S
S. Muthukrishnan
Researcher at Rutgers University
Publications - 77
Citations - 8123
S. Muthukrishnan is an academic researcher from Rutgers University. The author has contributed to research in topics: Recommender system & Greedy algorithm. The author has an hindex of 31, co-authored 77 publications receiving 7092 citations. Previous affiliations of S. Muthukrishnan include Chennai Mathematical Institute & Association for Computing Machinery.
Papers
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Proceedings Article
Approximate String Joins in a Database (Almost) for Free
Luis Gravano,Panagiotis G. Ipeirotis,H. V. Jagadish,Nick Koudas,S. Muthukrishnan,Divesh Srivastava +5 more
TL;DR: In this article, the authors propose a technique for building approximate string join capabilities on top of commercial databases by exploiting facilities already available in them. But this technique relies on matching short substrings of length, called -grams, and taking into account both positions of individual matches and the total number of such matches.
Journal ArticleDOI
Faster least squares approximation
TL;DR: This work presents two randomized algorithms that provide accurate relative-error approximations to the optimal value and the solution vector of a least squares approximation problem more rapidly than existing exact algorithms.
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 Article
Optimal Histograms with Quality Guarantees
H. V. Jagadish,Nick Koudas,S. Muthukrishnan,Viswanath Poosala,Kenneth C. Sevcik,Torsten Suel +5 more
TL;DR: Algorithms for computing optimal bucket boundaries in time proportional to the square of the number of distinct data values, for a broad class of optimality metrics and an enhancement to traditional histograms that allows us to provide quality guarantees on individual selectivity estimates are presented.
Proceedings Article
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
TL;DR: This work presents general “sketch” based methods for capturing various linear projections of the data and use them to provide pointwise and rangesum estimation of data streams.