Semantic of Cloud Computing services for Time Series workflows
- URL: http://arxiv.org/abs/2202.00609v1
- Date: Tue, 1 Feb 2022 17:57:40 GMT
- Title: Semantic of Cloud Computing services for Time Series workflows
- Authors: Manuel Parra-Roy\'on, Francisco Baldan, Ghislain Atemezing, J.M.
Benitez
- Abstract summary: 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.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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