Summary of Exploring Scalability in Large-scale Time Series in Deepvats Framework, by Inmaculada Santamaria-valenzuela et al.
Exploring Scalability in Large-Scale Time Series in DeepVATS framework
by Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, David Camacho
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary DeepVATS, a tool that combines Deep Learning and Visual Analytics, enables insightful analysis of large time series datasets. The tool consists of three interconnected modules: Deep Learning, Storage, and Visual Analytics. The R-based Deep Learning module trains models and manages data loading and embedding acquisition from the latent space. The Storage module utilizes Weights and Biases, while the Visual Analytics module, built on an R Shiny application, allows users to adjust parameters for projection and clustering of embeddings. Interactive plots visualizing both embeddings and time series are generated. This paper introduces DeepVATS and evaluates its scalability through log analytics, examining execution time evolution as the length of the time series is varied by resampling a large dataset into smaller subsets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpower that lets you see hidden patterns in big datasets! That’s what Visual Analytics can do. It helps us understand trends and insights from huge amounts of data over time. A new tool called DeepVATS combines these powers with another powerful technique, Deep Learning. This tool has three parts: one that trains models, one that stores the data, and one that shows you cool visualizations. The tool is super fast and can handle really big datasets! In this paper, scientists tested how well it works when dealing with massive amounts of data. |
Keywords
* Artificial intelligence * Clustering * Deep learning * Embedding * Latent space * Time series
