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JSON: No Smile Format Specification: Yes No Yes Partial (JSON Schema Proposal, other JSON schemas/IDLs) Partial (via JSON APIs implemented with Smile backend, on Jackson, Python) — SOAP: W3C: XML: Yes W3C Recommendations: SOAP/1.1 SOAP/1.2: Partial (Efficient XML Interchange, Binary XML, Fast Infoset, MTOM, XSD base64 data) Yes Built-in id ...
JSON-LD is designed around the concept of a "context" to provide additional mappings from JSON to an RDF model. The context links object properties in a JSON document to concepts in an ontology. In order to map the JSON-LD syntax to RDF, JSON-LD allows values to be coerced to a specified type or to be tagged with a language.
Concatenated JSON isn't a new format, it's simply a name for streaming multiple JSON objects without any delimiters. The advantage of this format is that it can handle JSON objects that have been formatted with embedded newline characters, e.g., pretty-printed for human readability. For example, these two inputs are both valid and produce the ...
Comma-separated values is a data format that predates personal computers by more than a decade: the IBM Fortran (level H extended) compiler under OS/360 supported CSV in 1972. [14] List-directed ("free form") input/output was defined in FORTRAN 77, approved in 1978. List-directed input used commas or spaces for delimiters, so unquoted character ...
It was derived from JavaScript, but many modern programming languages include code to generate and parse JSON-format data. JSON filenames use the extension .json. Douglas Crockford originally specified the JSON format in the early 2000s. [1] He and Chip Morningstar sent the first JSON message in April 2001.
JSON-RPC (JavaScript Object Notation-Remote Procedure Call) is a remote procedure call (RPC) protocol encoded in JSON. It is similar to the XML-RPC protocol, defining only a few data types and commands. JSON-RPC allows for notifications (data sent to the server that does not require a response) and for multiple calls to be sent to the server ...
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.