Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: An authentication system that supports passive provisioning of an authentication credential includes a widget that controls a user interface and that is embedded in a first inline frame (iframe) of a first webpage and an authenticator that conditionally provides the widget with access to confidential user account information. The authenticator is configured to generate an authentication token and store the authentication token in association with a first session ID associated with a set of inputs received through the user interface and launch a second webpage that includes a second iframe populated with the authentication token, where the second iframe identifies a URL in a same domain as a URL identified by the first iframe embedded within the first webpage. The second webpage further embeds a communication instruction executable to transmit the authentication token from the second iframe embedded within the second webpage to the widget embedded within the first webpage.

Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: A method is directed to associating quality metadata with underlying data. The method includes, for one or more data items, a computing system identifying one or more threshold conditions related to the data items. The computing system determines that the one or more threshold conditions related to the data items have been met. As a result of determining that the one or more threshold conditions related to the data items have been met, the computing system associates quality metadata with the data items.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Identifying data quality along a data flow. A method includes identifying quality metadata for two or more datasets. The quality metadata defines one or more of quality of a data source, accuracy of a dataset, completeness of a dataset, freshness of a dataset, or relevance of a dataset. At least some of the metadata is based on results of operations along a data flow. Based on the metadata, the method includes creating one or more quality indexes for the datasets. The one or more quality indexes include a characterization of quality of two or more datasets.

Abstract: A method is directed to associating quality metadata with underlying data. The method includes, for one or more data items, a computing system identifying one or more threshold conditions related to the data items. The computing system determines that the one or more threshold conditions related to the data items have been met. As a result of determining that the one or more threshold conditions related to the data items have been met, the computing system associates quality metadata with the data items.

Abstract: Fluent API Patterns promote balance in managing object persistence. Code which obtains an object-relational mapper session contains API Patterns that manipulate persistence ignorant objects using calls to a fluent interface. When the developer's code executes, it may manipulate an object state, a property state, and/or a persistent relationship of the persistence ignorant object. The code may implement API Patterns such as find-change-save, current-value-original-value, nested-property, incremental-graph-load, incremental-graph-filtered-load, property-modification, dictionary, and/or other API Patterns. Additional examples of API Patterns include an API Pattern to read current, original, and database values for all properties of an entity; an API Pattern to set current and/or original values from another object; and an API Pattern to create a cloned object containing current, original, and/or database values.

Abstract: Techniques are described herein that are capable of translating programming language patterns into database schema patterns. Examples of a programming language pattern include but are not limited to a source code pattern, an intermediate language code pattern, metadata associated with a source code pattern, metadata associated with an intermediate language code pattern, or any combination thereof. The programming language patterns may define objects that are included in an object model. Database schema patterns that define a database are automatically (e.g., inferentially) created based on respective programming language patterns that define objects of an object model. The programming language patterns are automatically mapped to the respective database schema patterns.

Abstract: Fluent API Patterns promote balance in managing object persistence. Code which obtains an object-relational mapper session contains API Patterns that manipulate persistence ignorant objects using calls to a fluent interface. When the developer's code executes, it may manipulate an object state, a property state, and/or a persistent relationship of the persistence ignorant object. The code may implement API Patterns such as find-change-save, current-value-original-value, nested-property, incremental-graph-load, incremental-graph-filtered-load, property-modification, dictionary, and/or other API Patterns. Additional examples of API Patterns include an API Pattern to read current, original, and database values for all properties of an entity; an API Pattern to set current and/or original values from another object; and an API Pattern to create a cloned object containing current, original, and/or database values.

Abstract: Programmers typically like operating with databases through use of classes. Classes allow a user to function with a database through use of a non-database programming language. To ease use of the classes, a class context is generated at runtime that does not force a programmer to handle various source files that are a product of class generation. Therefore, the user can have the ease of using classes without having to perform extra actions.

Abstract: Binding can automatically take place between controls and information sources at runtime. This minimizes an amount of code that is to be generated by a user and thus can lower errors from the code. In addition, some wizards that allow binding without code writing can become problematic since some information is not available at runtime (e.g., when the wizard operates prior to runtime.) Relevant information is received and at least one binding is created based off received information.

Abstract: Programmers typically like operating with databases through use of classes. Classes allow a user to function with a database through use of a non-database programming language. To ease use of the classes, a class context is generated at runtime that does not force a programmer to handle various source files that are a product of class generation. Therefore, the user can have the ease of using classes without having to perform extra actions.