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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 XML. jq is like sed for JSON data – it can be used to slice and filter and map and transform structured data.
While JSON provides a syntactic framework for data interchange, unambiguous data interchange also requires agreement between producer and consumer on the semantics of specific use of the JSON syntax. [25] One example of where such an agreement is necessary is the serialization of data types that are not part of the JSON standard, for example ...
The CIM standard includes the CIM Infrastructure Specification and the CIM Schema: . CIM Infrastructure Specification; The CIM Infrastructure Specification defines the architecture and concepts of CIM, including a language by which the CIM Schema (including any extension schema) is defined, and a method for mapping CIM to other information models, such as SNMP.
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
Double jeopardy (marketing) Double loop marketing; Emotional branding; Engagement (marketing) Facelift (product) Fallacy of quoting out of context; Fine print; Flighting (advertising) Growth Hacking; Heavy-up; Inseparability; Intangibility; Integrated marketing communications; Low-end market; Marketing communications; Marketing experimentation ...
Marketers typically begin planning with a detailed understanding of customer needs and wants. A need is something required for a healthy life (e.g. food, water, shelter, emotional bonding); A want is a desire, wish or aspiration; When needs or wants are backed by purchasing power, they have the potential to become demands.
Big data in marketing is a highly lucrative tool that can be used for large corporations, its value being as a result of the possibility of predicting significant trends, interests, or statistical outcomes in a consumer-based manner. [115] There are three significant factors in the use of big data in marketing:
The pub/sub pattern scales well for small networks with a small number of publisher and subscriber nodes and low message volume. However, as the number of nodes and messages grows, the likelihood of instabilities increases, limiting the maximum scalability of a pub/sub network. Example throughput instabilities at large scales include: