Dilated Convolution for Time Series Learning
Wang Zhang, Subhro Das, et al.
ICASSP 2025
Researchers are developing domain-driven data mining techniques that target actionable knowledge discovery (KDD) in complex domain problems. The domain-driven technique aims to utililize and mine many aspects of intelligence, such as in-depth data, domain expertise, real-time human involvement, process, environment, and social intelligence. It also metasynthesizes its intelligence sources for actionable knowledge discovery. The method works to expose next-generation methodologies for actionable knowledge discovery, identifying ways in which KDD can better contribute to critical domain problems in theory and practice. It undercovers domain-driven techniques to help KDD, strengthen business intelligence in complex enterprise applications. It also reveals applications that effectively deploy domain-driven data mining method,to solve complex practical problems.
Wang Zhang, Subhro Das, et al.
ICASSP 2025
Ran Iwamoto, Kyoko Ohara
ICLC 2023
Liya Fan, Fa Zhang, et al.
JPDC
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022