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classification emerges from usage
- Objects are classified solely through ordinary use of the system. A separate classification process, involving topic authorities is not required.
- Systematic distinction between classfier and classified object does not pertain. e.g. mammal / Andrew Henderson. Tags are classified only according to their proximity to other tags.
objective data, subjective information
heterogeneity
- Any pre-existing schema such as natural languages and newly emerging
schemas may be assimilated into Akinity either indirectly through
linking or directly through synthesis.
- As a currency does for goods exchange, so Akinity brings fungibility to information exchange between heterogeneous schemas.
data distribution
- Data is distributed unevenly and this pattern allows semantics to emerge.
- Distribution patterns change over time. Therefore the system evolves.
- There is no central locus of complete information.
probabilistic
- The system does not deliver absolute certainties, but for typical use-cases confidence can be extremely high.
a system of tags
not a graph
- Akinity's model is more like physical 3-D space than a logical network of nodes and edges. Any object in space has an intrinsic location that bestows it with a quantifiable distance from every other known object, without necessary recourse to some extrinsic maping table. This is also true for Akinity .
non-arbitrary data values
- Values may only be assigned by the algorithms.
- Values are binary. Nevertheless, they may be translated to text through an encoding such as Base64 or Base16
steganograpy
- Interpretation in Akinity is an application of exformation.
- The system is tolerant of asymmetric information in an exchange. Information may be passed through an unknowing subject to a third-party