In an embodiment, a computer obtains a mapping of a relational schema of a database to a graph data model. The relational schema identifies vertex table(s) that correspond to vertex type(s) in the graph data model and edge table(s) that correspond to edge type(s) in the graph data model. Each edge type is associated with a source vertex type and a target vertex type. Based on that mapping, a forward compressed sparse row (CSR) representation is populated for forward traversal of edges of a same edge type. Each edge originates at a source vertex and terminates at a target vertex. Based on the forward CSR representation, a reverse CSR representation of the edge type is populated for reverse traversal of the edges of the edge type. Acceleration occurs in two ways. Values calculated for the forward CSR are reused for the reverse CSR. Elastic and inelastic scaling may occur.

Various embodiments relate generally to data science and data analysis, computer software and systems, and network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform configured to provide one or more computerized tools to deploy predictive data models based on in-situ auxiliary query commands implemented in a query, and configured to facilitate development and management of data projects by providing an interactive, project-centric workspace interface coupled to collaborative computing devices and user accounts. For example, a method may include activating a query engine, implementing a subset of auxiliary instructions, at least one auxiliary instruction being configured to access model data, receiving a query that causes the query engine to access the model data, receiving serialized model data, performing a function associated with the serialized model data, and generating resultant data.

Herein are techniques that extend a software system to embed new guest programing languages (GPLs) that interoperate in a transparent, modular, and configurable way. In embodiments, a computer inserts an implementation of a GPL into a deployment of the system. A command registers the GPL, define subroutines for the GPL, generates a guest virtual environment, and adds a binding of a dependency to a guest module. In an embodiment, a native programing language invokes a guest programing language to cause importing intra- or inter-language dependencies. An embodiment defines a guest object that is implemented in a first GPL and accessed from a second GPL. In an embodiment, dependencies are retrieved from a virtual file system having several alternative implementation mechanisms that include: an archive file or an actual file system, and a memory buffer or a column of a database table.

As typical databases evolve and the schema defining the stored data changes, difficulties arise in interfacing with the database and compatibility to applications may be lost. Provided for are methods for retrieving data stored in a database using versioned reference perspectives of edges, which define relationships between nodes. The methods provide for backwards-compatibility in accessing node-data stored in accordance with a pre-defined schema based upon a request including a version identifier. Also provided for are backend systems, frontend systems, and industrial machines for the manipulation of work products.

An AI-based process monitoring system access a plurality of data sources having different data formats to collect and analyze KPI data and shortlist KPIs that are to be used for determining the impact of automation of an automated process or sub-process. Information regarding an automated process is received and KPIs associated with the process and sub-processes of the process are identified. The identified KPIs are put through an approval process and the approved KPIs are presented to a user for selection. The user-selected KPIs are evaluated based on classification, ranking and sentiments associated therewith. The evaluations are again presented to the user along with a set of questionnaires wherein each of the questions has a dynamically controlled weight associated therewith. Based at least on the weights and user responses, a subset of the evaluated KPIs are shortlisted for use in evaluating the impact of process automation.

Techniques are disclosed relating to providing and using probabilistic data structures to at least reduce requests between database nodes. In various embodiments, a first database node processes a database transaction that involves writing a set of database records to an in-memory cache of the first database node. As part of processing the database transaction, the first database node may insert, in a set of probabilistic data structures, a set of database keys that correspond to the set of database records. The first database node may send, to a second database node, the set of probabilistic data structures to enable the second database node to determine whether to request, from the first database node, a database record associated with a database key.

A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

A computing system includes: a control unit is configured to: communicate with a vehicle telematics monitoring system including a statistical database, request an efficiency data, from the vehicle telematics monitoring system, calculate a threshold for the efficiency data, process diagnostic information for a flow, an intake air pressure, an intake air temperature, rotations per minute (RPM), or a combination thereof, calculate an efficiency model based on the diagnostic information; and a communication unit, coupled to the control unit, configured to: communicate a message, when the efficiency model is less than or equal to the threshold, indicating an air filter should be replaced and the message is for displaying on a device.

A computer displays, in a user interface, a data field region and an object model visualization region that includes object model icons. The computer detects user input to join a first object class and a second object class and detects user selection of a first linking field and user selection of a second linking field. In response to receiving the user selection of the first linking field and the second linking field, the computer generates a relationship that connects the first object class and second object class according to shared data values of the first linking field and the second linking field. The computer also displays, in the data field region, information regarding cardinality of the relationship and information regarding referential integrity of the relationship. The computer also updates the object model visualization region to display a visual connection between the object icons representing the first and second object classes.

In an embodiment, a method comprises creating and storing, at a client computer, schema blueprint data comprising a plurality of different service definitions, each of the service definitions composed in a domain specific language (DSL), each service definition comprising identification of an endpoint and one or more schema definition language elements; generating, based on the schema blueprint data, a combined schema in a graph query language processing system, the combined schema indicating which querying operations and mutating operations that a graph endpoint of the graph query language processing system can execute, the generating the combined schema comprising: automatically mapping a first resource of a first plurality of digitally stored resources from the endpoint of a first service definition of the plurality of service definitions to a first field in the combined schema; automatically mapping a second resource of a second plurality of digitally stored resources from the endpoint of a second service definition of the plurality of service definitions to a second field in the combined schema; generating and submitting a query to the graph endpoint based on the combined schema that causes, by traversing the mappings, retrieving the first resource from the endpoint of the first service definition and the second resource from the endpoint of the second service definition.