Abstract: Techniques are disclosed for implementing cross-zone communication for computing zones executing different coding protocols. A server computer system may receive, via a proxy layer of a first instance of an application executing within a first computing zone according to a first set of coding protocols, a request for a service executed via a second instance of the application in a second computing zone according to a second, different set of coding protocols. The system may alter, via a remote layer of the first instance, a set of data specified in the request to comply with the second, set of protocols. The system may transmit, via the remote layer of the first instance to a remote layer of the second instance, the altered set of data. The system may advantageously provide a simplified development interface allowing for both development and testing within a local environment without deployment of multiple different services.

Abstract: A method for operating a graph database, including receiving, by a computer system, a query to a particular graph database, the query identifying a plurality of vertices of the particular graph database. The method further includes performing, by the computer system, hash operations on two or more of the plurality of vertices to generate respective hash values and dividing, using the respective hash values, the query into a plurality of sub-queries, each corresponding to a subset of the plurality of vertices. The method also includes sending, by the computer system, ones of the plurality of sub-queries to a plurality of database repositories for the particular graph database.

Abstract: Methods and systems are presented for assisting a user to identify and evaluate features for use in a machine learning model configured to perform a task. Based on graph data associated with a graph data structure, a user interface is provided on a device. Based on user inputs received via the user interface, a feature candidate for the machine learning model is determined. The feature candidate is associated with a particular way of traversing the graph data structure to obtain attribute values associated with one or more vertices and/or one or more edges in the graph data structure. Based on the attribute values, a value corresponding to the feature candidate can be calculated. The value can be used to evaluate the effectiveness of the feature candidate in performing the task. The feature candidate can then be incorporated into the machine learning model as one of the input features.