Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning

Rainer Meindl; Simone Sandler; Elisabeth Mayrhuber; Oliver Krauss

doi:10.46354/i3m.2022.emss.022

Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning

Rainer Meindl, Simone Sandler, Elisabeth Mayrhuber, Oliver Krauss

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

Fitting real world data into a model for classification, is a challenging task. Modern approaches to classification are often resource intensive and may become bottlenecks. A microservice architecture that allows maintaining a model of real world data, and adding new information as it becomes available is presented in this paper. Updates to the model are handled via different microservices. The architecture and connected workflows are demonstrated in a use case of classifying text data in a taxonomy represented by a directed acyclic graph (DAG). The presented architecture removes the classification bottleneck, as multiple data points can be added independent of each other, and reading access to the model is not restricted. Additional microservices also enable a manual intervention to update the model.

Original language	English
Title of host publication	34th European Modeling and Simulation Symposium, EMSS 2022
Editors	Michael Affenzeller, Agostino G. Bruzzone, Emilio Jimenez, Francesco Longo, Antonella Petrillo
Publisher	DIME UNIVERSITY OF GENOA
ISBN (Electronic)	9788885741737
DOIs	https://doi.org/10.46354/i3m.2022.emss.022
Publication status	Published - 2022
Event	34th European Modeling and Simulation Symposium, EMSS 2022 - Rome, Italy Duration: 19 Sept 2022 → 21 Sept 2022

Publication series

Name	34th European Modeling and Simulation Symposium, EMSS 2022

Conference

Conference	34th European Modeling and Simulation Symposium, EMSS 2022
Country/Territory	Italy
City	Rome
Period	19.09.2022 → 21.09.2022

Keywords

Distributed Environment
Microservice Architecture
Semi-Supervised Learning
Text Classification

Access to Document

10.46354/i3m.2022.emss.022

Cite this

Meindl, R., Sandler, S., Mayrhuber, E., & Krauss, O. (2022). Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning. In M. Affenzeller, A. G. Bruzzone, E. Jimenez, F. Longo, & A. Petrillo (Eds.), 34th European Modeling and Simulation Symposium, EMSS 2022 Article 022 (34th European Modeling and Simulation Symposium, EMSS 2022). DIME UNIVERSITY OF GENOA. https://doi.org/10.46354/i3m.2022.emss.022

Meindl, Rainer ; Sandler, Simone ; Mayrhuber, Elisabeth et al. / Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning. 34th European Modeling and Simulation Symposium, EMSS 2022. editor / Michael Affenzeller ; Agostino G. Bruzzone ; Emilio Jimenez ; Francesco Longo ; Antonella Petrillo. DIME UNIVERSITY OF GENOA, 2022. (34th European Modeling and Simulation Symposium, EMSS 2022).

@inproceedings{03904b351b1e452aaa7e86c20955cdb6,

title = "Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning",

abstract = "Fitting real world data into a model for classification, is a challenging task. Modern approaches to classification are often resource intensive and may become bottlenecks. A microservice architecture that allows maintaining a model of real world data, and adding new information as it becomes available is presented in this paper. Updates to the model are handled via different microservices. The architecture and connected workflows are demonstrated in a use case of classifying text data in a taxonomy represented by a directed acyclic graph (DAG). The presented architecture removes the classification bottleneck, as multiple data points can be added independent of each other, and reading access to the model is not restricted. Additional microservices also enable a manual intervention to update the model.",

keywords = "Distributed Environment, Microservice Architecture, Semi-Supervised Learning, Text Classification",

author = "Rainer Meindl and Simone Sandler and Elisabeth Mayrhuber and Oliver Krauss",

note = "Publisher Copyright: {\textcopyright} 2022 The Authors.; 34th European Modeling and Simulation Symposium, EMSS 2022 ; Conference date: 19-09-2022 Through 21-09-2022",

year = "2022",

doi = "10.46354/i3m.2022.emss.022",

language = "English",

series = "34th European Modeling and Simulation Symposium, EMSS 2022",

publisher = "DIME UNIVERSITY OF GENOA",

editor = "Michael Affenzeller and Bruzzone, {Agostino G.} and Emilio Jimenez and Francesco Longo and Antonella Petrillo",

booktitle = "34th European Modeling and Simulation Symposium, EMSS 2022",

}

Meindl, R, Sandler, S , Mayrhuber, E & Krauss, O 2022, Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning. in M Affenzeller, AG Bruzzone, E Jimenez, F Longo & A Petrillo (eds), 34th European Modeling and Simulation Symposium, EMSS 2022., 022, 34th European Modeling and Simulation Symposium, EMSS 2022, DIME UNIVERSITY OF GENOA, 34th European Modeling and Simulation Symposium, EMSS 2022, Rome, Italy, 19.09.2022. https://doi.org/10.46354/i3m.2022.emss.022

Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning. / Meindl, Rainer; Sandler, Simone ; Mayrhuber, Elisabeth et al.
34th European Modeling and Simulation Symposium, EMSS 2022. ed. / Michael Affenzeller; Agostino G. Bruzzone; Emilio Jimenez; Francesco Longo; Antonella Petrillo. DIME UNIVERSITY OF GENOA, 2022. 022 (34th European Modeling and Simulation Symposium, EMSS 2022).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning

AU - Meindl, Rainer

AU - Sandler, Simone

AU - Mayrhuber, Elisabeth

AU - Krauss, Oliver

PY - 2022

Y1 - 2022

N2 - Fitting real world data into a model for classification, is a challenging task. Modern approaches to classification are often resource intensive and may become bottlenecks. A microservice architecture that allows maintaining a model of real world data, and adding new information as it becomes available is presented in this paper. Updates to the model are handled via different microservices. The architecture and connected workflows are demonstrated in a use case of classifying text data in a taxonomy represented by a directed acyclic graph (DAG). The presented architecture removes the classification bottleneck, as multiple data points can be added independent of each other, and reading access to the model is not restricted. Additional microservices also enable a manual intervention to update the model.

AB - Fitting real world data into a model for classification, is a challenging task. Modern approaches to classification are often resource intensive and may become bottlenecks. A microservice architecture that allows maintaining a model of real world data, and adding new information as it becomes available is presented in this paper. Updates to the model are handled via different microservices. The architecture and connected workflows are demonstrated in a use case of classifying text data in a taxonomy represented by a directed acyclic graph (DAG). The presented architecture removes the classification bottleneck, as multiple data points can be added independent of each other, and reading access to the model is not restricted. Additional microservices also enable a manual intervention to update the model.

KW - Distributed Environment

KW - Microservice Architecture

KW - Semi-Supervised Learning

KW - Text Classification

UR - http://www.scopus.com/inward/record.url?scp=85142901943&partnerID=8YFLogxK

U2 - 10.46354/i3m.2022.emss.022

DO - 10.46354/i3m.2022.emss.022

M3 - Conference contribution

AN - SCOPUS:85142901943

T3 - 34th European Modeling and Simulation Symposium, EMSS 2022

BT - 34th European Modeling and Simulation Symposium, EMSS 2022

A2 - Affenzeller, Michael

A2 - Bruzzone, Agostino G.

A2 - Jimenez, Emilio

A2 - Longo, Francesco

A2 - Petrillo, Antonella

PB - DIME UNIVERSITY OF GENOA

T2 - 34th European Modeling and Simulation Symposium, EMSS 2022

Y2 - 19 September 2022 through 21 September 2022

ER -

Meindl R, Sandler S , Mayrhuber E , Krauss O. Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning. In Affenzeller M, Bruzzone AG, Jimenez E, Longo F, Petrillo A, editors, 34th European Modeling and Simulation Symposium, EMSS 2022. DIME UNIVERSITY OF GENOA. 2022. 022. (34th European Modeling and Simulation Symposium, EMSS 2022). doi: 10.46354/i3m.2022.emss.022

Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

34th European Modeling and Simulation Symposium, EMSS 2022

Cite this

Distributed Classification - A Scalable Approach to Semi Supervised Machine Learning

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Activities

34th European Modeling and Simulation Symposium, EMSS 2022

Cite this