Enhancing the Situation Awareness of Decision Makers by Applying Case-Based Reasoning on Streaming Data
Doctoral thesis
Permanent lenke
http://hdl.handle.net/11250/279021Utgivelsesdato
2014Metadata
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Sammendrag
Data is generally expected to continue its exponential growth the next five to ten
years. However, it is commonly agreed that the amount of data that currently
exist is abundant and that there is still much to achieve with it. The industry, in
general, has recognized both the growth and the need to analyze the data. This
is also the case with oil well drilling operations. Currently, operators monitor oil
well drilling operations manually by staring at real-time measurements visualized
in a computer program as graphs. Having humans monitoring the drilling
operations manually has its disadvantages, as people get bored, tired and distracted.
In this thesis, we investigate whether real-time decision making can be improved
by enhancing the decision maker’s situation awareness through applying
case-based reasoning on streaming data. This thesis is not about automating decisions,
but informing human decision makers about the current situation so that
they can make an informed decision. A hybrid reasoning system that abstracts
recognize symptoms in time-series data and describe the current situation using
these is described. The current situation is compared to past problematic situations
and the similar past situations are brought to the attention of the decision
maker to support decisions. Furthermore, situation assessment, the process of
acquiring an understanding of the current state of a situation, in oil well drilling
is analyzed and described.
There are four main contributions of the research effort presented in this thesis:
(i) a case representation for drilling situations; (ii) a hybrid reasoning architecture
that is capable of reasoning with real-time data streams; (iii) a similarity
metric for sequences of complex events; and (iv) a knowledge level model of situations
and situation assessment.