Process-Oriented Stream Classification Pipeline: A Literature Review

Applied Sciences, vol. 12, pp. 9094

Abstract

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—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.

Authors 5

1. Lena Clever corresponding

   University of Münster

   Affiliation as printed

   Department of Information Systems, University of Münster, 48149 Münster, Germany

2. Janina Pohl

   University of Münster

   Affiliation as printed

   Department of Information Systems, University of Münster, 48149 Münster, Germany

3. Jakob Bossek Aachen Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Department of Computer Science, RWTH Aachen University, 52062 Aachen, Germany

4. Pascal Kerschke

   Fakultät Verkehrswissenschaften "Friedrich List" · Technische Universität Dresden

   Affiliation as printed

   “Friedrich List” Faculty of Transport and Traffic Sciences, TU Dresden, 01062 Dresden, Germany

   "Friedrich List" Faculty of Transport and Traffic Sciences, TU Dresden, 01062 Dresden, Germany

5. Heike Trautmann

   University of Münster · University of Twente

   Affiliation as printed

   Data Management & Biometrics Group, University of Twente, 7522 NB Enschede, The Netherlands

   Department of Information Systems, University of Münster, 48149 Münster, Germany