--- _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. Applied Sciences. 2022;12(18):9094. doi:10.3390/app12189094' apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094' bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094}, 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.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.' ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” Applied Sciences, vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.' mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.' 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' ... --- _id: '5910' author: - first_name: Birte full_name: Heinemann, Birte last_name: Heinemann - first_name: Simone full_name: Opel, Simone last_name: Opel - first_name: Lea full_name: Budde, Lea last_name: Budde - first_name: Carsten full_name: Schulte, Carsten last_name: Schulte - first_name: Daniel full_name: Frischemeier, Daniel last_name: Frischemeier - first_name: Rolf full_name: Biehler, Rolf last_name: Biehler - first_name: Susanne full_name: Podworny, Susanne last_name: Podworny - first_name: Thomas full_name: Wassong, Thomas id: '21241' last_name: Wassong citation: ama: Heinemann B, Opel S, Budde L, et al. Drafting a Data Science Curriculum for Secondary Schools. Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18. 2018;(17):1-5. doi:10.1145/3279720.3279737 apa: Heinemann, B., Opel, S., Budde, L., Schulte, C., Frischemeier, D., Biehler, R., … Wassong, T. (2018). Drafting a Data Science Curriculum for Secondary Schools. Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18, (17), 1–5. https://doi.org/10.1145/3279720.3279737 bibtex: '@article{Heinemann_Opel_Budde_Schulte_Frischemeier_Biehler_Podworny_Wassong_2018, title={Drafting a Data Science Curriculum for Secondary Schools}, DOI={10.1145/3279720.3279737}, number={17}, journal={Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18}, author={Heinemann, Birte and Opel, Simone and Budde, Lea and Schulte, Carsten and Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Wassong, Thomas}, year={2018}, pages={1–5} }' chicago: 'Heinemann, Birte, Simone Opel, Lea Budde, Carsten Schulte, Daniel Frischemeier, Rolf Biehler, Susanne Podworny, and Thomas Wassong. “Drafting a Data Science Curriculum for Secondary Schools.” Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18, no. 17 (2018): 1–5. https://doi.org/10.1145/3279720.3279737.' ieee: B. Heinemann et al., “Drafting a Data Science Curriculum for Secondary Schools,” Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18, no. 17, pp. 1–5, 2018. mla: Heinemann, Birte, et al. “Drafting a Data Science Curriculum for Secondary Schools.” Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18, no. 17, 2018, pp. 1–5, doi:10.1145/3279720.3279737. short: B. Heinemann, S. Opel, L. Budde, C. Schulte, D. Frischemeier, R. Biehler, S. Podworny, T. Wassong, Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling ’18 (2018) 1–5. date_created: 2018-11-27T10:44:10Z date_updated: 2022-01-06T07:02:47Z doi: 10.1145/3279720.3279737 issue: '17' keyword: - Curriculum Development - Data Science - Secondary Sc - acm reference format - big data - curriculum development - data literacy - data science - educa- - interdisciplinary - secondary schools - tion language: - iso: eng page: 1-5 publication: Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling '18 publication_identifier: isbn: - '9781450365352' publication_status: published status: public title: Drafting a Data Science Curriculum for Secondary Schools type: journal_article user_id: '21241' year: '2018' ... --- _id: '4689' author: - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller - first_name: Iris full_name: Junglas, Iris last_name: Junglas - first_name: Jan full_name: vom Brocke, Jan last_name: vom Brocke - first_name: Stefan full_name: Debortoli, Stefan last_name: Debortoli citation: ama: 'Müller O, Junglas I, vom Brocke J, Debortoli S. Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems. 2016;(4):289--302. doi:10.1057/ejis.2016.2' apa: 'Müller, O., Junglas, I., vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems, (4), 289--302. https://doi.org/10.1057/ejis.2016.2' bibtex: '@article{Müller_Junglas_vom Brocke_Debortoli_2016, title={Utilizing big data analytics for information systems research: Challenges, promises and guidelines}, DOI={10.1057/ejis.2016.2}, number={4}, journal={European Journal of Information Systems}, author={Müller, Oliver and Junglas, Iris and vom Brocke, Jan and Debortoli, Stefan}, year={2016}, pages={289--302} }' chicago: 'Müller, Oliver, Iris Junglas, Jan vom Brocke, and Stefan Debortoli. “Utilizing Big Data Analytics for Information Systems Research: Challenges, Promises and Guidelines.” European Journal of Information Systems, no. 4 (2016): 289--302. https://doi.org/10.1057/ejis.2016.2.' ieee: 'O. Müller, I. Junglas, J. vom Brocke, and S. Debortoli, “Utilizing big data analytics for information systems research: Challenges, promises and guidelines,” European Journal of Information Systems, no. 4, pp. 289--302, 2016.' mla: 'Müller, Oliver, et al. “Utilizing Big Data Analytics for Information Systems Research: Challenges, Promises and Guidelines.” European Journal of Information Systems, no. 4, 2016, pp. 289--302, doi:10.1057/ejis.2016.2.' short: O. Müller, I. Junglas, J. vom Brocke, S. Debortoli, European Journal of Information Systems (2016) 289--302. date_created: 2018-10-12T08:29:46Z date_updated: 2022-01-06T07:01:18Z doi: 10.1057/ejis.2016.2 extern: '1' issue: '4' keyword: - analytics - big data - data source - information systems research - methodology language: - iso: eng page: 289--302 publication: European Journal of Information Systems publication_identifier: isbn: - 0960-085X issn: - '14769344' status: public title: 'Utilizing big data analytics for information systems research: Challenges, promises and guidelines' type: journal_article user_id: '72849' year: '2016' ... --- _id: '4695' author: - first_name: Stefan full_name: Debortoli, Stefan last_name: Debortoli - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller - first_name: Jan full_name: vom Brocke, Jan last_name: vom Brocke citation: ama: 'Debortoli S, Müller O, vom Brocke J. Comparing business intelligence and big data skills: A text mining study using job advertisements. Business and Information Systems Engineering. 2014;(5):289--300. doi:10.1007/s12599-014-0344-2' apa: 'Debortoli, S., Müller, O., & vom Brocke, J. (2014). Comparing business intelligence and big data skills: A text mining study using job advertisements. Business and Information Systems Engineering, (5), 289--300. https://doi.org/10.1007/s12599-014-0344-2' bibtex: '@article{Debortoli_Müller_vom Brocke_2014, title={Comparing business intelligence and big data skills: A text mining study using job advertisements}, DOI={10.1007/s12599-014-0344-2}, number={5}, journal={Business and Information Systems Engineering}, author={Debortoli, Stefan and Müller, Oliver and vom Brocke, Jan}, year={2014}, pages={289--300} }' chicago: 'Debortoli, Stefan, Oliver Müller, and Jan vom Brocke. “Comparing Business Intelligence and Big Data Skills: A Text Mining Study Using Job Advertisements.” Business and Information Systems Engineering, no. 5 (2014): 289--300. https://doi.org/10.1007/s12599-014-0344-2.' ieee: 'S. Debortoli, O. Müller, and J. vom Brocke, “Comparing business intelligence and big data skills: A text mining study using job advertisements,” Business and Information Systems Engineering, no. 5, pp. 289--300, 2014.' mla: 'Debortoli, Stefan, et al. “Comparing Business Intelligence and Big Data Skills: A Text Mining Study Using Job Advertisements.” Business and Information Systems Engineering, no. 5, 2014, pp. 289--300, doi:10.1007/s12599-014-0344-2.' short: S. Debortoli, O. Müller, J. vom Brocke, Business and Information Systems Engineering (2014) 289--300. date_created: 2018-10-12T08:30:31Z date_updated: 2022-01-06T07:01:18Z doi: 10.1007/s12599-014-0344-2 extern: '1' issue: '5' keyword: - Big data - Business intelligence - Competencies - Latent semantic analysis - Text mining language: - iso: eng page: 289--300 publication: Business and Information Systems Engineering publication_identifier: isbn: - 0910-8327 (Print)$\backslash$n0910-8327 (Linking) issn: - '18670202' status: public title: 'Comparing business intelligence and big data skills: A text mining study using job advertisements' type: journal_article user_id: '72849' year: '2014' ... --- _id: '4696' author: - first_name: Jan full_name: vom Brocke, Jan last_name: vom Brocke - first_name: Stefan full_name: Debortoli, Stefan last_name: Debortoli - first_name: Nadine full_name: Reuter, Nadine last_name: Reuter - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller citation: ama: 'vom Brocke J, Debortoli S, Reuter N, Müller O. How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti. Communications of the Association for Information Systems. 2014:151--167. doi:10.17705/1CAIS.03407' apa: 'vom Brocke, J., Debortoli, S., Reuter, N., & Müller, O. (2014). How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti. Communications of the Association for Information Systems, 151--167. https://doi.org/10.17705/1CAIS.03407' bibtex: '@article{vom Brocke_Debortoli_Reuter_Müller_2014, title={How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti}, DOI={10.17705/1CAIS.03407}, journal={Communications of the Association for Information Systems}, author={vom Brocke, Jan and Debortoli, Stefan and Reuter, Nadine and Müller, Oliver}, year={2014}, pages={151--167} }' chicago: 'Brocke, Jan vom, Stefan Debortoli, Nadine Reuter, and Oliver Müller. “How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti.” Communications of the Association for Information Systems, 2014, 151--167. https://doi.org/10.17705/1CAIS.03407.' ieee: 'J. vom Brocke, S. Debortoli, N. Reuter, and O. Müller, “How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti,” Communications of the Association for Information Systems, pp. 151--167, 2014.' mla: 'vom Brocke, Jan, et al. “How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti.” Communications of the Association for Information Systems, 2014, pp. 151--167, doi:10.17705/1CAIS.03407.' short: J. vom Brocke, S. Debortoli, N. Reuter, O. Müller, Communications of the Association for Information Systems (2014) 151--167. date_created: 2018-10-12T08:30:38Z date_updated: 2022-01-06T07:01:18Z doi: 10.17705/1CAIS.03407 extern: '1' keyword: - Advanced business analytics - Big Data - Business intelligence - IT business value - In-memory technology - OLAP - OLTP - Realtime analytics - Sentiment analysis language: - iso: eng page: 151--167 publication: Communications of the Association for Information Systems publication_identifier: issn: - '15293181' status: public title: 'How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti' type: journal_article user_id: '72849' year: '2014' ...