Event Analytics Service¶
The Event Analytics Service provides the essential functionality for a data-driven analysis of event data. It enables the user to get a better grasp of what is happening inside the system through statistical analysis.
The service enables the user to run the following applications:
- Alarm management
- System troubleshooting
- Root-cause analysis: identification of the root causes of faults or problems
Almost any programmable device emits event data, containing a reasonable message text with a corresponding time stamp of the occurring events. Event data can be leveraged in order to create a new form of economic value. By analyzing it, we can extract additional knowledge and uncover useful patterns and hidden relationships between the emitted events. In return, it can not only help us to better understand the system's behaviour and dynamics, but also drastically increase the effectiveness of system management and support reasoning.
The service processes event data sets as tuples of the data types time and string. An analytic session should have a maximum data volume of 1 MB and at least 1000 events.
The statistical analysis enables the user to identify statistically significant dependencies by displaying the most frequently occurring events (Top Events). It is a reasonable assumption that those events are of the highest interest to the user. This information can be used to determine which data is useful for further analysis (e.g., by passing the data to future Event Analytics endpoints). It also helps to reduce the cost of further analysis, as well as increase the algorithm performance (both in terms of accuracy and time required).
The analysis takes the entire data set as input, sorts it according to the number of identical events and returns a sorted list of events with the corresponding number.
The Event Analytics Service exposes its API for realizing the following tasks:
- Find most frequently occurring events.
The manager of a brewery wants to know a possible reason for the repeatedly unexpected behaviour of his production line.
Collect all event data between successive unexpected behaviours of the production line. Feed the Event Analytics Service API with these events and evaluate the analysis.
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