Clustering Quality of a High-dimensional Service Monitoring Time-series Dataset

Farzana Anowar, Samira Sadaoui, Hardik Dalal

Abstract: Our study evaluates the quality of a high-dimensional time-series dataset gathered from service observability and monitoring application. We construct the target dataset by extracting heterogeneous sub-datasets from many servers, tackling data incompleteness in each sub-dataset using several imputation techniques, and fusing all the optimally imputed sub-datasets. Based on robust data clustering approaches and metrics, we thoroughly assess the quality of the initial dataset and the reconstructed datasets produced with Deep and Convolutional AutoEncoders. The experiments reveal that the Deep AutoEncoder dataset’s performances outperform the initial dataset’s performances.