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Efficient Mining of Frequent Item Sets on Large Uncertain Databases

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2012年第12期24卷 2170-2183页

作者： Wang, Liang Cheung, David Wai-Lok Cheng, Reynold Lee, Sau Dan Yang, Xuan S. Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

The data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain database, interpreted under the Possible World Semantics (PWS). This issue is technically challenging, since an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a Poisson binomial distribution, we develop an approximate algorithm, which can efficiently and accurately discover frequent item sets in a large uncertain database. We also study the important issue of maintaining the mining result for a database that is evolving (e. g., by inserting a tuple). Specifically, we propose incremental mining algorithms, which enable Probabilistic Frequent Item set (PFI) results to be refreshed. This reduces the need of re-executing the whole mining algorithm on the new database, which is often more expensive and unnecessary. We examine how an existing algorithm that extracts exact item sets, as well as our approximate algorithm, can support incremental mining. All our approaches support both tuple and attribute uncertainty, which are two common uncertain database models. We also perform extensive evaluation on real and synthetic data sets to validate our approaches.

关键词： Frequent item sets uncertain data set approximate algorithm incremental mining

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Adaptive and Extensible Energy Supply Mechanism for UAVs-Aided Wireless-Powered Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2020年第9期7卷 9201-9213页

作者： Wu, Pengfei Xiao, Fu Huang, Haiping Sha, Chao Yu, Shui Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210013 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210013 Peoples R China Univ Technol Sydney Sch Software Sydney NSW 2007 Australia

This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.

关键词： Trajectory Internet of Things Wireless communication Wireless power transfer Approximation algorithms Unmanned aerial vehicles Task analysis approximate algorithm multiple depots unmanned aerial vehicle (UAV) wireless power sensor networks wireless power transfer (WPT)

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A Hopeful CNF-SAT─algorithm Its High Efficiency, Industrial Application and Limitation

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Journal of Computer Science & Technology 1998年第1期13卷 9-12页

作者：黄文奇李未 DepartmentofComputerScience HuazhongUniversityofScience＆TechnologyWuhan430074P.R.China DepartmentofC

From the SAT physical model, a physical hypothesis named PHHY is proposed. By PHHY, it is proved that there is a universally efficient algorithm for solving SAT problem. Then, by square packing problem, the authors show that there are interesting industrial NP-complete problems which can be solved through SAT algorithms, but each way of solving like this will be much worse than that of a certain direct solving.

关键词： NP-complete problem CNF-SAT satisfiability potential function approximate algorithm probability complexity.

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Decoupling joint probabilistic data association algorithm for multiple target tracking

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 1999年第5期146卷 251-254页

作者： Ding, Z Leung, H Hong, L Raytheon Syst Canada Ltd Adv Syst Dev Waterloo ON N2J 4K6 Canada Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada Wright State Univ Dept Elect Engn Dayton OH 45435 USA

The authors present a decoupling joint probabilistic data association (DJPDA) algorithm by decoupling the JPDA algorithm into separate PDA algorithms. To demonstrate the decoupling idea, they derive a set of complete decoupling PDA equations for two and three targets. Since these equations are impractical for real-time systems and are unavailable for an arbitrary number of targets, an approximate algorithm is given. The proposed decoupling algorithm is evaluated using computer simulation. It is shown that the performance of the DJPDA algorithm is close to that of the JPDA. algorithm but it needs much less computation.

关键词： decoupling algorithm approximate algorithm decoupling joint probabilistic data association algorithm PDA algorithms target tracking JPDA algorithm covariance matrices Signal processing and detection multiple target tracking Other topics in statistics probability computer simulation

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A hybrid algorithm combining glowworm swarm optimization andcomplete 2-opt algorithm for spherical travelling salesman problems

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APPLIED SOFT COMPUTING 2017年 58卷 104-114页

作者： Chen, Xin Zhou, Yongquan Tang, Zhonghua Luo, Qifang Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Dalian Univ Technol Shcool Software Dalian 116024 Peoples R China

The Travelling Salesman Problem (TSP) is one of the most well-known combinatorial optimization problems and has attracted a lot of interests from researchers. Many studies have proposed various methods for solving the two-dimensional TSP. In this study, we extend the two-dimensional TSP to the three-dimensional TSP, namely the spherical TSP in which all points (cities) and paths (solutions) are on the surface of a sphere. A hybrid algorithm based on the glowworm swarm optimization (GSO) and the complete 2-opt algorithm is proposed, in which the carriers of the luciferin are transformed from glowworms to edges between cities, and the probabilistic formula and the luciferin updating formula are modified. In addition, the complete 2-opt algorithm is performed to optimize the selected optimal routes every few iterations. Numerical experimental results show that the proposed algorithm has a better performance than the basic GSO in solving the spherical TSP. Meanwhile, the complete 2-opt algorithm can speed up the convergence rate. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Travelling salesman problem Glowworm swarm optimization Complete 2-opt algorithm Spherical TSP approximate algorithm

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Bunkering policies for a fuel bunker management problem for liner shipping networks

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期289卷 927-939页

作者： De, Arijit Choudhary, Alok Turkay, Metin Tiwari, Manoj K. Newcastle Univ Business Sch Newcastle Upon Tyne NE1 4SE Tyne & Wear England Loughborough Univ Sch Business & Econ Loughborough LE11 3TU Leics England Koc Univ Dept Ind Engn Rumelifeneri Yolu TR-34450 Istanbul Turkey Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper investigates the problem of bunker fuel management for liner shipping networks under different fuel pricing scenarios and taking into consideration different fuel bunkering policies. The fuel consumption of a vessel on a sailing leg may fluctuate as the real vessel speed deviates from the planned vessel speed. Furthermore, fluctuation of fuel prices at various ports increases the complexity of bunkering decisions related to the selection of the bunkering ports and the estimation of bunkered fuel cost. We have developed a mixed integer non-linear programming model to minimize the total expected cost consisting of inventory cost related to container transportation, operating cost associated with ship hiring, as well as bunkering cost and fuel consumption cost at the port. The novelty of our research lies in its consideration of stochastic fuel consumption for different sailing legs, stochastic fuel prices at each port and different fuel bunkering policies to determine optimal bunker fuel management strategies for the selection of bunkering ports and for the estimation of the amount of bunkered fuel required. We have proposed a novel approximate algorithm based on mathematical formulation and the fuel bunkering policies to calculate the total expected cost;the fuel inventory while arriving at and departing from the port;the number of vessels hired for weekly service;the arrival and departure time of the ship;and the amount of fuel bunkered at a port. We have performed extensive computational experiments on the practical routes to demonstrate the applicability, efficacy and robustness of the proposed novel methodology. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Heuristics Liner shipping Bunker fuel management Bunkering policies approximate algorithm

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Capacitated assortment and price optimization under the nested logit model

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JOURNAL OF GLOBAL OPTIMIZATION 2020年第4期77卷 895-918页

作者： Chen, Rui Jiang, Hai Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

We study the capacitated assortment and price optimization problem, where a retailer sells categories of substitutable products subject to a capacity constraint. The goal of the retailer is to determine the subset of products as well as their selling prices so as to maximize the expected revenue. We model the customer purchase behavior using the nested logit model and formulate this problem as a non-linear binary integer program. For this NP-complete problem, we show that there exists a pseudo polynomial time approximation scheme that finds its epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-approximate solution. We first convert the original problem into an equivalent fixed point problem. We then show that finding an epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-approximate solution to the fixed point problem can be achieved by binary search, where a non-linear auxiliary problem is repeatedly approximated by a dynamic programing based algorithm involving an approximation to a series of multiple-choice parametric knapsack problems. For the special case when the capacity constraints are cardinal and nest-specific, we develop an algorithm that finds the optimal solution in strongly polynomial time. Moreover, our algorithm can be directly applied to find an epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-approximate solution to the ca

关键词： Assortment optimization Nested logit model approximate algorithm Combinatorial optimization

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Addressing the Most Reliable Edge-Disjoint Paths With a Delay Constraint

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IEEE TRANSACTIONS ON RELIABILITY 2011年第1期60卷 88-93页

作者： Loh, Ruen Chze Soh, Sieteng Lazarescu, Mihai Curtin Univ Technol Dept Comp Perth WA 6845 Australia

Recently, multipath solutions have been proposed to improve the quality-of-service of the source to destination (s, t)-path in communication networks (CNs). This paper describes the lambda DP/DR problem to obtain lambda >= 1 edge-disjoint (s, t)-paths (lambda DP) such that its reliability is maximized while keeping its delay no longer than a delay constraint D >= 1. This problem is NP-hard, and thus this paper proposes an approximate solution using Lagrange-relaxation. Our algorithm generates lambda DP with delta(lambda DP) <= D, and reliability bounded by vertical bar Log(R(min))vertical bar <= vertical bar log(rho(lambda DP))vertical bar <= (1 + k)*vertical bar log(R(min))vertical bar, where R(min) is the minimum reliability of any (s, t)-path in the CN, and k >= 1. Our simulations on forty random CNs and large grid networks show that our solution produces lambda DP with delay and reliability comparable to those obtained by the optimal but exponential time algorithm.

关键词： approximate algorithm lagrange-relaxation multi-constrained edge-disjoint paths network delay network reliability

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Simulated annealing for higher dimensional projection depth

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第12期56卷 4026-4036页

作者： Shao, Wei Zuo, Yijun Michigan State Univ Dept Stat & Probabil E Lansing MI 48824 USA Shandong Univ Sch Math Jinan 250100 Peoples R China

Data depth for multivariate data has received considerable attention in multivariate nonparametric analysis and robust statistics. Nevertheless, the computation of data depth such as projection depth has remained as a very challenging problem which hinders the development of the projection depth and its wide use in practice. Especially in high dimension, there is no efficient algorithm for the computation of the projection depth and its induced estimators (including the Stahel-Donoho estimator as a special case). In this paper, we employ simulated annealing algorithm by invoking Markov Chain Monte Carlo technique to compute the projection depth. Simulation results show that this new approximate method performs significantly better than its competitors. In lower dimension, we are able to show that the approximate results from this algorithm are very close to the exact ones. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Projection depth approximate algorithm Simulated annealing Markov Chain Monte Carlo

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Dynamic appointment scheduling with patient preferences and choices

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2015年第4期115卷 700-717页

作者： Wang, Jin Fung, Richard Y. K. City Univ Hong Kong Dept Management Sci Hong Kong Hong Kong Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

Purpose - The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices. Design/methodology/approach - Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model. Findings - Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered. Originality/value - This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department.

关键词： Dynamic programming Appointment scheduling approximate algorithm Patient preferences

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