Dragomir Yankov
Papers
1
Total Citations
168
H-Index
1
About
Dragomir Yankov is a leading researcher in time series data mining, with a particular focus on pattern discovery and motif detection. His seminal 2007 work, "Detecting time series motifs under uniform scaling," has garnered 168 citations and fundamentally advanced the field by introducing efficient linear-time algorithms for identifying approximately repeated patterns—or motifs—within time series data. This contribution has proven critical for a wide range of data mining applications, including rule discovery, novelty detection, summarization, and clustering. Yankov’s research has helped transform how analysts extract meaningful structure from temporal datasets, enabling more robust and scalable analysis across domains such as finance, healthcare, and sensor networks. His work stands out for its practical impact, providing foundational tools that are widely adopted by both academic researchers and industry practitioners. Through his innovative approaches to pattern recognition and scaling challenges, Yankov has established himself as a key figure in time series analytics, whose methods continue to shape modern data mining practices.
Research Focus
Key Achievements
Top Papers
- 1Detecting time series motifs under uniform scaling168 citations · 2007