- (Krempl et al., 2014) ⇒ Georg Krempl, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, and Jerzy Stefanowski. (2014). “Open Challenges for Data Stream Mining Research.” In: ACM SIGKDD Explorations Newsletter Journal, 16(1). doi:10.1145/2674026.2674028
Subject Headings: Data Stream Mining.
Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application - relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.
|2014 OpenChallengesforDataStreamMini||Georg Krempl|
|Open Challenges for Data Stream Mining Research||10.1145/2674026.2674028||2014|
|Author||Georg Krempl +, Indre Žliobaite +, Dariusz Brzeziński +, Eyke Hüllermeier +, Mark Last +, Vincent Lemaire +, Tino Noack +, Ammar Shaker +, Sonja Sievi +, Myra Spiliopoulou + and Jerzy Stefanowski +|
|title||Open Challenges for Data Stream Mining Research +|