Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Automatically mapping and combining the application of machine learning models to answer queries according to semantic specification. A query is parsed to extract keywords from the query and to contextualize the query. Based on the keywords, machine learning models are selected that process concepts associated with the keywords. The machine learning models are sorted according to the contextualization of the query. The machine learning models are run on multimodal data according to a sorted order, where data resulting from an output of one of the machine learning models is used as input to another one of the machine learning models. A query result is output based on a result from running the machine learning models.

Computer-readable media, methods, and systems are disclosed for validating data associated with schemas. A user defines the object model of at least one asset and a first schema is generated in accordance with the defined object model, and a unique fingerprint is generated. Data is collected from one or more devices in accordance with the object model. The collected data is serialized, and a second schema is generated. The second schema is ordered in accordance with the first schema and a unique fingerprint is generated. The fingerprint of the first schema is compared to the fingerprint of the second schema to provide an efficient review process for determining whether the schemas are equal, and the associated data may be validated. A fingerprint cache may be updated with fingerprints associated with a plurality of schemas, as well as version history of each schema, to provide an efficient review process.

Persistent storage may contain: (i) a database table containing entries, (ii) a definition of a communication endpoint of a remote system, and (iii) outbound flow processing. One or more processors may be configured to: detect a state change associated with a local entry in the database table; read, from the database table, a set of data representing the local entry; transform, using the outbound flow processing, the set of data into a format receivable by the remote system; create, for the set of data, a correlation record that contains a local correlation identifier, wherein the correlation record specifies the local entry; transmit, to the remote system, the set of data as transformed and the local correlation identifier; receive, from the remote system and for the set of data, a remote correlation identifier; add, to the correlation record, the remote correlation identifier; and write, to a correlation table, the correlation record.

Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

A system and methods for efficiently and consistently creating and delivering content tailored to a user's specific needs and channel of distribution. The system generates coherent, precise, and logical content that is in full compliance with the relevant policies, rules, regulations, and laws of users of the system. The content created and delivered by the system may be in any form, such as, but not limited to a video, a script, a press release, a social media post, and a keynote speech.

A transmission format cache may be implemented at a database storage node. Versions of data items stored in a database at the database storage node may be processed according to anticipated access requests to generate an anticipated access responses. The anticipated access responses are then stored in a transmission format cache to provide low latency reads of the data items. The versions of the data items may be processed as a result of updates to the database items. The database storage node may be one of a plurality of database storage nodes implementing a distributed database system with the transmission format cache implementing a portion of a distributed response cache providing low latency, eventually consistent or consistent reads of data items in a distributed database.

Disclosed herein is a system and method to any existing Unilevel MLM to be merged into a Multiline MLM system. Further the existing MLM members have full access to the Multiline MLM commission structure, for example, a member of a Unilevel MLM will maintain their existing lines and downlines.

Generating an improved/more accurate geolocation database is provided. Given a dataset of reverse DNS hostnames for IP addresses, ground truth information, and a hierarchical geographical database, a machine learning classifier can be trained to extract and disambiguate location information from the reverse DNS hostnames of IP addresses and to apply machine learning algorithms to determine location candidates and to select a most probable candidate for a reverse DNS hostname based on a confidence score. The classifier can be used to generate an accurate geolocation database, or to provide accurate geolocation information as a service.

This disclosure relates to computer systems that isolate data processing modules and methods of operating the same. In one embodiment of a method, a computer system receives a first data processing request for a first data file. The computer system may determine that a first data type of the first data file is supported by an encapsulated data processing module and determine a first communication protocol from a set of communication protocols that can be used to receive the first data file. The computer system can then receive the first data file from the first storage location in accordance with the first communication protocol and the encapsulated data processing module may be executed to convert the first data file in the first data format into second data in a second data format.