H
Hui Ding
Researcher at Northwestern University
Publications - 14
Citations - 2948
Hui Ding is an academic researcher from Northwestern University. The author has contributed to research in topics: Social graph & Fréchet distance. The author has an hindex of 9, co-authored 13 publications receiving 2587 citations. Previous affiliations of Hui Ding include Facebook.
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Journal ArticleDOI
Querying and mining of time series data: experimental comparison of representations and distance measures
TL;DR: An extensive set of time series experiments are conducted re-implementing 8 different representation methods and 9 similarity measures and their variants and testing their effectiveness on 38 time series data sets from a wide variety of application domains to provide a unified validation of some of the existing achievements.
Journal ArticleDOI
Experimental comparison of representation methods and distance measures for time series data
TL;DR: An extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants and testing their effectiveness on 38 time series data sets from a wide variety of application domains gives an overview of these different techniques and presents comparative experimental findings regarding their effectiveness.
Proceedings Article
TAO: Facebook's distributed data store for the social graph
Nathan Bronson,Zach Amsden,George Cabrera,Prasad Chakka,Peter Dimov,Hui Ding,Jack Ferris,Anthony Giardullo,Sachin Kulkarni,Harry Li,Mark Marchukov,Dmitri Petrov,Lovro Puzar,Yee Jiun Song,Venkat Venkataramani +14 more
TL;DR: TAO is a geographically distributed data store that provides efficient and timely access to the social graph for Facebook's demanding workload using a fixed set of queries.
Posted Content
Experimental Comparison of Representation Methods and Distance Measures for Time Series Data
TL;DR: In this article, the authors present a comparative experimental study of time series representations and similarity measures and their performance on thirty-eight time series data sets from a wide variety of application domains.
Proceedings ArticleDOI
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
TL;DR: This work formalizes the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provides a compact structure for their representation and efficient algorithms for constructing that structure.