## 2 Related Work  
Currently, there is an important trend in using real-time data streams in the oil and gas community [7]. In particular, the research community and the related drilling industry is very active applying new types of data analysis strategies to build decision systems supporting drilling engineers and companies in their daily work. While the main purpose of such supporting systems is an improvement in efficiency and a decrease in drilling costs, real-time data analysis can also be used for the discovery of critical events such as kick, pump startup, or lost circulation. Standard approaches for analysis use different reasoning strategies to detect deviations in physical models. A typical example is early symptom detection using real-time data [8]. This has been the case in this work, i.e., the deviated models have been applied to the detection of several symptoms (e.g., sliding or rotational friction) under real-world conditions. Another interesting real-time system for decision support for high-cost oil-well drilling operations is presented in [9]. \"DrillEdge\" uses case-based reasoning techniques to predict problems and give advice to mitigate them. Although the application of such standard strategies to observations of real-time data streams to predict critical events is clearly feasible, reliable and stable detection and prediction require robust and novel machine learning strategies in order to promote even more reliable and even safer drilling operations. This problem is far from trivial, and to our best knowledge, most of the work using machine learning methods are works in progress for specific events (e.g., stuck pipe) or look at all unusual events. In most cases, the analysis modules or set of analysis modules are only integrated into research prototypes; examples include [10; 11] where stuck pipe events are analyzed. Another general observation of related research is the fact that a single method is not usually sufficient. It is necessary to develop systems that integrate various machine learning methods (with specific advantages and disadvantages) to solve such complex problems. For example, [PERSON] [12] proposes the use of a hybrid system based upon fuzzy logic and neural networks to predict a stuck pipe. A decision support system can use influence diagrams [13] that seem well-suited for real-time support and for large and complex drilling decisions where there is some uncertainty. They used Bayesian Decision Networks. They came to the conclusion that solutions that take into account techniques to determine how strongly the result of an influence diagram computation depends on the values of the observable variables (sensitivity analysis) are very promising. Recently, it also turned out that efficient machine learning strategies allow for the increasing of information content via multiple real-time data stream analysis. Thus, the data cannot only be analyzed on a single rig, but also in real-time operating centers (RTOC), which has several advantages, as discussed by [PERSON] in [14]. Besides the use of simple data-streams, it is also feasible to construct deduced (combined) features in order to enrich the variety of information available for use. Sometimesit is easier for machine learning techniques to work with several \"weak\" features and combine them into a more powerful classifier (ensemble methods and feature selection). Hence, decision makers tend to spend extra effort on constructing new variables (or features) based upon the original variables in order to help make sense of the initial (or raw) measurements [15; 16; 17; 18]. One important solution to address the problem of scalable data processing in real-time is dimensionality reduction [19; 20; 21; 22; 23]. These solutions propose the use of regression-based techniques including Projection Pursuit Regression and Principal Component Analysis for variable transformation. The former technique is one of the most common multivariate regression techniques, while the latter is a special case of the former.