We cannot escape the failures arising from the data applications no matter what we try; however, applying sound measures to spot the anomalies is the best way to go.

Whether in manufacturing or in networking, chance of facing failures is always at stake. Even though the fact that all comprehensive planning and projections, a single thing that doesn’t go as planned would deteriorate the whole process from the heart. The failures as in our context refers to the events that are happening unparallel to initial plans. For example, a network system is expected to run without interruptions. Hence, a network system’s failure is an interruption of the normal flow of working – say, a server breakdown. On the other hand, in manufacturing, things are similar. For instance, an automated machine designated on a specific job is expected to continue its process. Normal wear and tear of the components may cause the machine to work absurdly or not work at all, which is called – failure.

Series of problems arose due to lack of efficiently examining former failures and the chance of future failures. Make it manufacture or network, failures may be threatening although they are not even recurring. Wear and tear of the machinery can cause disruptions in manufacturing line while unexpected server breakdowns may occur due to not taking the correct steps for normal activities. Answers to why we need to spot anomalies reveal as we are discussing the reasons behind failures in both sectors. Unplanned actions and unexpected changes in the process may posses a risk of financial loss. Managers should plan every other alternative to be ready for all kinds of surprises, nonetheless, unless a plan includes sound projection of possible failures (nearly impossible), there would be surprises. However, specific failures occurring on systems generally have a unique pattern. Those unique patterns of actions happening right before failures can be collected to be processed into something more meaningful.

Answer is of course machine learning. Machine learning is the number one selection if a correlation between parameters are searched. That is, the sequences of events resulting in failures form a correlation bond between numbers of failures observed and specific parameters. Thus, having a control parameter at hand (failure) and an experiment group to observe (parameters) is more than enough to utilize machine learning to that event. In fact, failures would signal their arrival beforehand. Those signals are ranged from data fluctuations to missing parameters; hence, applying machine learning almost always points out those signals to warn other about upcoming failures. Therefore, machine learning does not only understand if something is a failure or not, but it also gives hints at future probable failures in the system.

How machine learning works in anomaly spotting? In order to understand it from the side of machine learning, one must understand the contextual form. That is, anomalies are broadly categorized as

  1. Point Anomalies: A single situation (an outlier) observed far from the rest of the data
  2. Contextual Anomalies: Observed in time-series data, an occurrence where fitness of data is odd in terms of common context.
  3. Collective Anomalies: A situation where data is behaving oddly in every level.

Thus, the most common way to identify anomalies in a statistical set is to select data points deviating from table statistics values such as mean, median, mode etc. For instance, defining a point as an anomaly means that that point is deviating at a certain degree from chosen property. For example, variation of standard deviation from the mean signals an anomalous data.

Pure statistics based approach is, although helpful, not comprehensive enough to be significant in all cases. In terms of seasonality and noisy behavior, those methods become obsolete. Thanks to the merits of 21st century, machine learning is there to help us in our journey of spotting anomalies. There are some methods in machine learning applications that are being used in finding anomalies. In our use cases we have shown that spotting anomalies beforehand is simple with Enhencer as it utilizes the best methods in machine learning realm. Below you can see the brief overviews of the machine learning based anomaly detection approaches.

  1. Density-Based Anomaly Detection: key assumption in this method is that normal behaving data stay around a dense neighborhood whereas anomalies occur far away from the neighborhoods. K-nearest neighbor and Relative Density of Data algorithms are used in that method to classify data based on their similarities and reachabilities, then uses distance metrics to reveal abnormalities.
  2. Clustering-Based Anomaly Detection: In case of unsupervised learning, clustering is the most widespread alternative. The key assumption here is that data points tend to belong in similar groups if they have the similar characteristics in distance from local ultimate points (centroids). K-Means is the most popular clustering algorithm. It creates “k” similar clusters from data points that tend to get in groups. Points falling outside of the clusters marked as anomalies.

To conclude, whether the sector or the method of approach, pointing out anomalies has a huge value proposition for the ones practicing it. Enhencer, utilizes machine learning algorithms of all kind to assist you for early failure detection. As constantly observing data and knowing when to have an anomaly can only be endorsed vis machine learning, we are encouraging you to visit our use cases to see it with your eyes!

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