Disclosed herein is a system and method to any existing Matrix 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 Matrix MLM will maintain their existing lines and downlines.

Systems and methods related to the seamless integration of Content Management Interoperability Services (CMIS) client systems with native data models of repositories that may be utilized with such client systems are disclosed. In particular, certain embodiment of systems and methods for the integration of CMIS compliant client systems with CMIS compliant ECM systems by conforming CMIS secondary types utilized by CMIS clients to the native ECM artifacts utilized by the ECM system are disclosed.

Systems and methods are provided receiving, from a computing device associated with a first user of a content sharing platform, a request to access a second user profile associated with a second user in the content sharing platform, accessing activity data related to both the first user and the second user in the content sharing platform, determining common activity data to both the first user and the second user, wherein the common activity data comprises at least one media content item generated by the second user that was viewed or saved by the first user, or one or more datum saved by the first user from a communication received from the second user, and generating second user profile data comprising the common activity data related to both the first user and the second user in the content sharing platform as part of the second user profile.

A memory stores graph information representing a graph that includes nodes and inter-node edges. The nodes include a first plurality of nodes each associated with node information and a first node. Each of the inter-node edges has a weight. A processor extracts, in accordance with the node information, two or more nodes and transforms the two or more nodes into an aggregate node. The processor generates an aggregate inter-node edge between the aggregate node and the first node. The aggregate inter-node edge is associated with a weight based on two or more weights associated with two or more inter-node edges between the two or more nodes and the first node. The processor estimates first node information to be associated with the first node based on transformed graph information representing a transformed graph including the aggregate node and the aggregate inter-node edge.

A data complementing method implemented by a computer, the method includes: calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, respectively; performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold; and performing complementation of the missing data value by a statistical method based on a data value other than the missing data value of a data item corresponding to the missing data value, in a case where the degree of correlation is not larger than the correlation threshold.

In an illustrative embodiment, an automated system engineers customized feature vectors from geospatial information system (GIS) metadata. The system may include computing systems and devices for extracting metadata for GIS features located within a predetermined distance of a property from a GIS map file and storing the extracted GIS features within a feature vector. The system can augment each of the extracted GIS features with amplifying data features extracted from external data sources. The system can calculate a distance between the property and each extracted GIS feature, which establishes a relationship between the property and each GIS feature and associated amplifying data features. Amounts of correlation between each of the extracted GIS features and associated amplifying data features within the feature vector and a market assessment of the property location can be identified using a data model trained with a data set customized to characteristics of the property.

Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

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.

A non-transitory computer-readable storage medium storing a management program that causes a processor included in a computer to execute a process, the process includes extracting data identification information that identifies monitoring target data from instruction log data, the instruction log data being recorded operation for the monitoring target data to be monitored, and storing, in a memory, the data identification information and history identification information that identifies history data in association with each other, the history data indicating a history of the operation for the monitoring target data.

Various aspects of this disclosure relate to determining mapping issues in object relational mapping (ORM). An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic.