A database storing data associated with an identifier unique to each sample, the data including first data representative of at least one of composition data, processing data, and property data for the each sample, and second data representative of microstructure data for the each sample. The microstructure data includes a feature determined based on a temperature dependence of magnetization for the each sample.

In various aspects of the present disclosure, systems and methods are described to identify and resolve structured queries so they execute consistently and accurately against any data architecture, and for example, dynamic or unstructured database stores. According to one embodiment, a dynamic schema data system implements a query dialect that is configured to expose underlying flexible schemas of the dynamic schema data system, any structured data, unstructured or partially structured data, and expressive querying native to the dynamic schema system in a language that is compatible with structured queries, and for example, compatible with SQL-92. In further embodiments, the query dialect is configured to enable consistency with existing dynamic schema database query semantics (e.g., the known MongoDB database and associated query semantics).

A method includes: receiving, by a computing device, input defining a custom storage class in a first dispersed storage network; receiving, by the computing device, input defining a data management rule in the first dispersed storage network; determining, by the computing device, a data object in the first dispersed storage network satisfies the data management rule; and moving, by the computing device and in response to the determining, the data object to a second dispersed storage network according to the custom storage class.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.

Described are methods, systems and computer readable media for performance logging of complex query operations.

Described are methods, systems and computer readable media for data source refreshing.

A multi-tenant system, comprises a main storage system including: a monolithic database storing global records associated with global objects, each global object including global fields common for all tenants; a monolithic application configured to process a particular global record storage request by instructing the monolithic database to store particular global field values of the particular global record for a particular tenant, and to process a particular global record fetch request by instructing the monolithic database to retrieve the one or more particular global field values; a custom object storage system including: a custom object database configured to store custom records associated with one or more custom objects, each custom object including one or more custom fields for a tenant; a custom object record service configured to process a particular custom record storage request by instructing the custom object database to store one or more particular custom field values for the tenant, and to process a particular custom record fetch request by instructing the custom object database to retrieve the one or more particular custom field values; and a query engine configured to receive a query, fetch relevant global records from the monolithic database, fetch relevant custom records from the custom object database, and generate a query response.

User information is protected by providing a protective layer between a provider and a user device. A server receives a suggestion to present to the user device from a third party, such as a provider of goods or services that wants to push the suggestion to the user device. The suggestion includes a request for user information. The server then determines a likelihood that the request for user information is a necessary component of the suggestion. When the likelihood is low, the request is removed from the suggestion. When the likelihood is high, the server creates an executable computer code that includes the request. The executable computer code can be transmitted to the user device to present the suggestion to the user device without disclosing the user's information to the server.

In general, embodiments of the technology relate to a method and system for implementing external content type (ECT) object types in a content management system (CMS). More specifically, embodiments of the technology relate to augmenting the object model with a new class of object type—an external content type (ECT) object type. The ECT object types are a class of object types that are used to manage object (referred to as managed external objects) that are located in external data systems.