Determining the impact regions of competing options in preference space

2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017, Chicago, Illinois, USA, 14-19 May 2017In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option (focal record) is among the top-k score-wise. In effect, these regions capture all possible user profiles for which the focal record is highly preferable, and are therefore essential in market impact analysis, potential customer identification, profile-based marketing, targeted advertising, etc. We refer to our problem as k-Shortlist Preference Region identification (kSPR), and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem.Department of Computing2016-2017 > Academic research: refereed > Refereed conference paperbcw

Efficient Answering of Polyhedral Queries in Rd Using BBS-Trees

Abstract We present a simple method for answering d-dimensional polyhedral queries, based on partitioning the space into disjoint regions and using a BalancedBinary Search (BBS) tree to index the points in each region. By appropriately selecting the boundaries ofeach region, we can guarantee an average search time that almost matches a known lower bound for the prob-lem. Specifically, for a fixed d, if the coordinates ofa given set of n points are statistically independent,the proposed technique answers polyhedral queries, on the average, in O(mn1-1/d.(log n)1/d + mk) using O(n)space, where k is the number of reported points, and mis the number of linear constraints bounding the query region. Our approach is novel in that, while it providesa theoretical upper bound on the average query time, it avoids the use of complicated data structures, makingit an effective candidate for practical applications. 1 Introduction The problem of answering polyhedral queries has re-ceived reasonable attention. There are theoretically known lower and upper bounds for this problem. Inparticular, Chazelle and Rosenberg [3] show that simplex reporting in d-dimensions, using only linear O(n)space, requires \Omega ( n1-1/d+k) query time. For the upperbounds, Matou^sek [5] gave an almost optimal algorith

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal