Abstract: Various examples are directed to systems and methods for evaluating an Application Program Interface (API) for interfacing an application to a database through a data model. A computing system may access a first view data structure associated with a first API call. The computing system may use a first view data structure to select a first table from the one or more tables, the first table being associated with the first API call, where the data model is described by a plurality of view data structures based on one or more tables at a database, including the first view data structure. The computing system may measure the API against a standard of performance for the API using a first performance double view data structure and a first performance double table.

Abstract: In an implementation, a computer-implemented method, includes collecting, as collected integration flows (iFlows), published iFlows. Descriptions of the collected iFlows are extracted as extracted descriptions and the extracted descriptions are parsed. A list of one or more interchangeable operators is created. The collected iFlows are iterated through. Automated performance recommendations for a new iFlow are provided.

Abstract: Various examples are directed to systems and methods for evaluating an Application Program Interface (API) for interfacing an application to a database through a data model. A computing system may access a first view data structure associated with a first API call. The computing system may use a first view data structure to select a first table from the one or more tables, the first table being associated with the first API call, where the data model is described by a plurality of view data structures based on one or more tables at a database, including the first view data structure. The computing system may measure the API against a standard of performance for the API using a first performance double view data structure and a first performance double table.

Abstract: Embodiments may be associated with database access. In some embodiments, a machine learning linear regression training platform determines a set of database properties (e.g., direct and/or indirect properties such as a document type, a type of process, a number of items, etc.) associated with Online Transaction Processing (“OLTP”) database table access. The machine learning linear regression training platform may then train a linear regression model based on the set of database properties and prior locking information (e.g., locking and unlocking timestamps) that represent access to the OLTP database table. Information about the linear regression model may be output to a wait time estimation platform that adapted to use the linear regression model to generate, in substantially real-time, an estimated wait time for an OLTP database table access based on the database properties of the OLTP database table access.

Abstract: A method, a system, and a computer program product for generating name recommendations in a core data services computing environment. A dataset for training a name data model is received. The name data model is configured for determination of a recommendation for one or more names in a plurality of names associated with one or more artifacts in a plurality of artifacts of a database management system. The name data model is trained using the received dataset and applied to generate one or more names associated with the one or more artifacts.

Abstract: Various examples are directed to systems and methods for tuning a database service in a cloud platform. A tuning service may access a neural network model trained to classify workload points to one of classes. The tuning service may execute the neural network model with a first source workload point as input to return a first class as output, where the first source workload describing a source database. The tuning service may select a target workload for the first source workload point from a plurality of reference workloads. Selecting the target workload may be based at least in part on the first class returned by the neural network model. The tuning service may generate a recommended knob configuration for the source database using the target workload.

Abstract: Embodiments allow automated provisioning of a plan upgrade for databases hosted in storage environments. A database is hosted in a shared storage environment according an existing plan, based upon consumption of available system resources (e.g., processing, I/O, memory, disk). An agent periodically issues requests for information relevant to database behavior (e.g., performance metrics, query logs, and/or knob settings). The agent collects the received information (e.g., via a domain socket), performing analysis thereon to predict whether future database activity is expected remain within the existing plan. Such analysis can include but is not limited to compiling statistics, and calculating values such as entropy, information divergence, and/or adjusted settings for database knobs. Based upon this analysis, the agent communicates a recommendation including a plan update and supporting statistics.

Abstract: Embodiments may be associated with database access. In some embodiments, a machine learning linear regression training platform determines a set of database properties (e.g., direct and/or indirect properties such as a document type, a type of process, a number of items, etc.) associated with Online Transaction Processing (“OLTP”) database table access. The machine learning linear regression training platform may then train a linear regression model based on the set of database properties and prior locking information (e.g., locking and unlocking timestamps) that represent access to the OLTP database table. Information about the linear regression model may be output to a wait time estimation platform that adapted to use the linear regression model to generate, in substantially real-time, an estimated wait time for an OLTP database table access based on the database properties of the OLTP database table access.

Abstract: Technologies are described for the automated determination of materials. For example, material can be automatically identified (e.g., unique material numbers can be determined) based on sensor data and using machine learning models. In some implementations, as part of a first phase, a first set of sensor information describing the material is obtained. Using the first set of sensor information, a material class of the material is determined. As part of a second phase, a second set of sensor information describing the material is obtained. Using the second set of sensor information, the specific material is identified (e.g., a unique material identifier is determined for the material).

Abstract: Technologies are described for the automated determination of materials. For example, material can be automatically identified (e.g., unique material numbers can be determined) based on sensor data and using machine learning models. In some implementations, as part of a first phase, a first set of sensor information describing the material is obtained. Using the first set of sensor information, a material class of the material is determined. As part of a second phase, a second set of sensor information describing the material is obtained. Using the second set of sensor information, the specific material is identified (e.g., a unique material identifier is determined for the material).

Abstract: Various examples are directed to systems and methods for tuning a database service in a cloud platform. A tuning service may access a neural network model trained to classify workload points to one of classes. The tuning service may execute the neural network model with a first source workload point as input to return a first class as output, where the first source workload describing a source database. The tuning service may select a target workload for the first source workload point from a plurality of reference workloads. Selecting the target workload may be based at least in part on the first class returned by the neural network model. The tuning service may generate a recommended knob configuration for the source database using the target workload.

Abstract: Embodiments allow automated provisioning of a plan upgrade for databases hosted in storage environments. A database is hosted in a shared storage environment according an existing plan, based upon consumption of available system resources (e.g., processing, I/O, memory, disk). An agent periodically issues requests for information relevant to database behavior (e.g., performance metrics, query logs, and/or knob settings). The agent collects the received information (e.g., via a domain socket), performing analysis thereon to predict whether future database activity is expected remain within the existing plan. Such analysis can include but is not limited to compiling statistics, and calculating values such as entropy, information divergence, and/or adjusted settings for database knobs. Based upon this analysis, the agent communicates a recommendation including a plan update and supporting statistics.