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.

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.

Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

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).

The present disclosure involves computer-implemented method, medium, and system for automatically correlating semantically connected data and metadata. One example method includes identifying a document that is to be analyzed using a semantics based mapping (SBM) infrastructure. A matching process is performed for the identified document using the SBM infrastructure, where the matching process identifies a plurality of matching terms within the document, the plurality of matching terms are assigned to a plurality of semantics identifiers (IDs), and each semantics ID corresponds to one or more terms in the plurality of matching terms. Each of the plurality of matching terms is replaced with a respective term ID to generate an updated document. A request to search for a target term in the document is received. The target term is translated to a target term ID based on the SBM infrastructure. The updated document is searched for one or more matching terms.

A method, apparatus, and system for policy driven data placement and information lifecycle management in a database management system are provided. A user or database application can specify declarative policies that define the movement and transformation of stored database objects. The policies are associated with a database object and may also be inherited. A policy defines, for a database object, an archiving action to be taken, a scope, and a condition before the archiving action is triggered. Archiving actions may include compression, data movement, table clustering, and other actions to place the database object into an appropriate storage tier for a lifecycle phase of the database object. Conditions based on access statistics can be specified at the row level and may use segment or block level heatmaps. Policy evaluation occurs periodically in the background, with actions queued as tasks for a task scheduler.

A method, apparatus, and system for policy driven data placement and information lifecycle management in a database management system are provided. A user or database application can specify declarative policies that define the movement and transformation of stored database objects. The policies are associated with a database object and may also be inherited. A policy defines, for a database object, an archiving action to be taken, a scope, and a condition before the archiving action is triggered. Archiving actions may include compression, data movement, table clustering, and other actions to place the database object into an appropriate storage tier for a lifecycle phase of the database object. Conditions based on access statistics can be specified at the row level and may use segment or block level heatmaps. Policy evaluation occurs periodically in the background, with actions queued as tasks for a task scheduler.

An embodiment relates to a computer-implemented data processing system and method for storing a data set at a plurality of data centers. The data centers and hosts within the data centers may, for example, be organized according to a multi-tiered ring arrangement. A hashing arrangement may be used to implement the ring arrangement to select the data centers and hosts where the writing and reading of the data sets occurs. Version histories may also be written and read at the hosts and may be used to evaluate causal relationships between the data sets after the reading occurs.

A centralized database identifier repository may identify databases using a unique identifier, or key tag, for each database. Each identified database may include data relating to one or more specific data elements. The repository may include a variety of data elements. Each data element may be associated with one or more database keys. The repository may be a repository of reference pointers. The repository may facilitate data viewing and data retrieval. A requestor may search for a data element using the centralized repository. The repository may retrieve data relating to a specific data element, from all databases identified by unique identifiers, that include data relating to the data element. The databases' unique identifiers may be encrypted tokens.