J
Jin Shieh
Researcher at University of California, Riverside
Publications - 5
Citations - 745
Jin Shieh is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Search engine indexing & Data stream mining. The author has an hindex of 5, co-authored 5 publications receiving 680 citations.
Papers
More filters
Proceedings ArticleDOI
iSAX: indexing and mining terabyte sized time series
Jin Shieh,Eamonn Keogh +1 more
TL;DR: This work shows how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature, allowing for the exact mining of truly massive real world datasets.
Proceedings ArticleDOI
iSAX 2.0: Indexing and Mining One Billion Time Series
TL;DR: iSAX 2.0 is described, a data structure designed for indexing and mining truly massive collections of time series, and a novel bulk loading mechanism is introduced, the first of this kind specifically tailored to a time series index.
Journal ArticleDOI
iSAX: disk-aware mining and indexing of massive time series datasets
Jin Shieh,Eamonn Keogh +1 more
TL;DR: This work introduces a novel multi-resolution symbolic representation which can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature, and provides analysis concerning parameter sensitivity, approximate search effectiveness, and lower bound comparisons between time series representations in a bit constrained environment.
Proceedings ArticleDOI
Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems
TL;DR: This work introduces a novel framework for allowing visualization to take place in the background of normal day-to-day operation of any GUI based operation system by replacing the standard file icons with automatically created icons that reflect the contents of the files in a principled way.
Proceedings ArticleDOI
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times
Jin Shieh,Eamonn Keogh +1 more
TL;DR: This work shows that this simple observation can be exploited to improve overall classification performance by using an anytime framework to allocate resources among a set of objects buffered from a fast arriving stream.