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JSON Pointer [10] defines a string syntax for identifying a single value within a given JSON value of known structure. JSONiq [11] is a query and transformation language for JSON. XPath 3.1 [12] is an expression language that allows the processing of values conforming to the XDM [13] data model. The version 3.1 of XPath supports JSON as well as ...
JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. It provides a contract for the JSON data required by a given application and how that data can be modified. [ 29 ]
JsonML, the JSON Markup Language is a lightweight markup language used to map between XML (Extensible Markup Language) and JSON (JavaScript Object Notation). It converts an XML document or fragment into a JSON data structure for ease of use within JavaScript environments such as a web browser , allowing manipulation of XML data without the ...
It can convert a wide range of complex data structures, including dict, array, numpy ndarray, into JData representations and export the data as JSON or UBJSON files. The BJData Python module, pybj, [4] enabling reading/writing BJData/UBJSON files, is also available on PyPI, Debian/Ubuntu and GitHub.
^ XML data bindings and SOAP serialization tools provide type-safe XML serialization of programming data structures into XML. Shown are XML values that can be placed in XML elements and attributes. ^ This syntax is not compatible with the Internet-Draft, but is used by some dialects of Lisp.
JSONiq primarily provides means to extract and transform data from JSON documents or any data source that can be viewed as JSON (e.g. relational databases or web services). The major expression for performing such operations is the SQL-like “FLWOR expression” that comes from XQuery. A FLWOR expression is constructed from the five clauses ...
Download as PDF; Printable version; In other projects ... Data structure: level: 4: topic ... Drug test: level: 5: topic "Biology and health sciences"
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.