cloud application provider. The method also includes determining whether the cloud application provider is capable of encrypting the first piece of data, and providing the first piece of data to the cloud application provider if it is determined that the cloud application provider is capable of encrypting the first piece of data. If it is determined that the cloud application provider is not capable of encrypting the first piece of data, the method further includes encrypting the first piece of data to create the first encrypted form and providing the first encrypted form to the cloud application provider.

Abstract: In particular embodiments, a method includes receiving a query for particular sensor data among multiple sensor data from multiple sensors. The plurality of sensor data has been indexed according to a multi-dimensional array. One or more first ones of the dimensions include time, and one or more second ones of the dimensions include one or more pre-determined sensor-data attributes. The method includes translating the query to correspond to the indexing of the plurality of sensor data. The translated query includes one or more values for one or more of the dimensions of the multi-dimensional array. The method includes communicating the translated query to search among the plurality of sensor data according to its indexing to identify the particular sensor data.

Abstract: In particular embodiments, a method includes receiving a query for particular sensor data among multiple sensor data from multiple sensors. The plurality of sensor data has been indexed according to a multi-dimensional array. One or more first ones of the dimensions include time, and one or more second ones of the dimensions include one or more pre-determined sensor-data attributes. The method includes translating the query to correspond to the indexing of the plurality of sensor data. The translated query includes one or more values for one or more of the dimensions of the multi-dimensional array. The method includes communicating the translated query to search among the plurality of sensor data according to its indexing to identify the particular sensor data.

Abstract: In particular embodiments, a system includes a sensor-data-collection network layer including multiple sensors. The sensor-data-collection network layer is a first logical layer of a sensor network. The system includes an aggregation network layer including one or more aggregators configured to access sensor data from the sensors and aggregate the sensor data. The aggregation network layer is a second logical layer residing logically above the first logical layer. The system includes an indexing network layer including one or more indexers that are configured to access the aggregated sensor data and generate an index of the aggregated sensor data according to a multi-dimensional array. The indexing network layer is a third logical layer residing logically above the second logical layer. The system includes a search network layer including one or more search engines. The search network layer is a fourth logical layer residing logically above the third logical layer.

Abstract: In particular embodiments, a method includes, from an indexer in a sensor network, accessing a set of sensor data that includes sensor data aggregated together from sensors in the sensor network, one or more time stamps for the sensor data, and metadata for the sensor data identifying one or more pre-determined attributes of the sensor data. The method includes, at the indexer, generating an index of the set of sensor data according to a multi-dimensional array configured for querying of the set of sensor data along with a plurality of other sets of sensor data. One or more first ones of the dimensions of the multi-dimensional array include time, and one or more second ones of the dimensions of the multi-dimensional array include one or more of the pre-determined sensor-data attributes. The method includes, from the indexer, communicating the index of the set of sensor data for use in responding to one or more queries of the set of sensor data along with a plurality of other sets of sensor data.