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.

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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

We present a parallel algorithm for finding the convex hull of a sorted point set. The algorithm runs in O(log log n) (doubly logarithmic) time using n/log log n processors on a Common CRCW PRAM. To break the Ω(log n/log log n) time barrier required to output the convex hull in a contiguous array, we introduce a novel data structure for representing the convex hull. The algorithm is optimal in two respects: (1) the time-processor product of the algorithm, which is linear, cannot be improved, and (2) the running time, which is doubly logarithmic, cannot be improved even by using a linear number of processors. The algorithm demonstrates the power of the “the divide-and-conquer doubly logarithmic paradigm” by presenting a non-trivial extension to situations that previously were known to have only slower algorithms.

A fast parallel algorithm for finding the convex hull of a sorted point set

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 which enables us to answer each query in O (1) time, as in Harel and Tarjan [HT-84]. 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.

On finding lowest common ancestors: simplification and parallelization (extended summary)

Say that a parallel algorithm that uses p processors and N (>p) shared memory locations is given. The problem of simulating this algorithm by p processors and only p shared memory locations without increasing the running time by more than a constant factor is considered. A solution for a family of such parallel algorithms is given. The solution utilizes the idea of dynamically changing locations of the addresses of the algorithm throughout the simulation.

Dynamic Parallel Memories

Oblivious RAM ORAM is a cryptographic primitive that allows a trusted CPU to securely access untrusted memory, such that the access patterns reveal nothing about sensitive data. ORAM is known to have broad applications in secure processor design and secure multi-party computation for big data. Unfortunately, due to a logarithmic lower bound by Goldreich and Ostrovsky Journal of the ACM, '96, ORAM is bound to incur a moderate cost in practice. In particular, with the latest developments in ORAM constructions, we are quickly approaching this limit, and the room for performance improvement is small.

In this paper, we consider new models of computation in which the cost of obliviousness can be fundamentally reduced in comparison with the standard ORAM model. We propose the Oblivious Network RAM model of computation, where a CPU communicates with multiple memory banks, such that the adversary observes only which bank the CPU is communicating with, but not the address offset within each memory bank. In other words, obliviousness within each bank comes for free--either because the architecture prevents a malicious party from observing the address accessed within a bank, or because another solution is used to obfuscate memory accesses within each bank--and hence we only need to obfuscate communication patterns between the CPU and the memory banks. We present new constructions for obliviously simulating general or parallel programs in the Network RAM model. We describe applications of our new model in secure processor design and in distributed storage applications with a network adversary.

/pdf/oblivious-network-ram-and-leveraging-parallelism-to-achieve-2byiiy303p.pdf

Oblivious Network RAM and Leveraging Parallelism to Achieve Obliviousness

From Algorithm Parallelism to Instruction-Level Parallelism: An Encode-Decode Chain Using Prefix-Sum

Uzi Vishkin

Papers

A fast parallel algorithm for finding the convex hull of a sorted point set

On finding lowest common ancestors: simplification and parallelization (extended summary)

Dynamic Parallel Memories

Oblivious Network RAM and Leveraging Parallelism to Achieve Obliviousness

From algorithm parallelism to instruction-level parallelism: an encode-decode chain using prefix-sum