I4TSPS: a Visual-Interactive Web System for Industrial Time-Series Pre-processing

Data pre-processing in smart manufacturing scenarios implies that data engineers must have knowledge of many different techniques necessary for example to clean, segment, normalize and to efficiently store time-series data. However, for many data engineers often results difficult to be aware of the most adequate pre-processing techniques due to the heterogeneous time-series data they must deal with and the variety of existing techniques. In this paper, a visual-interactive web system that facilitates the pre-processing task to data engineers is presented. On the one hand, data engineers can use it to select an adequate technology for detecting and handling outliers, to remove noise and to impute missing values in time series. Moreover, the system provides some recommendations to select the appropriate techniques and parameter values required by them. On the other hand, the system includes a machine learning module that suggests the most appropriate reduced representations in order to efficiently store those time series. The system has been tested with real time series of two different manufacturing companies.