Abstract: Processes for monitoring downhole corrosion and directing operational plans using same. In some embodiments, the process can include acquiring a plurality of corrosion factors for at least one well. The process can also include acquiring a plurality of corrosion loss logs for the at least one well. The plurality of corrosion factors and the plurality of corrosion loss logs can be provided to a repository. The repository can be provided to a machine learning model to generate a corrosion prediction. At least the plurality of corrosion factors, the plurality of corrosion loss logs, and the corrosion prediction can be combined into a user dashboard. The user dashboard can be used to determine an operational plan for the at least one well. The determined operational plan for the at least one well can be carried out.

Abstract: The present disclosure relates to systems and methods for scheduling meetings for a business process. The systems and methods may dynamically create and send resource requests for meetings based on meeting criteria for the meeting. The systems and methods may identify available resources for a meeting based on attributes of the available resources matching the meeting criteria. The systems and methods may book a meeting with one or more available resources for the meeting.

Abstract: Techniques are described for integrating prediction capabilities from data management platforms into applications. Implementations employ a data science platform (DSP) that operates in conjunction with a data management solution (e.g., a data hub). The DSP can be used to orchestrate data pipelines using various machine learning (ML) algorithms and/or data preparation functions. The data hub can also provide various orchestration and data pipelining capabilities to receive and handle data from various types of data sources, such as databases, data warehouses, other data storage solutions, internet-of-things (IoT) platforms, social networks, and/or other data sources. In some examples, users such as data engineers and/or others may use the implementations described herein to handle the orchestration of data into a data management platform.

Abstract: An adapter framework is provided for consuming resources provided by a database service by client applications. A location of an interface of the database service is received. Static metadata of a client application is received from the client application. Mapping of field names defined at the database service to field names defined in the client application is received at the framework. Metadata for a service interface is created at the adapter framework based on the received static metadata and dynamic metadata for dynamic fields from the database service. The mapping of field names is appended with mappings for the dynamic fields. At a runtime processor, a type of a data request from the client to the database service is determined. The request is processed and a response from the database service is generated by the adapter framework and provided to the client application in an expected format.

Abstract: Techniques are described for integrating prediction capabilities from data management platforms into applications. Implementations employ a data science platform (DSP) that operates in conjunction with a data management solution (e.g., a data hub). The DSP can be used to orchestrate data pipelines using various machine learning (ML) algorithms and/or data preparation functions. The data hub can also provide various orchestration and data pipelining capabilities to receive and handle data from various types of data sources, such as databases, data warehouses, other data storage solutions, internet-of-things (IoT) platforms, social networks, and/or other data sources. In some examples, users such as data engineers and/or others may use the implementations described herein to handle the orchestration of data into a data management platform.

Abstract: Techniques are described for integrating prediction capabilities from data management platforms into applications. Implementations employ a data science platform (DSP) that operates in conjunction with a data management solution (e.g., a data hub). The DSP can be used to orchestrate data pipelines using various machine learning (ML) algorithms and/or data preparation functions. The data hub can also provide various orchestration and data pipelining capabilities to receive and handle data from various types of data sources, such as databases, data warehouses, other data storage solutions, internet-of-things (IoT) platforms, social networks, and/or other data sources. In some examples, users such as data engineers and/or others may use the implementations described herein to handle the orchestration of data into a data management platform.

Abstract: Various examples are directed to exposing database artifacts. For example, a rules engine may receive schema data describing a database schema of an in-memory database. The schema data may describe a table, a view, and a procedure. The rules engine may generate a data model comprising a plurality of translation artifacts including a table translation artifact describing a table of the database schema, a view translation artifact describing a view of the database schema, and a procedure translation artifact describing a procedure of the database schema. A mapping service may receive from a client application a first client request comprising first metadata describing the view translation artifact. The mapping service may initiate execution of the view at an in-memory database to determine a view result; and send the view result to the client application.

Abstract: An adapter framework is provided for consuming resources provided by a database service by client applications. A location of an interface of the database service is received. Static metadata of a client application is received from the client application. Mapping of field names defined at the database service to field names defined in the client application is received at the framework. Metadata for a service interface is created at the adapter framework based on the received static metadata and dynamic metadata for dynamic fields from the database service. The mapping of field names is appended with mappings for the dynamic fields. At a runtime processor, a type of a data request from the client to the database service is determined. The request is processed and a response from the database service is generated by the adapter framework and provided to the client application in an expected format.

Abstract: Various examples are directed to exposing database artifacts. For example, a rules engine may receive schema data describing a database schema of an in-memory database. The schema data may describe a table, a view, and a procedure. The rules engine may generate a data model comprising a plurality of translation artifacts including a table translation artifact describing a table of the database schema, a view translation artifact describing a view of the database schema, and a procedure translation artifact describing a procedure of the database schema. A mapping service may receive from a client application a first client request comprising first metadata describing the view translation artifact. The mapping service may initiate execution of the view at an in-memory database to determine a view result; and send the view result to the client application.