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  2. Comparison of data-serialization formats - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_data...

    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 ...

  3. JSON - Wikipedia

    en.wikipedia.org/wiki/JSON

    XML separates "data" from "metadata" (via the use of elements and attributes), while JSON does not have such a concept. Another key difference is the addressing of values. JSON has objects with a simple "key" to "value" mapping, whereas in XML addressing happens on "nodes", which all receive a unique ID via the XML processor.

  4. Serialization - Wikipedia

    en.wikipedia.org/wiki/Serialization

    Flow diagram. In computing, serialization (or serialisation, also referred to as pickling in Python) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer ...

  5. RDFLib - Wikipedia

    en.wikipedia.org/wiki/RDFLib

    RDFLib is a Python library for working with RDF, [2] a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1).

  6. JData - Wikipedia

    en.wikipedia.org/wiki/JData

    One can use either the direct-format or annotated-format for storing higher dimensional arrays, as natively supported by both JSON/UBJSON, but the benefit of using the annotated format for text-based JData, and the packed-array optimized format for binary-JData becomes more advantageous due to faster processing speed.

  7. Cubes (OLAP server) - Wikipedia

    en.wikipedia.org/wiki/Cubes_(OLAP_server)

    The logical conceptual model in Cubes is described using JSON and can be provided either in a form of a file, directory bundle or from an external model provider (for example a database). The basic model objects are: cubes and their measures and aggregates, dimensions and their attributes, hierarchies.

  8. BSON - Wikipedia

    en.wikipedia.org/wiki/BSON

    Compared to JSON, BSON is designed to be efficient both in storage space and scan-speed. Large elements in a BSON document are prefixed with a length field to facilitate scanning. In some cases, BSON will use more space than JSON due to the length prefixes and explicit array indices.

  9. Graph database - Wikipedia

    en.wikipedia.org/wiki/Graph_database

    Java, SQL, Python, C++, R: Massive parallel processing (MPP) database incorporating patented engines supporting native SQL, MapReduce, and graph data storage and manipulation; provides a set of analytic function libraries and data visualization [40] TerminusDB: 11.0.6: 2023-05-03 [41] Apache 2: Prolog, Rust, Python, JSON-LD