---
_id: '48878'
abstract:
- lang: eng
  text: Due to the rise of continuous data-generating applications, analyzing data
    streams has gained increasing attention over the past decades. A core research
    area in stream data is stream classification, which categorizes or detects data
    points within an evolving stream of observations. Areas of stream classification
    are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing
    a wide range of (social) media applications. Research in stream classification
    is related to developing methods that adapt to the changing and potentially volatile
    data stream. It focuses on individual aspects of the stream classification pipeline,
    e.g., designing suitable algorithm architectures, an efficient train and test
    procedure, or detecting so-called concept drifts. As a result of the many different
    research questions and strands, the field is challenging to grasp, especially
    for beginners. This survey explores, summarizes, and categorizes work within the
    domain of stream classification and identifies core research threads over the
    past few years. It is structured based on the stream classification process to
    facilitate coordination within this complex topic, including common application
    scenarios and benchmarking data sets. Thus, both newcomers to the field and experts
    who want to widen their scope can gain (additional) insight into this research
    area and find starting points and pointers to more in-depth literature on specific
    issues and research directions in the field.
author:
- first_name: Lena
  full_name: Clever, Lena
  last_name: Clever
- first_name: Janina Susanne
  full_name: Pohl, Janina Susanne
  last_name: Pohl
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
- first_name: Heike
  full_name: Trautmann, Heike
  last_name: Trautmann
citation:
  ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
    Classification Pipeline: A Literature Review. <i>Applied Sciences</i>. 2022;12(18):9094.
    doi:<a href="https://doi.org/10.3390/app12189094">10.3390/app12189094</a>'
  apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2022).
    Process-Oriented Stream Classification Pipeline: A Literature Review. <i>Applied
    Sciences</i>, <i>12</i>(18), 9094. <a href="https://doi.org/10.3390/app12189094">https://doi.org/10.3390/app12189094</a>'
  bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
    Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={<a href="https://doi.org/10.3390/app12189094">10.3390/app12189094</a>},
    number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital
    Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek,
    Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }'
  chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
    Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
    Review.” <i>Applied Sciences</i> 12, no. 18 (2022): 9094. <a href="https://doi.org/10.3390/app12189094">https://doi.org/10.3390/app12189094</a>.'
  ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
    Stream Classification Pipeline: A Literature Review,” <i>Applied Sciences</i>,
    vol. 12, no. 18, p. 9094, 2022, doi: <a href="https://doi.org/10.3390/app12189094">10.3390/app12189094</a>.'
  mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
    Review.” <i>Applied Sciences</i>, vol. 12, no. 18, {Multidisciplinary Digital
    Publishing Institute}, 2022, p. 9094, doi:<a href="https://doi.org/10.3390/app12189094">10.3390/app12189094</a>.'
  short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
    12 (2022) 9094.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:50:56Z
department:
- _id: '819'
doi: 10.3390/app12189094
intvolume: '        12'
issue: '18'
keyword:
- big data
- data mining
- data stream analysis
- machine learning
- stream classification
- supervised learning
language:
- iso: eng
page: '9094'
publication: Applied Sciences
publication_identifier:
  issn:
  - 2076-3417
publisher: '{Multidisciplinary Digital Publishing Institute}'
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '102979'
volume: 12
year: '2022'
...