This paper presents a framework for the collection, management and mining of sensor
data in large cyber-physical systems. Particular emphasis has been placed on mathematical
methods, data structures and implementations which enable the real-time solution of inverse
problems associated with the system in question. That is, given a system model, to obtain
an estimate for the phenomenological cause of the sensor observation. This enables the use
of causality, rather than mere correlation, when computing measures of signicance during
machine learning and knowledge discovery in very large data sets. The model is an abstract
representation of a real physical system establishing the relationships between cause and eects.
The pertinent behaviour of the model is captured in the form of equations, e.g., dierential
equations. The inverse solution of these model-equations, within certain constraints, permit us
to establish the semantic reference between the sensor observation and its cause. Without this
semantic reference there can be no physically based knowledge discovery.
Embrechts pyramid of knowledge is addressed and shown that it will not suce for future
developments. The issue of information content is addressed more formally than in most data
mining literature. Additionally the Epistemology for the emergent-perceptive portion of speech
is presented and a prototype implementation with experimental results in data mining are
presented. A lexical symbolic analysis of sensor data is implemented