N
Nick Koudas
Researcher at University of Toronto
Publications - 190
Citations - 13710
Nick Koudas is an academic researcher from University of Toronto. The author has contributed to research in topics: Query optimization & Set (abstract data type). The author has an hindex of 56, co-authored 182 publications receiving 13229 citations. Previous affiliations of Nick Koudas include Association for Computing Machinery & AT&T.
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Proceedings ArticleDOI
Holistic twig joins: optimal XML pattern matching
TL;DR: This paper proposes a novel holistic twig join algorithm, TwigStack, that uses a chain of linked stacks to compactly represent partial results to root-to-leaf query paths, which are then composed to obtain matches for the twig pattern.
Proceedings ArticleDOI
TwitterMonitor: trend detection over the twitter stream
Michael Mathioudakis,Nick Koudas +1 more
TL;DR: TwitterMonitor, a system that performs trend detection over the Twitter stream and provides meaningful analytics that synthesize an accurate description of each topic on Twitter in real time, is presented.
Proceedings ArticleDOI
Structural joins: a primitive for efficient XML query pattern matching
TL;DR: It is shown that, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse, and this behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack- tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-MERge algorithms do not have the same guarantee.
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.
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.