@article{48878,
  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\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       = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}},
  issn         = {{2076-3417}},
  journal      = {{Applied Sciences}},
  keywords     = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}},
  number       = {{18}},
  pages        = {{9094}},
  publisher    = {{{Multidisciplinary Digital Publishing Institute}}},
  title        = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}},
  doi          = {{10.3390/app12189094}},
  volume       = {{12}},
  year         = {{2022}},
}

@article{17156,
  abstract     = {{Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.}},
  author       = {{Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio, Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and Matzner, Martin and Mendling, Jan and Ogonek, Nadine and Post, Till and Resinas, Manuel and Revoredo, Kate and del-Río-Ortega, Adela and La Rosa, Marcello and Santoro, Flávia Maria and Solti, Andreas and Song, Minseok and Stein, Armin and Stierle, Matthias and Wolf, Verena}},
  issn         = {{2363-7005}},
  journal      = {{Business & Information Systems Engineering}},
  keywords     = {{Business process management (BPM), Social computing, Smart devices, Big data analytics, Real-time computing, BPM life-cycle}},
  pages        = {{145--156}},
  publisher    = {{SpringerNature}},
  title        = {{{Seven Paradoxes of Business Process Management in a Hyper-Connected World}}},
  doi          = {{10.1007/s12599-020-00646-z}},
  volume       = {{63}},
  year         = {{2020}},
}

@article{5910,
  author       = {{Heinemann, Birte and Opel, Simone and Budde, Lea and Schulte, Carsten and Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Wassong, Thomas}},
  isbn         = {{9781450365352}},
  journal      = {{Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling '18}},
  keywords     = {{Curriculum Development, Data Science, Secondary Sc, acm reference format, big data, curriculum development, data literacy, data science, educa-, interdisciplinary, secondary schools, tion}},
  number       = {{17}},
  pages        = {{1--5}},
  title        = {{{Drafting a Data Science Curriculum for Secondary Schools}}},
  doi          = {{10.1145/3279720.3279737}},
  year         = {{2018}},
}

@article{4689,
  author       = {{Müller, Oliver and Junglas, Iris and vom Brocke, Jan and Debortoli, Stefan}},
  isbn         = {{0960-085X}},
  issn         = {{14769344}},
  journal      = {{European Journal of Information Systems}},
  keywords     = {{analytics, big data, data source, information systems research, methodology}},
  number       = {{4}},
  pages        = {{289----302}},
  title        = {{{Utilizing big data analytics for information systems research: Challenges, promises and guidelines}}},
  doi          = {{10.1057/ejis.2016.2}},
  year         = {{2016}},
}

@article{4695,
  author       = {{Debortoli, Stefan and Müller, Oliver and vom Brocke, Jan}},
  isbn         = {{0910-8327 (Print)$\backslash$n0910-8327 (Linking)}},
  issn         = {{18670202}},
  journal      = {{Business and Information Systems Engineering}},
  keywords     = {{Big data, Business intelligence, Competencies, Latent semantic analysis, Text mining}},
  number       = {{5}},
  pages        = {{289----300}},
  title        = {{{Comparing business intelligence and big data skills: A text mining study using job advertisements}}},
  doi          = {{10.1007/s12599-014-0344-2}},
  year         = {{2014}},
}

@article{4696,
  author       = {{vom Brocke, Jan and Debortoli, Stefan and Reuter, Nadine and Müller, Oliver}},
  issn         = {{15293181}},
  journal      = {{Communications of the Association for Information Systems}},
  keywords     = {{Advanced business analytics, Big Data, Business intelligence, IT business value, In-memory technology, OLAP, OLTP, Realtime analytics, Sentiment analysis}},
  pages        = {{151----167}},
  title        = {{{How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti}}},
  doi          = {{10.17705/1CAIS.03407}},
  year         = {{2014}},
}