entrelacs

Introduction

We anticipate that an achieved Entrelacs system will demonstrate many outstanding native capabilities in comparison with a legacy computer system. To name a few:

information relativity

Every piece of information is “relative-able”. That is, it can be stated relatively to a referential. More specifically, the relation between a fact and its referential can itself been comprehended relatively to a meta-referential (abstraction).

All in all, there is only one absolute referential of valid relations.

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information conceptualizing

Information deduping is transparent and performed at system level. Each piece of information is present only once in the whole system. This representation is literally a mapping of the corresponding pure concept of information.

information self indexation

Information is self-indexed and fully connected. One can retrieve in a efficient way (O(n)?) all the knowledge related to some given information.

native persistent data structures

Information may be mostly stored in persistent data structures^W.

native orthogonal persistence

Information persistence may be easily orthogonal^W.

Memory management may be transparent as well. Data caching (in high memory levels) and garbage collection^W may be transparent and full-scope system process.

native memoization

Memoization^W may be easily made orthogonal. It could take the form of an automated process performed by the system, everytime, everywhere.

native fine-grained virtualization

Any computation process may be easily virtualized^W, that is performed in a relative environment. It makes a top-down security scheme easily feasible.

Conclusion

Because of these anticipated capabilities (also listed in this page), we consider the Entrelacs paradigm as the missing link up to a much clever generation of computers. Most data computing disciplines will get major benefits by transposing their works into Entrelacs systems. For example:

• Adapted programming languages will exhibit outstanding abilities like system-level persistent introspection and intercession, computation back-tracking, fine-grained virtualization, etc. <more …>
• Adapted Artificial Learning and Knowledge Representation algorithms will work across different abstraction and complexity scales and will be able to cross-cut ontologies across the whole information set. <more …>

Pragmatic note

We acknowledge that most systems may benefit from natively managing complex objects like raw data and tuples in addition to regular arrows. We consider such pragmatic systems to be still considered as Entrelacs systems as long as they manage such exotic objects in the same way as their arrows made equivalent constructs, especially in terms of uniqueness, immutability, and connectivity. <more …>