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.

Approximation Algorithms

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 bridging model for parallel computation

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.

/pdf/a-guided-tour-to-approximate-string-matching-22fowlmq5b.pdf

A guided tour to approximate string matching

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.

https://hornad.fei.tuke.sk/~genci/Vyucba/OOBDS/Archiv/Podklady/TExtoveIS&IR/Information%20Retrieval%20Data%20Structures%20&%20Algorithms.PDF

Information Retrieval: Data Structures and Algorithms

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

/pdf/duplicate-record-detection-a-survey-4crkywvv1d.pdf

Duplicate Record Detection: A Survey

The problem of merging two sorted arrays A = (a1, a2, ..., an1) and B = (b1, b2, ..., bn2) is considered. For input elements that are drawn from a domain of integers 1...s] we present an algorithm that runs in O(log log log s) time using n/log log log s CREW PRAM processors (optimal speed-up) and O(ns?) space, where n = n1 + n2. For input elements that are drawn from a domain of integers 1...n] we present a second algorithm that runs in O(?(n)) time (where ?(n) is the inverse of Ackermann?s function) using n/?(n) CREW PRAM processors and linear space. This second algorithm is non-uniform; however, it can be made uniform at a price of a certain loss of speed, or by using a CRCW PRAM.

On Parallel Integer Merging

Proximity effect correction by dose modulation is widely practiced in electron-beam lithography. Optical proximity control is also possible using a combination of shape adjustment and phase control. Assigning “the right” dose (or fill factor and phase for optics) is a well known mathematical inverse problem. Linear programming, by definition, is the appropriate method for determining dose. In the past, the technique was too slow for full-scale implementation in mask making. Here, the authors discuss how recent developments in computer speed and architecture have improved the prospects for full-scale implementation. In addition, the authors discuss some numerical techniques, analogous to gridding and relaxation, that make linear programming more attractive in mask making.

Electron beam and optical proximity effect reduction for nanolithography: New results

The resource discovery problem was introduced by Harchol-Balter, Leigh ton and Lewin. They developed a number of algorithms for the problem in the weakly connected directed graph model. This model is a directed logical graph, that represents the vertices' “knowledge” about the topology of the underlying communication network.The current paper proposes a deterministic algorithm for the problem in the same model, with improved time, message, and communication complexities. Each previous algorithm had a complexity that was higher at least in one of the measures. Specifically, previous deterministic solutions required either time linear in the diameter of the initial network, or communication complexity O(n3) (with message complexity O(n2)), or message complexity O(¦E0¦ log n) (where E0 is the edge set of the initial graph). Compared to the main randomized algorithm of Harchol-Balter, Leigh ton, and Lewin, the time complexity is reduced from O(log2 n) to O(log n), the message complexity from O(n log2 n) to O(n log n), and the communication complexity from O(n2 log3 n) to O(¦E0¦ log2 n). Our work significantly extends the connectivity algorithm of Shiloach and Vishkin which was originally given for a parallel model of computation. Our result also confirms a conjecture of Harchol-Balter, Leighton, and Lewin, and addresses an open question due to R. Lipton.

http://iew3.technion.ac.il/~kutten/dblp_69.pdf

Uzi Vishkin

Papers

On Parallel Integer Merging

Electron beam and optical proximity effect reduction for nanolithography: New results

Deterministic resource discovery in distributed networks

Finding all nearest neighbors for convex polygons in parallel: a new lower bound technique and a matching algorithm

Solving NP-hard problems in almost trees: vertex cover