Such intelligent knowledge management services operate as the user's eyes under the covers, watching for meaningful patterns as they form, so applications and users can take remedial action before problems take root or opportunities fade away.

High scalability for success
By dynamically linking high-dimensional attribution through spatial continuum analogs, the conundrum of responsive, flexible, and descriptive high-dimensionalism is addressed in the face of massively scalable systems indexing across huge amounts of multimedia data. Through fractal spatial semantic indexing, dynamic compression becomes a natural byproduct of the self-simplification, self-organization, and self-describing, semantic fractal containment architecture. As volume increases, dimensional population converges dynamically into clusters of sameness through the process of semantic convergence, a natural 2G-KM dynamic model phenomenon.

Real-time response for productive learning and sharing
By implementing incremental indexing, where each document's kSig is independently indexed in real-time, without need to reorder ambient concept indexes ahead of time, the knowledge index becomes compute-inexpensive such that standard computing architectures can be harnessed to provide highly scalable implementations. And by implementing the index with a capability of expanding into massively parallel-distributed architectures, corporations and governments can grow and integrate their knowledge management networks on an affordable and responsive basis as their usage grows through successful implementations.

Harnessing Knowledge Signatures
kSigs can be harnessed transparently as primitive semantic operators for a number of knowledge concept aggregation and transformational services. Aggregate knowledge components can be built just-in-time, in real-time, in response to user needs, through similarity at the paragraph and concept level. Knowledge components can be aggregated by similarity, in focus to various perspectives called ViewPoints profiles, shown in Figure B.

FIGURE B


ViewPoints can be automatically generated from arbitrary text. Roll over picture for a larger image.

ViewPoints can be automatically generated from arbitrary text entered or copy-pasted by the user, application, or device, or from a document, document collection or user profile. This provides a highly intuitive way to query, discover, collect, and navigate meaningful text around knowledge components that reflect the significance of the concepts articulated through common language.

In the next article in the series, I'll discuss knowledge componetization and knowledge perspectives for mixing and matching knowledge concept combinations, and I'll explore how we learn, how we process signals in our brains, and how hyperspatial semantic indexing can facilitate the process.