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Challenges
Usage
Quality
Context
Streaming
Scalability
Data operators Data modalities
Collect Ontologies
Prepare Structured
Represent Networks
Model Text
Reason Multimedia
Visualize Signals
Fig. 4.6 Importance and relevant areas that interact in data mining projects. (Modified
from Leskovec, J., Rajaraman, A., Ullman, J.D., 2014. Mining of Massive Datasets, second ed.,
Cambridge University Press.)
modeling and high-performance computing (HPC), modern data mining is
largely extracting data models or patterns that can sometimes be the sum-
mary of the data or even the set of most extreme features of the data.
The data mining tasks are mainly classified into the following:
• Descriptive methods: where automated and intelligent tools discover pre-
viously unknown human-interpretable patterns that describe the data.
– Example: data clustering of reservoir parameters to identify sweet
spots for new drilling campaigns (Roth et al., 2013).
• Predictive methods: where automated systems [e.g., recommendation sys-
tem (Leskovec et al., 2014; Jordan and Mitchell, 2015)] and models use
certain variables (predictors) to predict unknown future values, trends, or
behavior of other (response) variables.
– Example: well production prediction and optimization (Zhong et al.,
2015) or equipment predictive maintenance, both based on histori-
cally recorded data.
• Root-cause analysis: where automated tools are used to identify roots and
causes of a system’s faults and problems, mostly based on the analysis of
historical categorical, continuous, and temporal data.
– Example: down-time or job-paused time analysis of hydraulic fractur-
ing, well artificial lift, or well stimulation equipment (Maucec
et al., 2015).