The presently disclosed subject matter includes a computerized method and system that provide the ability to train and execute a unique machine learning (ML) model specifically configured to enhance classifier (e.g., RegEx) output by identifying and removing false positive results from the classifiers output. Classifier output, comprising a collection of data-subsets (e.g., columns in a relational database) of one or more structured or semi-structured data sources (e.g., tables of a relational database), are transformed to be represented by a plurality of numerical vectors. The numerical vectors are used during a training phase (as well as the execution phase) for training a machine learning model to enhance the classifier output and reduce false positives.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

A method and apparatus for managing capabilities of an account of a user system at a commerce platform are described. The method may include receiving, at the commerce platform, a request from a user system that identifies an account at the commerce platform and includes a capability requested for the account, where the requested capability corresponds to a desired functionality of the account. The method may also include mapping the requested capability to a compliance plan that defines a set of account data to be collected before activation of the capability for the account by the commerce platform. Furthermore, the method may include transmitting a message, generated by the commerce platform, that comprises a listing of additional data to be provided by the user system before activation of the capability for the account, where the additional data comprises a minimal set of account data defined by the compliance plan less any data already associated with the account at the commerce platform. The method may also include, in response to receiving, by the commerce platform, at least one response from the user system that includes the additional data, activating the capability for the account.

A method may include calculating a first set of hash values for a set of well log channels, extracting a well log channel snippet from an unknown well log channel, calculating a second set of hash values for the well log channel snippet, identifying, for the unknown well log channel, a matching well log channel by searching the first set of hash values with the second set of hash values, and storing, for the unknown well log channel, a channel context corresponding to the matching well log channel.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

An example operation may include one or more of determining, by a supply-chain node, a plurality of assets of a supply-chain node, and executing, by the supply-chain node, a smart contract to: assign aliases to the plurality of the assets; and match the plurality of the assets with a plurality of assets of a different supply-chain node based on the aliases.

Systems, apparatuses and methods may provide for technology that includes a single server to store a portion of a temporal graph to a first memory of the single server, and store a second portion of the temporal graph to a second memory of the single server, wherein an access rate of the first memory is greater than an access rate of the second memory, and wherein a capacity of the second memory is greater than a capacity of the first memory. The single server may also retrieve vertices of the second portion in response to a selectivity of an input query exceeding a cost model threshold.

Embodiments of the present disclosure are directed to systems and methods for managing a database. In one or more examples, the system obtains input data comprising one or more data entries, where each data entry comprises one or more data items, and each data item comprises a field name and a field value. The system can generate a key-value set for each data item to obtain a plurality of key-value sets. Each key-value set includes at least a first key element comprising the field name of the respective data item and a second key element comprising the field value of the respective data item. The system can sort and store the plurality of key-value sets in the database. The system can further receive a query indicative of a field name or a field value, and generate, for display, an output based on retrieved key elements sets based on the query.

A binary relational database model is described whereby application-layer object structures are easily inferred from database query templates. The object structures take the form of acyclic hypergraphs, which are induced from primal graphs representing query templates. Database applications may iterate through the collection of returned object structures, accessing the data in each structure. The returned object structures are not based on a fixed object model, thereby permitting rich structures with greater applicability than traditional ORM systems. A relationship between non-primitive entities may be directly expressed without the need for alternative join tables. Development and maintenance costs are thus substantially reduced, and data is more efficiently stored and manipulated for database applications.

A method for comparing a first and a second databases to determine whether an individual is represented by both an element of the first database and an element of the second database, wherein said elements are biometric data, including the implementation of the steps applying a classification model for each element of each database so as to construct a set of first and second bins of the respective first and second databases, each bin bringing together similar elements, each first bin being associated with a second bin; comparing the elements from the first database for at least one pair of an associated first bin and second bin belonging to said first bin with the elements from the second database belonging to said second bin, at least one of the first and the second databases then being encrypted homomorphically.