An Information Data Structure is a data structure for storing Real Information. Real information is imprecise by nature. it has no natural order. It's context is more important than it's associated data. It can be kept in a variable, providing context to a piece of data rather than in a hardcoded format such as xml, or objects. You see, each piece of data in one of these formats is important information only within the context that it resides, for example 'patient-name-firstname' can be a context for the name 'Jon'. 'Jon' is the data and 'patient-name-firstname' is the information. In an information data structure, this information is kept in a variable rather than as literal in the form of xml tags or class fields. Information data structures are used in Information Modeling.
Some Examples:
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Fuzzy Logic data structures
Endemes and their related Endeme Set - this is the one that can be used for field storage as described above. It can also be used for Artificial Creativity, creating Mutual Language s, Decision Automation, and Resource Management. Defining the word endeme in English it is a Concept Permutation Emergent Property Structure.
Computer Ontologies - Relationship definition systems such as OWL.
Data structures for technical analysis of stocks and other time vs number graphs.
Data Mining result sets
-- Jon Grover
See original on c2.com 