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- Evaluation of response to
last action
- Update of analysis model
Action
Analysis
- Evaluation of analysis model
- Derivation of suitable action
Fig. 1.1 Realtime analytics as interplay of analysis and action
Before we look at the new approach in more detail, it is worth mentioning that
adaptive behavior is a mega-trend at present, not just in data mining but in many
scientific disciplines. Examples include adaptive finite element methods to solve
partial differential equations, adaptive control systems in production, and adaptive
e-learning in education.
1.3 Advantages of Realtime Analytics Systems
Let's begin by discussing the general advantages and disadvantages of (adaptive)
realtime analytics systems. The advantages are higher quality, fewer statistical
conditions required, immediate adaptation to a changed environment, and no
storage of historical data necessary.
The first of these, higher quality, is the most important. Whereas classical data
mining is based exclusively on the analysis of historical data, the realtime analytics
paradigm is aimed at the
interplay
of analysis and action. This requires an entirely
new way of thinking and a new theoretical foundation. This foundation is reminis-
cent of the cybernetic approach and is based on
control theory
.
The common modeling of analysis and action is more than merely the sum of its
parts. Electromagnetic waves are a graphic example of this (Fig.
1.2
). These waves
are based on the interplay of an electrical and a magnetic field, as defined by
Maxwell's equations: the change in the electrical field over time is always associ-
ated with a spatial change in the magnetic field. Likewise, the change in the
magnetic field over time is associated in turn with a spatial change in the electrical
field. The result is a continuous wave of unsurpassed speed - that of light.
The same is true of the interplay of analysis and action: the results of the analysis
lead to improved actions (e.g., recommendations), which are instantly applied and
