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35th ACM SIGIR Annual International Conference on Research and Development in Information Retrieval

作者： Najork, Marc Microsoft Res 1065 La Ave Mountain View CA 94043 USA

ISBN: (纸本)9781450316583

Web-based advertising and electronic commerce, combined with the key role of search engines in driving visitors to ad-monetized and e-commerce web sites, has given rise to the phenomenon of web spam: web pages that are of little value to visitors, but that are created mainly to mislead search engines into driving traffic to target web sites. A large fraction of spam web pages is automatically generated, and some portion of these pages is generated by stitching together parts (sentences or paragraphs) of other web pages. This paper presents a scalable algorithm for detecting such "quilted" web pages. Previous work by the author and his collaborators introduced a sampling-based algorithm that was capable of detecting some, but by far not all quilted web pages in a collection. By contrast, the algorithm presented in this work identifies all quilted web pages, and it is scalable to very large corpora. We tested the algorithm on the half-billion page English-language subset of the ClueWeb09 collection, and evaluated its effectiveness in detecting web spam by manually inspecting small samples of the detected quilted pages. This manual inspection guided us in iteratively refining the algorithm to be more efficient in detecting real-world spam.

关键词： Web spam detection phrase-level duplication scalable algorithms

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Efficient and dimension independent methods for neural network surrogate construction and training

Efficient and dimension independent methods for neural netwo...

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学位级别：博士

In this dissertation I investigate how to efficiently construct neural network surrogates for parametric maps defined by PDEs, and how to use second order information to improve solutions to the related neural network training problem. Many-query problems arising in scientific applications (such as optimization, uncertainty quantification and inference problems) require evaluation of an input output mapping parametrized by a high dimensional nonlinear PDE model. The cost of these evaluations makes solution using the model prohibitive, and efficient accurate surrogates are the key to solving these problems in practice. In this work I investigate neural network surrogates that use model information to detect informed subspaces of the input and output where the parametric map can be represented efficiently. These compact representations require relatively few data to train and outperform conventional data-driven approaches which require large training data sets. Once a neural network is designed, training is a major issue. One seeks to find optimal weights for a neural network that generalize to data not seen during training. In this work I investigate how second order information can be efficiently exploited to design optimizers that have fast convergence and good generalization properties. These optimizers are shown to outperform conventional methods in numerical experiments.

关键词： Optimization Machine learning Surrogate modeling Reduced order methods Matrix-free methods Neural networks Uncertainty quantification scalable algorithms Randomized methods

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Global Convection In Earth'S Mantle: Advanced Numerical Methods And Extreme-Scale Simulations

Global Convection In Earth'S Mantle: Advanced Numerical Meth...

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作者： Johann Rudi University of Texas – Austin

学位级别：博士

The thermal convection of rock in Earth's mantle and associated plate tectonics are modeled by nonlinear incompressible Stokes and energy equations. This dissertation focuses on the development of advanced, scalable linear and nonlinear solvers for numerical simulations of realistic instantaneous mantle flow, where we must overcome several computational challenges. The most notable challenges are the severe nonlinearity, heterogeneity, and anisotropy due to the mantle's rheology as well as a wide range of spatial scales and highly localized features. Resolving the crucial small scale features efficiently necessitates adaptive methods, while computational results greatly benefit from a high accuracy per degree of freedom and local mass conservation. Consequently, the discretization of Earth's mantle is carried out by high-order finite elements on aggressively adaptively refined hexahedral meshes with a continuous, nodal velocity approximation and a discontinuous, modal pressure approximation. These velocityÂ â pressure pairings yield optimal asymptotic convergence rates of the finite element approximation to the infinite-dimensional solution with decreasing mesh element size, are inf-sup stable on general, non-conforming hexahedral meshes with "hanging nodes,'' and have the advantage of preserving mass locally at the element level due to the discontinuous pressure. However, because of the difficulties cited above and the desired accuracy, the large implicit systems to be solved are extremely poorly conditioned and sophisticated linear and nonlinear solvers including powerful preconditioning techniques are required. The nonlinear Stokes system is solved using a grid continuation, inexact NewtonÂ â Krylov method. We measure the residual of the momentum equation in the Hâ»Â¹-norm for backtracking line search to avoid overly conservative update steps that are significantly reduced from one. The Newton linearization is augmented by a perturbation of a highly nonlinea

关键词： Partial differential equations Stokes Numerical methods Variable viscosity Schur complement Preconditioning Multigrid scalable algorithms Parallel computing Iterative methods Nonlinear problems Inexact Newton-Krylov methods Mantle convection

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A scalable and efficient systolic algorithm for the longest common subsequence problem

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2002年第4期18卷 519-532页

作者： Lin, YC Yeh, JW Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan

A longest common subsequence (LCS) of two strings is a common subsequence of two strings of maximal length. The LCS problem is that of finding an LCS of two given strings and the length of the LCS. This problem has been the subject of much research because its solution can be applied in many areas. In this paper, a scalable and efficient systolic algorithm is presented. For two given strings of length m and n, where m greater than or equal to n, the algorithm can solve the LCS problem in m + 2r - 1 (respectively n + 2r - 1) time steps with r < n/2 (respectively r < m/2) processors. Experimental results show that the algorithm can be faster on multicomputers than all the previous systolic algorithms for the same problem.

关键词： longest common subsequence multicomputers parallel algorithms scalable algorithms systolic arrays

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P3DFFT: A FRAMEWORK FOR PARALLEL COMPUTATIONS OF FOURIER TRANSFORMS IN THREE DIMENSIONS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2012年第4期34卷 C192-C209页

作者： Pekurovsky, Dmitry Univ Calif San Diego San Diego Supercomp Ctr La Jolla CA 92093 USA

Fourier and related transforms are a family of algorithms widely employed in diverse areas of computational science, notoriously difficult to scale on high-performance parallel computers with a large number of processing elements (cores). This paper introduces a popular software package called P3DFFT which implements fast Fourier transforms (FFTs) in three dimensions in a highly efficient and scalable way. It overcomes a well-known scalability bottleneck of three-dimensional (3D) FFT implementations by using two-dimensional domain decomposition. Designed for portable performance, P3DFFT achieves excellent timings for a number of systems and problem sizes. On a Cray XT5 system P3DFFT attains 45% efficiency in weak scaling from 128 to 65,536 computational cores. Library features include Fourier and Chebyshev transforms, Fortran and C interfaces, in- and out-of-place transforms, uneven data grids, and single and double precision. P3DFFT is available as open source at http://***/p/p3dfft/. This paper discusses P3DFFT implementation and performance in a way that helps guide the user in making optimal choices for parameters of their runs.

关键词： fast Fourier transforms parallel computing scalable algorithms all-to-all communication numerical libraries petascale computation bisection bandwidth

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Bellwether Analysis: Searching for Cost-Effective Query-Defined Predictors in Large Databases

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2009年第1期3卷 1–49页

作者： Chen, Bee-Chung Ramakrishnan, Raghu Shavlik, Jude W. Tamma, Pradeep Yahoo Res Santa Clara CA 95054 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA Microsoft Redmond WA 98052 USA

How to mine massive datasets is a challenging problem with great potential value. Motivated by this challenge, much effort has concentrated on developing scalable versions of machine learning algorithms. However, the cost of mining large datasets is not just computational;preparing the datasets into the "right form" so that learning algorithms can be applied is usually costly, due to the human labor that is typically required and a large number of choices in data preparation, which include selecting different subsets of data and aggregating data at different granularities. We make the key observation that, for a number of practically motivated problems, these choices can be defined using database queries and analyzed in an automatic and systematic manner. Specifically, we propose a new class of data-mining problem, called bellwether analysis, in which the goal is to find a few query-defined predictors (e. g., first week sales of Peoria, IL of an item) that can be used to accurately predict the result of a target query (e. g., first year worldwide sales of the item) from a large number of queries that define candidate predictors. To make a prediction for a new item, the data needed to generate such predictors has to be collected (e. g., selling the new item in Peoria, IL for a week and collecting the sales data). A useful predictor is one that has high prediction accuracy and a low data-collection cost. We call such a cost-effective predictor a bellwether. This article introduces bellwether analysis, which integrates database query processing and predictive modeling into a single framework, and provides scalable algorithms for large datasets that cannot fit in main memory. Through a series of extensive experiments, we show that bellwethers do exist in real-world databases, and that our computation techniques achieve good efficiency on large datasets.

关键词： Cost-effective prediction data cube OLAP queries predictive models scalable algorithms bellwether

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Multi-purchase Behavior: Modeling, Estimation, and Optimization

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2023年第6期25卷 2298-2313页

作者： Tulabandhula, Theja Sinha, Deeksha Karra, Saketh Reddy Patidar, Prasoon Univ Illinois Informat & Decis Sci Chicago IL 60607 USA MIT Operat Res Ctr Cambridge MA 02139 USA Carnegie Mellon Univ Software & Societal Syst Pittsburgh PA 15213 USA

Problem definition: We study the problem of modeling purchase of multiple products and using it to display optimized recommendations for online retailers and e-commerce platforms. Rich modeling of users and fast computation of optimal products to display given these models can lead to significantly higher revenues and simultaneously enhance the user experience. Methodology/results: We present a parsimonious multi purchase family of choice models called the BundleMVL-K family and develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model. We establish the hardness of computing optimal recommendation sets and derive several structural properties of the optimal solution that aid in speeding up computation. This is one of the first attempts at operationalizing multi-purchase class of choice models. We show one of the first quantitative links between modeling multiple purchase behavior and revenue gains. The efficacy of our modeling and optimization techniques compared with competing solutions is shown using several real-world data sets on multiple metrics such as model fitness, expected revenue gains, and run-time reductions. For example, the expected revenue benefit of taking multiple purchases into account is observed to be similar to 5% in relative terms for the Ta Feng and UCI shopping data sets compared with the multinomial choice model for instances with similar to 1,500 products. Additionally, across six real-world data sets, the test log-likelihood fits of our models are on average 17% better in relative terms. Managerial implications: Our work contributes to the study of multi-purchase decisions, analyzing consumer demand, and the retailers optimization problem. The simplicity of our models and the iterative nature of our optimization technique allows practitioners meet stringent computational constraints while increasing their revenues in practical recommendation applications at scale, especially in e-co

关键词： multichoice purchase behavior recommendations scalable algorithms structural properties

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Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model

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PARALLEL COMPUTING 2019年 89卷 102548-000页

作者： Artemov, Anton G. Rudberg, Elias Rubensson, Emanuel H. Uppsala Univ Dept Informat Technol Div Sci Comp Box 337 SE-75105 Uppsala Sweden

We present three methods for distributed memory parallel inverse factorization of block-sparse Hermitian positive definite matrices. The three methods are a recursive variant of the AINV inverse Cholesky algorithm, iterative refinement, and localized inverse factorization. All three methods are implemented using the Chunks and Tasks programming model, building on the distributed sparse quad-tree matrix representation and parallel matrix-matrix multiplication in the publicly available Chunks and Tasks Matrix Library (CHTML). Although the algorithms are generally applicable, this work was mainly motivated by the need for efficient and scalable inverse factorization of the basis set overlap matrix in large scale electronic structure calculations. We perform various computational tests on overlap matrices for quasi linear glutamic acid-alanine molecules and three-dimensional water clusters discretized using the standard Gaussian basis set STO-3G with up to more than 10 million basis functions. We show that for such matrices the computational cost increases only linearly with system size for all the three methods. We show both theoretically and in numerical experiments that the methods based on iterative refinement and localized inverse factorization outperform previous parallel implementations in weak scaling tests where the system size is increased in direct proportion to the number of processes. We show also that, compared to the method based on pure iterative refinement, the localized inverse factorization requires much less communication. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Parallel computing Sparse matrix algorithms scalable algorithms Large-scale computing Algorithm analysis

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Value-added tax fraud detection with scalable anomaly detection techniques

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Vanhoeyveld, Jellis Martens, David Peeters, Bruno Univ Antwerp Fac Appl Econ Prinsstr 13 B-2000 Antwerp Belgium Univ Antwerp Fac Law Venusstr 23 B-2000 Antwerp Belgium

The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with these domain issues. We analyse a unique dataset containing the VAT declarations and client listings of all Belgian VAT numbers pertaining to ten sectors. Our methodology consists in applying AD methods to firms belonging to the same sector and enables an efficient auditing strategy that can be adopted by tax authorities worldwide. The high lifts and hit rates observed in most sectors demonstrate the success of this approach. Sectoral differences exist due to varying market conditions and legal requirements across sectors and we show that the optimal AD method is sector dependent. We focus on three methodological problems that show issues in the related literature. (1) Can we design suitable input features? We develop new fraud indicators from specific fields of the VAT form and client listings and show the predictive value of the combination of these features. (2) Can we design fast algorithms to deal with the large data sizes that can occur in the tax domain? New methods are developed and we demonstrate their scalability both theoretically as well as empirically. (3) How should fraud detection performance be assessed? A new evaluation methodology is proposed that provides reliable performance indications and guarantees that fraud cases are effectively detected by the proposed methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Unsupervised anomaly detection Tax fraud detection scalable algorithms

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Large-scale periodic scheduling in time-sensitive networks

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COMPUTERS & OPERATIONS RESEARCH 2022年 137卷 105512-105512页

作者： Vlk, Marek Brejchova, Katerina Hanzalek, Zdenek Tang, Siyu Czech Tech Univ Czech Inst Informat Robot & Cybernet Jugoslavskych Partyzanu 1580-3 Prague 16000 6 Czech Republic Charles Univ Prague Fac Math & Phys Malostranske Namesti 25 Prague 11800 1 Czech Republic Huawei Technol Duesseldorf GmbH Appl Network Technol Lab Munich Res Ctr Riesstr 253-0G D-80992 Munich Germany

The real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and timeliness guarantees can be achieved by time-triggered communication schedules that are enabled by the IEEE 802.1Qbv standard. However, generating such a schedule is a computationally challenging task due to several harsh constraints that must be satisfied. This problem is usually transformed into some existing formalism, e.g., Satisfiability Modulo Theories, and solved using a general third-party solver, which has the drawback of limited scalability. Except for some one-pass heuristics, such as the earliest deadline first or as-soon-as-possible scheduling, the current literature does not provide any algorithm tailored to this specific problem of scheduling time-triggered traffic in time-sensitive networks. In this paper, we develop an efficient algorithm that does not use any third-party solver and is based on the search in the space of partial schedules while being guided by the discovered conflicts. Our algorithm calculates schedules for problem instances consisting of 2000 network nodes and more than 10 000 flows.

关键词： Time-sensitive networking Periodic scheduling Time-triggered message scheduling Periodic job-shop extensions scalable algorithms Heuristics

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