Abstract: Described is a system, method, and computer program product to perform bi-directional mapping of hierarchical data (e.g. JSON, XML) to database object types (e.g., user defined database object types).

Abstract: The present invention relates to autonomous tuning of a data grid of documents in a database. Herein are techniques for storage cells to autonomously maintain local indices and other optimization metadata and algorithms to accelerate selective access into a distributed collection of documents. In an embodiment, each storage cell persists a respective subset of documents. Each storage cell stores, in memory, respective index(s) that map each item to location(s), in one or more documents of the respective subset of documents, where the item occurs. One or more computers execute, based on at least a subset of the indices of the storage cells, a data access request from a database management system. In an embodiment, a cloud of JSON document services provides an easy-to-use, fully autonomous JSON document database that horizontally and elastically scales to deliver fast execution of document transactions and queries without needing tuning by a database administrator.

Abstract: Functionalities of new data types are enabled for data stored using native data types (“recognized data types”) supported by DBMSs. Values having a latent data type may be stored using a recognized data type of a DBMS. That is, a value stored in a database table may be recognized by a DBMS as having a recognized data type (e.g., character string) but the value may also include information that indicates the value conforms to a particular latent data type. DBMSs compile database statements. The database statement can contain multiple expressions that may specify a latent data type or that may have a recognized data type but that also imply latent data types. By determining that various expressions have latent data types, operations that rely on the expressions as having a latent data type may be validated in a query and executed accordingly.

Abstract: Functionalities of new data types are enabled for data stored using native data types (“recognized data types”) supported by DBMSs. Values having a latent data type may be stored using a recognized data type of a DBMS. That is, a value stored in a database table may be recognized by a DBMS as having a recognized data type (e.g., character string) but the value may also include information that indicates the value conforms to a particular latent data type. DBMSs compile database statements. The database statement can contain multiple expressions that may specify a latent data type or that may have a recognized data type but that also imply latent data types. By determining that various expressions have latent data types, operations that rely on the expressions as having a latent data type may be validated in a query and executed accordingly.

Abstract: An antenna of an implant programming device that has a telemetry transmit/receive stage, in particular working in the ULP-AMI band, for wireless communication with an electronic medical implant. The antenna is formed as a conductor track on a stiff planar carrier substrate. The surface area of the carrier substrate is much larger than the surface area covered by the conductor track.

Abstract: A data guide is dynamically generated. The data guide describes the structures of hierarchical data objects added to a collection of hierarchical data objects. Examples of hierarchical data objects are documents that conform to XML (Extensible Mark-up Language) or data objects that conform to JSON (JavaScript Object Notation). The data guide may be created and/or updated as hierarchical data objects are added to the collection.

Abstract: A method and apparatus for automatically analyzing and providing feedback regarding the optimizability of a relational database query. A query developer's primary goal is to ensure that queries and DML operations are rewritten for the most efficient execution. Rewrite diagnosability captures metadata for each attempted query optimization including success or failure and the reasons for failure. The metadata is stored in association with the operators that were not removed through rewriting. Once all optimizations have been attempted and rewriting is complete, the metadata is selectively displayed based on the cost to perform the associated operation. The context of performing the operation may affect the cost. The cost may be based at least on the type of operation and where within the query tree the operation is located. A query developer may configure the database system not to execute the resulting query plan based on one or more criteria.

Abstract: A data guide is dynamically generated. The data guide describes the structures of hierarchical data objects added to a collection of hierarchical data objects. Examples of hierarchical data objects are documents that conform to XML (Extensible Mark-up Language) or data objects that conform to JSON (JavaScript Object Notation). The data guide may be created and/or updated as hierarchical data objects are added to the collection.

Abstract: Techniques for determining whether an XML query may be written are provided. In one scenario, an XML query includes an expandable path, which is a path expression that includes a wildcard character (*) or a descendant axis (//). An XML schema is used to generate multiple expanded paths (each of which is a simple path expression) from the expandable path. The XML schema is used to compare each expanded path to another expanded path and to determine whether the nodes identified by the respective expanded paths are guaranteed to occur in a particular order in XML documents that conform to the XML schema. In another scenario, an XML query includes an order operator that takes two paths as input. Similarly, an XML schema is used to compare the two paths and to determine whether the nodes identified by the two paths are guaranteed to occur in a particular order in XML documents that conform to the XML schema.

Abstract: Techniques and approaches are provided for creating indexes and column constraints on structured XML data that is stored in a relational database. Data Definition Language (DDL) Create Index and Create Constraint commands have extended syntax that allows the specification of a path-based expression instead of requiring a column and table name. A mapping created by the system when an XML Schema is registered stores the correspondence of XML data elements to automatically-created database tables and columns that are given names only useful for the internal system. When a user provides a path-based expression in a DDL when creating an index or constraint, the path-based expression is translated to the underlying database constructs using the mapping. Issues are addressed for handling path-based expressions that evaluate to more than one element. Additional index optimization is described using data type information available in the XML schema to select the optimal index type.

Abstract: A method and apparatus for automatically analyzing and providing feedback regarding the optimizability of a relational database query. A query developer's primary goal is to ensure that queries and DML operations are rewritten for the most efficient execution. Rewrite diagnosability captures metadata for each attempted query optimization including success or failure and the reasons for failure. The metadata is stored in association with the operators that were not removed through rewriting. Once all optimizations have been attempted and rewriting is complete, the metadata is selectively displayed based on the cost to perform the associated operation. The context of performing the operation may affect the cost. The cost may be based at least on the type of operation and where within the query tree the operation is located. A query developer may configure the database system not to execute the resulting query plan based on one or more criteria.

Abstract: Techniques and approaches are provided for creating indexes and column constraints on structured XML data that is stored in a relational database. Data Definition Language (DDL) Create Index and Create Constraint commands have extended syntax that allows the specification of a path-based expression instead of requiring a column and table name. A mapping created by the system when an XML Schema is registered stores the correspondence of XML data elements to automatically-created database tables and columns that are given names only useful for the internal system. When a user provides a path-based expression in a DDL when creating an index or constraint, the path-based expression is translated to the underlying database constructs using the mapping. Issues are addressed for handling path-based expressions that evaluate to more than one element. Additional index optimization is described using data type information available in the XML schema to select the optimal index type.

Abstract: Techniques for determining whether an XML query may be written are provided. In one scenario, an XML query includes an expandable path, which is a path expression that includes a wildcard character (*) or a descendant axis (//). An XML schema is used to generate multiple expanded paths (each of which is a simple path expression) from the expandable path. The XML schema is used to compare each expanded path to another expanded path and to determine whether the nodes identified by the respective expanded paths are guaranteed to occur in a particular order in XML documents that conform to the XML schema. In another scenario, an XML query includes an order operator that takes two paths as input. Similarly, an XML schema is used to compare the two paths and to determine whether the nodes identified by the two paths are guaranteed to occur in a particular order in XML documents that conform to the XML schema.