Exploring Scalability in Large-Scale Time Series in DeepVATS framework

TL;DR

This paper presents DeepVATS, a visual analytics tool combining deep learning and interactive visualization for large time series, and evaluates its scalability by analyzing execution times with varying data sizes.

Contribution

It introduces DeepVATS, a novel integrated framework for large-scale time series analysis combining deep learning, storage, and visual analytics modules, and assesses its scalability through empirical log analysis.

Findings

Execution time increases with data size, indicating scalability limits.

The tool effectively visualizes embeddings and time series interactively.

Scalability analysis guides future improvements for large datasets.

Abstract

Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS). It has three interconnected modules. The Deep Learning module, developed in R, manages the load of datasets and Deep Learning models from and to the Storage module. This module also supports models training and the acquisition of the embeddings from the latent space of the trained model. The Storage module operates using the Weights and Biases system. Subsequently, these embeddings can be analyzed in the Visual Analytics module. This module, based on an R Shiny application, allows the adjustment of the parameters related to the projection and clustering of the embeddings space. Once these parameters are set, interactive plots representing both the embeddings, and the time series are shown. This paper introduces the tool and examines its scalability through log analytics. The execution time evolution is examined while the length of the time series is varied. This is achieved by resampling a large data series into smaller subsets and logging the main execution and rendering times for later analysis of scalability.