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.

Some embodiments of the present application include obtaining datasets including a plurality of features and computing a correlation score between each of the features. Based on the correlation scores, the features may be clustered together such that each cluster includes features that are correlated with one another, and features included in different feature clusters lack correlation with one another. A machine learning model may be selected based on a set of input features for the model and the plurality of clusters such that each input feature is included in one of the feature clusters and no feature cluster includes more than one of the input features. Datasets may then be selected based on the set of input features, which may be used to generate training data for training the machine learning model.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes adapter information and resource type information. Further, an adapter instance may be generated using the adapter information to establish communication with the endpoint. Furthermore, an API response may be obtained, via the adapter instance, from the endpoint by querying the endpoint using an API call. Further, the API response may be parsed. Further, a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information corresponding to a resource type in the resource type information. Furthermore, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

An apparatus comprising an interface and a natural language processor. The interface receives a data retrieval request formatted in a natural language and the natural language processor processes the data retrieval request. Processing the data retrieval request includes identifying database entities, database relations, or any combination thereof based words in the data retrieval request. It can also include identifying database entity criterion, database relation criterion, or any combination thereof based on words in the data retrieval request. It also includes generating a database query based on the database entities, the database relations, the database entity criterion, the database relation criterion, or any combination thereof and causing the database query to be applied to a database. Wherein, processing the data retrieval request includes grammatically tagging the data retrieval request using part-of-speech tagging techniques, e.g. grammatical type, grammatical context, semantic, or any combination thereof, and a database ontology.

Systems, devices, and methods discussed herein are directed to generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees. A supported decision tree can be generated from the various decision chains (e.g., a sequence of elements comprising a premise and a decision connected by rhetorical relationships), where the nodes of the decision tree are identified from the plurality of decision chains and ordered based on a set of predefined priority rules. A lattice may include nodes that individually correspond to a respective supported decision tree. Nodes of the lattice may be identified for an input query. The passages corresponding to those nodes may be obtained and an answer for the query may be generated from the obtained passages using machine reading comprehension techniques. The generated answer may be provided in response to the query.

Embodiments of the present invention provide systems and methods for automated and intelligent analysis of information. The system receives interaction data, interaction metadata, and external information in order to identify parties of interactions, subjects of interactions, and infer relationships between parties and subjects based on the content, context, frequency, and amount of available interaction data. Weighted score scores are generated and used to rank the inferred relationships and determined relevance between parties and subjects. This data may be stored in a graphical database and later used to response to user data queries to facilitate collaboration.

A computer system processes a group of inputs. A group of entities that is input for processing is intercepted. The intercepted group is expanded into individual entities. Each of the individual entities is processed to produce results for each individual entity. The results for each individual entity are intercepted and merged to produce results for the group of entities. Embodiments of the present invention further include a method and program product for processing a group of inputs in substantially the same manner described above.