TAS' Approach to Data Science

- Determining key information units that underpin and define datasets or document corpuses that are pivotal to an organization’s data universe

- Find inherent = irrefutable, indisputable structures, elements and axioms in given document corpus or dataset through TAS axiomatic AI and Machine learning technology

- Model these inherent structures and elements into an inherent taxonomy for a particular document corpus/dataset or into an overarching “uber” taxonomy that incorporates multiple organizational silos

- This taxonomy represents hierarchical node structure interconnected through what we call as “linkbases” = these form an interlinkage between nodes (these could be  logical, numerical or conditional linkages)

- This taxonomy data model forms the backbone for all the information processing, analysis and analytical tasks performed in an information-intensive organization

- For TAS data science the taxonomy data model plays a pivotal role in TAS Axiomatic AI platform where system learns from given examples and tries to replicate the output in an unseen context

- Another key concept is “context carry-forward”  - as data is processed through taxonomy data model and systems learns to perform task automatically, it is important to record, and index contexts as this context could become vital in providing insightful analytics