Materialized view selection as constrained evolutionary optimization

One of the important issues in data warehouse development is the selection of a set of views to materialize in order to accelerate a large number of on-line analytical processing (OLAP) queries. The maintenance-cost view-selection problem is to select a set of materialized views under certain resource constraints for the purpose of minimizing the total query processing cost. However, the search space for possible materialized views may be exponentially large. A heuristic algorithm often has to be used to find a near optimal solution. In this paper, for the maintenance-cost view-selection problem, we propose a new constrained evolutionary algorithm. Constraints are incorporated into the algorithm through a stochastic ranking procedure. No penalty functions are used. Our experimental results show that the constraint handling technique, i.e., stochastic ranking, can deal with constraints effectively. Our algorithm is able to find a near-optimal feasible solution and scales with the problem size well.     

数据仓库中物化视图的遗传选择算法

针对贪心算法没有考虑物化视图的更新代价和计算量大等缺点,提出了一个物化视图的遗传选择算法。首先通过候选视图选择算法产生候选视图集;其次提出了优化的代价模型,不仅考虑了查询代价,而且考虑了更新代价;最后从候选视图集中选择出物化视图。该算法与贪心算法相比,降低了计算代价。     

Generalized smoothing splines and the optimal discretization of the Wiener filter

We introduce an extended class of cardinal L/sup */L-splines, where L is a pseudo-differential operator satisfying some admissibility conditions. We show that the L/sup */L-spline signal interpolation problem is well posed and that its solution is the unique minimizer of the spline energy functional /spl par/Ls/spl par//sub L2//sup 2/, subject to the interpolation constraint. Next, we consider the corresponding regularized least squares estimation problem, which is more appropriate for dealing with noisy data. The criterion to be minimized is the sum of a quadratic data term, which forces the solution to be close to the input samples, and a "smoothness" term that privileges solutions with small spline energies. Here, too, we find that the optimal solution, among all possible functions, is a cardinal L/sup */L-spline. We show that this smoothing spline estimator has a stable representation in a B-spline-like basis and that its coefficients can be computed by digital filtering of the input signal. We describe an efficient recursive filtering algorithm that is applicable whenever the transfer function of L is rational (which corresponds to the case of exponential splines). We justify these algorithms statistically by establishing an equivalence between L/sup */L smoothing splines and the minimum mean square error (MMSE) estimation of a stationary signal corrupted by white Gaussian noise. In this model-based formulation, the optimum operator L is the whitening filter of the process, and the regularization parameter is proportional to the noise variance. Thus, the proposed formalism yields the optimal discretization of the classical Wiener filter, together with a fast recursive algorithm. It extends the standard Wiener solution by providing the optimal interpolation space. We also present a Bayesian interpretation of the algorithm.     

An evolutionary approach to materialized views selection in a datawarehouse environment

A data warehouse (DW) contains multiple views accessed by queries. One of the most important decisions in designing a DW is selecting views to materialize for the purpose of efficiently supporting decision making. The search space for possible materialized views is exponentially large. Therefore heuristics have been used to search for a near optimal solution. In this paper, we explore the use of an evolutionary algorithm for materialized view selection based on multiple global processing plans for queries. We apply a hybrid evolutionary algorithm to solve three related problems. The first is to optimize queries. The second is to choose the best global processing plan from multiple global processing plans. The third is to select materialized views from a given global processing plan. Our experiment shows that the hybrid evolutionary algorithm delivers better performance than either the evolutionary algorithm or heuristics used alone in terms of the minimal query and maintenance cost and the evaluation cost to obtain the minimal cost     

OLAP性能分析及优化

数据仓库存储大量历史数据,OLAP应用涉及到对大面积历史数据的复杂查询,查询优化是提高OLAP响应速度的关键。目前最有效的方法是增加综合数据存储及查询方式的优化,但存储空间的有限限制了综合数据的存储量。常规优化数据库的方法不能满足OLAP的要求,针对以上出现的问题分别从数据仓库存储优化、OLAP实现方式的选择等工作出发,基于OLAP性能优化的查询优化策略等多角度实现对OLAP响应速度及提高查询优化。对这个问题进行了深入的研究。     

关于实视图维护问题的研究

随着数据仓库技术的迅速发展,实视图作为其中一项可以提供数据存储方式和提高查询响应性能的关键技术得到了充分的重视。但是如何对实视图集进行及时更新,以充分发挥其加快决策查询速度的作用,并满足用户对数据一致性和时新性的要求,却是一个迫切需要解决的关键性技术问题。本文在对实视图维护问题进行了深入研究的基础上,就目前的研究现状作详细的分析和总结。     

The smallest grammar problem

This paper addresses the smallest grammar problem: What is the smallest context-free grammar that generates exactly one given string /spl sigma/? This is a natural question about a fundamental object connected to many fields such as data compression, Kolmogorov complexity, pattern identification, and addition chains. Due to the problem's inherent complexity, our objective is to find an approximation algorithm which finds a small grammar for the input string. We focus attention on the approximation ratio of the algorithm (and implicitly, the worst case behavior) to establish provable performance guarantees and to address shortcomings in the classical measure of redundancy in the literature. Our first results are concern the hardness of approximating the smallest grammar problem. Most notably, we show that every efficient algorithm for the smallest grammar problem has approximation ratio at least 8569/8568 unless P=NP. We then bound approximation ratios for several of the best known grammar-based compression algorithms, including LZ78, B ISECTION, SEQUENTIAL, LONGEST MATCH, GREEDY, and RE-PAIR. Among these, the best upper bound we show is O(n/sup 1/2/). We finish by presenting two novel algorithms with exponentially better ratios of O(log/sup 3/n) and O(log(n/m/sup */)), where m/sup */ is the size of the smallest grammar for that input. The latter algorithm highlights a connection between grammar-based compression and LZ77.     

New View on an Anisotropic Medium and Its Application to Transformation from Anisotropic to Isotropic Problems

The metric factor is defined as m(epsilon*/sub x/, epsilon*/sub y/, theta/sub x/) = /spl radic/ cos/sup 2/theta/sub x/ / epsilon*/sub x/ + sin/sup 2/theta/sub x/ / epsilon*/sub y/ in the radial direction, with the angle theta/sub x/ from the x axis being one of the principal axes in an anisotropic dielectric medium filling the two-dimensional space. The normalized metric factor is defined as n(epsilon*/sub x/, epsilon*/sub y/, theta/sub x/, beta) /spl equiv/ m(epsilon*/sub x/, epsilon*/sub y/, theta/sub x/) / m(epsilon*/sub x/, epsilon*/sub y/, beta) in the form normalized by the metric factor in the direction with the angle beta from the x axis. The effective path length d'/sub P1P2/ between the points P1 and P2 is defined as d'/sup P1P2/ = n(epsilon*/sub x/, epsilon*/sub y/, theta/sub x/, beta)d/sub P1P2/ where d/sub P1P2/ is the actual path length of the straight line P1P2 with the angle theta/sub x/ from the x axis. We propose the minimun principle of the effective path length for electric flux in the region with multilayered anisotropic media. It is applied to solving the electrostatic problem with two anisotropic media whose principal axes are different. We show by using the normalized metric factor that the anisotropic problem can be transformed into the isotropic problem.     

Collision-minimizing CSMA and its applications to wireless sensor networks

Recent research in sensor networks, wireless location systems, and power-saving in ad hoc networks suggests that some applications' wireless traffic be modeled as an event-driven workload: a workload where many nodes send traffic at the time of an event, not all reports of the event are needed by higher level protocols and applications, and events occur infrequently relative to the time needed to deliver all required event reports. We identify several applications that motivate the event-driven workload and propose a protocol that is optimal for this workload. Our proposed protocol, named CSMA/p/sup */, is nonpersistent carrier sense multiple access (CSMA) with a carefully chosen nonuniform probability distribution p/sup */ that nodes use to randomly select contention slots. We show that CSMA/p/sup */ is optimal in the sense that p/sup */ is the unique probability distribution that minimizes collisions between contending stations. CSMA/p/sup */ has knowledge of N. We conclude with an exploration of how p/sup */ could be used to build a more practical medium access control protocol via a probability distribution with no knowledge of N that approximates p/sup */.     

Joint Beamforming and Scheduling for a Multi-Antenna Downlink with Imperfect Transmitter Channel Knowledge

We consider the downlink of a wireless system where the base-station has M ges 1 antennas and K user terminals have one antenna each. We study the weighted rate sum maximization in the case of non-perfect Channel State Information at the Transmitter (CSIT). Some relevant downlink optimization problems, such as the stabilization of the transmission queues under random packet arrivals and the proportional fair scheduling for infinite backlogged systems, can be solved as special cases of the proposed problem. We restrict the transmitter strategy to be based on Gaussian coding and beamforming. Even under this simplifying condition, the problem at hand is non-convex and it does not appear to lend itself to a simple algorithmic solution. Therefore, we introduce some approximations that yield a definition of signal-to-interference plus noise ratio (SINR) commonly used in the classical array- processing/beamforming literature. For the simpler (but still non-convex) approximated problem, we propose a powerful heuristic solution based on greedy user selection and a gradient iteration that converges to a local maximum of the objective function. This method yields very competitive results with relatively low computational complexity. Extensive simulations show that, in the case of perfect CSIT, the proposed heuristic scheme performs very closely to the optimal (dirty-paper coding) strategy while, in the case of non-perfect CSIT, it significantly outperforms previously proposed suboptimal approaches, such as random beamforming and approximated zero-forcing with greedy user selection.     

Environment-Driven Opportunity Forwarding Cross-Layer Optimization for Ubiquitous Wireless Networks

In this paper, an environment-driven cross-layer optimization scheme is proposed to maximize packet forwarding efficiency. The proposed algorithm is aimed to improve the performance of location-based routing protocol in respect of greedy forwarding and avoid void regions for ubiquitous wireless networks. In greedy forwarding mode, we use a new routing metric IAPS which can estimate the forwarding distance, link quality and the difficulty of channel access during the process of the next hop node selection. When the packet forwarding comes into a local minimum, the proposed scheme uses an opportunistic forwarding method based on competitive advantage to bypass the void regions. NS2 simulation results indicate that the proposed algorithm can improve network resource utilization and the average throughput, and reduce congestion loss rate of wireless multi-hop network comparison with existing GPSR algorithm.     

Algorithms for Dynamic Spectrum Access With Learning for Cognitive Radio

We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.      

多小区下行链路系统的资源效率最大化协同波束成形算法

本文研究了基于资源效率优化的多小区多用户协同波束成形算法设计。为了权衡系统频谱效率与能源效率,考虑在单基站发射功率约束以及达到用户服务质量需求条件下最大化系统资源效率,即系统能源效率与归一化系统频谱效率的加权和。由于优化变量之间的耦合性以及约束条件的非凸特性,该优化问题是一种非凸优化问题并且难以直接获得最优解。为了求解所考虑的优化问题,本文联合利用凸近似方法和分数规化理论,提出一种多小区下行链路系统中最大化资源效率的交替优化算法。所提算法的收敛性可以由凸近似方法和单调有界理论保证。同时,数值仿真验证了所提算法的有效性。      

基于可再生能量协作的雾无线接入网络资源分配

针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究.首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配.仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益.     

On the routing and wavelength assignment in multifiber WDM networks

This paper addresses the problem of routing and wavelength assignment (RWA) in multifiber WDM networks with limited resources. Given a traffic matrix, the number of fibers per link, and the number of wavelengths a fiber can support, we seek to maximize the carried traffic of connections. We formulate the problem as an integer linear program (ILP), and show that the lightpaths selected by this formulation can indeed be established by properly configuring the optical switches. An upper bound on the carried traffic can be computed by solving the linear programming (LP)-relaxation of the ILP formulation. It is shown that this bound can be also computed exactly, and in polynomial-time, by solving a significantly simplified LP which considers only one wavelength. The bound can, thus, easily scale to an arbitrarily large number of wavelengths. Furthermore, we demonstrate that any instance of the RWA problem is also an instance of the more general maximum coverage problem. This allows us to take a greedy algorithm for maximum coverage and obtain an algorithm which provides solutions for the RWA problem that are guaranteed to be within a factor of (1-(1/e)) of the optimal solution. Each iteration of the greedy algorithm selects a set of lightpaths that realizes, using one wavelength, the maximum number of connection requests not previously realized. Computational results confirm the high efficiency of our proposed algorithm.     

Resource Allocation for OFDMA Based Cognitive Radio Networks with Arbitrarily Distributed Finite Power Inputs

The existing works on resource allocation for OFDMA based cognitive radio networks are based on the assumption of Gaussian inputs whereas in practical systems the inputs are taken from a set of finite symbol alphabets. This paper considers a system with arbitrarily distributed finite power inputs and solve the resource allocation problem by employing the relationship between mutual information and minimum mean-square error. To protect the primary users’ links, constraint on interference power of the secondary users (SUs) is imposed. In OFDMA based CR networks, a tone can be assigned to one SU at most (exclusivity constraint), due to which the resource allocation problem becomes combinatorial and its solution becomes prohibitively difficult.In this paper, first, the exclusivity constraint on tones allocation is relaxed, the problem is convexified and an optimal solution is derived that provides an upper bound on the system performance. Then, an integer tone allocation and optimal power allocation (ITA–OPA) algorithm is developed that guarantees the assignment of each tone to a single SU with close-to-optimal performance. Finally, keeping in view the complexity of the optimal solution and ITA–OPA algorithm, a low-complexity suboptimal algorithm is devised that accounts for exclusive tone assignment. Simulation results show that the suboptimal algorithm also achieves near-optimal performance. The proposed algorithms outperforms the algorithms that assume Gaussian inputs.     

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Near-optimal depth-constrained codes

This note considers an n-letter alphabet in which the ith letter is accessed with probability p/sub i/. The problem is to design efficient algorithms for constructing near-optimal, depth-constrained Huffman and alphabetic codes. We recast the problem as one of determining a probability vector q/sup */=(q/sup *//sub 1/,...,q/sup *//sub n/) in an appropriate convex set, S, so as to minimize the relative entropy D(p/spl par/q) over all q/spl isin/S. Methods from convex optimization give an explicit solution for q/sup */ in terms of p. We show that the Huffman and alphabetic codes so constructed are within 1 and 2 bits of the corresponding optimal depth-constrained codes.     

业务非均匀分布CDMA系统中一种基于公平性保证的贪婪呼叫接纳控制策略

呼叫接纳控制(Call Admission Control,CAC)是移动通信系统资源管理的主要内容之一。它通过接纳或者拒绝一种用户服务请求,来保持系统的正常运行,是平衡用户服务满意度与系统资源最大化利用矛盾的主要手段。该文主要研究小区间业务非均匀分布时基于公平性保证的动态CAC策略,提出了一种新的CAC策略,可适用于多业务且有变速率(Variable BitRate,VBR)业务的情况。该策略采用贪婪算法,用户请求接入以全系统的预期信干比作为判决条件,从而实现了不同到达率小区之间阻塞率的均衡,保证了用户接入的公平性,对于实际系统的应用具有重要的意义。