Semantic of Cloud Computing services for Time Series workflows

TL;DR

This paper proposes a semantic modeling approach to describe and deploy Time Series workflows as cloud services, enhancing portability and reducing complexity in cloud environments.

Contribution

It introduces a comprehensive semantic vocabulary for modeling Time Series workflows as cloud services, covering various operations and machine learning tasks.

Findings

Semantic modeling enables effective deployment of TS workflows in cloud environments.

The proposed approach supports classification, regression, and clustering workflows.

It facilitates integration and reduces complexity in cloud-based TS analysis.

Abstract

Time series (TS) are present in many fields of knowledge, research, and engineering. The processing and analysis of TS are essential in order to extract knowledge from the data and to tackle forecasting or predictive maintenance tasks among others The modeling of TS is a challenging task, requiring high statistical expertise as well as outstanding knowledge about the application of Data Mining(DM) and Machine Learning (ML) methods. The overall work with TS is not limited to the linear application of several techniques, but is composed of an open workflow of methods and tests. These workflow, developed mainly on programming languages, are complicated to execute and run effectively on different systems, including Cloud Computing (CC) environments. The adoption of CC can facilitate the integration and portability of services allowing to adopt solutions towards services Internet Technologies (IT) industrialization. The definition and description of workflow services for TS open up a new set of possibilities regarding the reduction of complexity in the deployment of this type of issues in CC environments. In this sense, we have designed an effective proposal based on semantic modeling (or vocabulary) that provides the full description of workflow for Time Series modeling as a CC service. Our proposal includes a broad spectrum of the most extended operations, accommodating any workflow applied to classification, regression, or clustering problems for Time Series, as well as including evaluation measures, information, tests, or machine learning algorithms among others.