The applications of ADEPT are varied and fascinating but let's focus on a few of the most interesting.
Relational, graph, document store, spreadsheet, transactional, analytical: ADEPT as a database that does it all.
ADEPT represents an innovative approach to bridging the gaps between data silos and managing data transfer formats.
ADEPT includes a native representation of transactional processes which can enable new data models for marketplaces, platform cooperatives and virtual token economies.
Much like ADEPT's 7 channels, the human brain works with only a limited number of primary neurotransmitters. ADEPT is therefore a theoretical model for how the human brains may be wired to acheive consciousness.
Intelligent machines utilizing ADEPT as a memory framework can integrate sensory experience with stored knowledge in a single, connected and ordered graph instance to produce adaptive and transparent intelligence.
The first task in applying ADEPT is to identify the abstract informational patterns that underlie a particular set of data. The implementation diagrams that follow will serve as examples of this one-time work. The important point is that any and all of these implementations of various information frameworks can co-exist with integration of all content within a single instance of ADEPT.
We have developed a script for uploading data as a CSV file in ADEPT notation directly to a Neo4j graph database instance. Similar scripts for other graph database systems will be developed in time.
We have developed an initial prototype for an API and a browser-based user application built on a Neo4j graph database back-end that allows for create/read/update functionality, as well as data exploration of an ADEPT instance. This graphical user interface does not require the user to have any knowledge of graph traversal languages such as Cypher or Gremlin to manage and explore their dataset.
Property Graph Database