Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

Abstract: Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.

Abstract: A computer system receives a set of user-defined rules are that useable by a computer service to automate a decision flow. The computer system generates a graph model from the user-defined rules. From the graph model, the computer system determines an input dependency model that is indicative of a set of inputs referred to in the graph model. The input dependency model is useable by an orchestrator to coordinate accesses to the one or more data stores in which the set of inputs is stored. The computer system receives the set of inputs and determines one or more automated decisions by applying the set of inputs to the graph model.

Abstract: A system performs operations that include receiving, via first computing environment, a request to process text data using a first natural language processing (NLP) model. The operations further include accessing configuration data associated with the NLP model, where the configuration data generated using a domain specific language that supports a plurality of preprocessing modules in a plurality of programming languages. The operations also include selecting, based on the configuration data, one or more preprocessing modules of the plurality of preprocessing modules, generating, based on the configuration data, a preprocessing pipeline using the one or more preprocessing modules, and generating preprocessed text data by inputting the text data into the preprocessing pipeline. The preprocessed text data is provided to the first NLP model.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

Abstract: Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.