F
Flip Korn
Researcher at AT&T Labs
Publications - 39
Citations - 3083
Flip Korn is an academic researcher from AT&T Labs. The author has contributed to research in topics: Data stream mining & Data stream. The author has an hindex of 25, co-authored 39 publications receiving 2944 citations. Previous affiliations of Flip Korn include AT&T.
Papers
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Journal ArticleDOI
Influence sets based on reverse nearest neighbor queries
Flip Korn,S. Muthukrishnan +1 more
TL;DR: This paper formalizes a novel notion of influence based on reverse neighbor queries and its variants, and presents a general approach for solving RNN queries and an efficient R-tree based method for large data sets, based on this approach.
Proceedings ArticleDOI
On computing correlated aggregates over continual data streams
TL;DR: This work proposes single-pass techniques for approximate computation of correlated aggregates over both landmark and sliding window views of a data stream of tuples, using a very limited amount of space and shows that this effectiveness is explained by the fact that these techniques exploit monotonicity and convergence properties of aggregate over data streams.
Book ChapterDOI
Finding hierarchical heavy hitters in data streams
TL;DR: D deterministic and randomized algorithms for finding HHHs are presented, which builds upon existing techniques by incorporating the hierarchy into the algorithms and demonstrates several factors of improvement in accuracy over the straightforward approach, which is due to making algorithms hierarchy-aware.
Journal ArticleDOI
On the "dimensionality curse" and the "self-similarity blessing"
TL;DR: It is shown how the Hausdorff and Correlation fractal dimensions of a data set can yield extremely accurate formulas that can predict the I/O performance to within one standard deviation on multiple real and synthetic data sets.
Journal ArticleDOI
On generating near-optimal tableaux for conditional functional dependencies
TL;DR: This paper is the first to formally characterize a "good" pattern tableau, based on naturally desirable properties of support, confidence and parsimony, and shows that the problem of generating an optimal tableau for a given FD is NP-complete but can be approximated in polynomial time via a greedy algorithm.