Uzi Vishkin

Bio: Uzi Vishkin is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Parallel algorithm & Compiler. The author has an hindex of 57, co-authored 219 publications receiving 11690 citations. Previous affiliations of Uzi Vishkin include Max Planck Society & Tel Aviv University.

On Finding Lowest Common Ancestors: Simplification and Parallelization

Abstract: We consider the following problem. Suppose a rooted tree T is available for preprocessing. Answer on-line queries requesting the lowest common ancestor for any pair of vertices in T. We present a linear time and space preprocessing algorithm that enables us to answer each query in $O(1)$ time, as in Harel and Tarjan [SIAM J. Comput., 13 (1984), pp. 338–355]. Our algorithm has the advantage of being simple and easily parallelizable. The resulting parallel preprocessing algorithm runs in logarithmic time using an optimal number of processors on an EREW PRAM. Each query is then answered in $O(1)$ time using a single processor.

An Efficient Parallel Biconnectivity Algorithm

Abstract: In this paper we propose a new algorithm for finding the blocks (biconnected components) of an undirected graph. A serial implementation runs in $O(n + m)$ time and space on a graph of n vertices and m edges. A parallel implementation runs in $O(\log n)$ time and $O(n + m)$ space using $O(n + m)$ processors on a concurrent-read, concurrent-write parallel RAM. An alternative implementation runs in $O(n^2 /p)$ time and $O(n^2 )$ space using any number $p \leqq n^2 /\log ^2 n$ of processors, on a concurrent-read, exclusive-write parallel RAM. The last algorithm has optimal speedup, assuming an adjacency matrix representation of the input. A general algorithmic technique that simplifies and improves computation of various functions on trees is introduced. This technique typically requires $O(\log n)$ time using processors and $O(n)$ space on an exclusive-read exclusive-write parallel RAM.

Deterministic coin tossing with applications to optimal parallel list ranking

Abstract: The following problem is considered: given a linked list of length n , compute the distance from each element of the linked list to the end of the list. The problem has two standard deterministic algorithms: a linear time serial algorithm, and an O (log n ) time parallel algorithm using n processors. We present new deterministic parallel algorithms for the problem. Our strongest results are (1) O (log n log* n ) time using n /(log n log* n ) processors (this algorithm achieves optimal speed-up); (2) O (log n ) time using n log ( k ) n /log n processors, for any fixed positive integer k . The algorithms apply a novel “random-like” deterministic technique. This technique provides for a fast and efficient breaking of an apparently symmetric situation in parallel and distributed computation.

Approximation Algorithms

Abstract: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field. He gives clear, lucid explanations of key results and ideas, with intuitive proofs, and provides critical examples and numerous illustrations to help elucidate the algorithms. Many of the results presented have been simplified and new insights provided. Of interest to theoretical computer scientists, operations researchers, and discrete mathematicians.

A bridging model for parallel computation

Abstract: The success of the von Neumann model of sequential computation is attributable to the fact that it is an efficient bridge between software and hardware: high-level languages can be efficiently compiled on to this model; yet it can be effeciently implemented in hardware. The author argues that an analogous bridge between software and hardware in required for parallel computation if that is to become as widely used. This article introduces the bulk-synchronous parallel (BSP) model as a candidate for this role, and gives results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.

A guided tour to approximate string matching

Abstract: We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.

Information Retrieval: Data Structures and Algorithms

Abstract: An edited volume containing data structures and algorithms for information retrieved including a disk with examples written in C. For programmers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents.

Duplicate Record Detection: A Survey

Abstract: Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area