Materialized view selection algorithm—CSSA_VSP

The OLAP (online analytical processing) queries are always involved with queries on the massive dataset. As a result, how to perform multi-table join and aggregate operations becomes the key issue. A Join and Aggregate Algorithm Based on Dimension Hierarchy (JABDH) is proposed in this paper. Considering the semantic characteristic which is not in all the dimension hierarchies, dimension hierarchical encoding is used to retrieve the matching dimension hierarchies and evaluate the set of query ranges for semantic dimension hierarchies. To improve the efficiency of multi-table join and aggregate operations for non-semantic dimensional hierarchies, join and aggregate operations are translated into bitmapped join index of fact table. The performance analysis and experimental results show that JABDH has improved the speed of queries and the efficiency of the OLAP queries.

On-line analytical processing (OLAP) queries are strongly affected by the amount data needed to be accessed from the disk. Therefore, there is a need to employ techniques that can facilitate efficient execution of these queries. There has been a lot of work to optimize the performance of relational data warehouses. Among the two fragmentation techniques, vertical fragmentation is often considered more complicated than horizontal, it nearly impossible to obtain an optimal solution. Data partitioning concept that has been studied in the context of relational databases aims to reduce query execution time and facilitate the parallel execution of queries. In this paper, we develop a new framework based on genetic algorithm for applying the partitioning technique on relational DW schema (star schema) to minimize the total query execution cost. We develop an analytical cost model for executing a set of OLAP queries on a partitioned star schema. We conduct experiments to evaluate the utility of partitioning in efficiently executing OLAP queries. Finally, we show how partitioning can be used to facilitate parallel execution of OLAP queries.

ABSTRACTOne of the complex and time‐consuming stages of land consolidation, where both parcel geometry in rural lands and the ownership structure are changed, is land reallocation. Land reallocation consists of land redistribution and land partitioning stages. Land redistribution aims at careful location of the parcels whose ownership statuses have been changed into land blocks constructed through taking many parameters into account. The focus of this study is the optimization of the land redistribution stage. The performance of many optimization algorithms such as genetic algorithms (GA), Artificial Bee Colony (ABC) Algorithm, and Migrating Birds Optimization (MBO) Algorithm has been investigated for the solution of the land redistribution problem. In this study, we apply the Clonal Selection Algorithm (CSA) to the land redistribution problem for the first time, offering a novel approach to improving allocation efficiency. CSA has been applied to the land redistribution problem, which considers various redistribution parameters. The study area covers 1551.1 ha; there are 351 cadastral parcels and 143 blocks where the parcel areas will be redistributed. So as to test the validity of the application results, the outcomes of CSA were compared with those results of Genetic Algorithms (GA), a biological‐based optimization method previously used to solve the problem. According to the results obtained for GA, the best fitness value was achieved (523.4672), and the placement rate based on the most successful preferences was (67.41294). According to the results obtained for CSA, the best fitness value was achieved (296.4551), and the placement rate based on the most successful preferences was (66.54229). The primary goal of this process was to find the lowest fitness values. In addition, the best suitability solutions found with GA and CSA were compared according to the block fullness rates. The deviations in block occupancy rates obtained by GA are larger than the deviations obtained by CSA.

On-line analytical processing (OLAP) is a technology that encompasses applications requiring a multidimensional and hierarchical view of data. OLAP applications often require fast response time to complex grouping/aggregation queries on enormous quantities of data. Commercial relational database management systems use mainly multiple one-dimensional indexes to process OLAP queries that restrict multiple dimensions. However, in many cases, multidimensional access methods outperform one-dimensional indexing methods.We present an architecture for multidimensional databases that are clustered with respect to multiple hierarchical dimensions. It is based on the star schema and is called CSB star. Then, we focus on heuristically optimizing OLAP queries over this schema using multidimensional access methods. Users can still formulate their queries over a traditional star scheme, which are then rewritten by the query processor over the CSB star. We exploit the different clustering features of the CSB star to efficiently process a class of typical OLAP queries. We detect special cases where the construction of an evaluation plan can be simplified and we discuss improvements of our technique.

Clonal selection algorithms for optimal product line design: A comparative study

ABSTRACTThe optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well‐known test systems (33‐bus and 69‐bus). The uncertainty of power generation from PV‐ and WT‐based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33‐bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69‐bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS‐ based 33‐bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS‐based 69‐bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.

OnLine Analytical Processing (OLAP) research issues (Gray, Chaudhuri, Bosworth, Layman, Reichart & Venkatrao, 1997) such as data cube modeling, representation, indexing and management have traditionally attracted a lot of attention from the Data Warehousing research community. In this respect, as a fundamental issue, the problem of efficiently compressing the data cube plays a leading role, and it has involved a vibrant research activity in the academic as well as industrial world during the last fifteen years. Basically, this problem consists in dealing with massive-in-size data cubes that make access and query evaluation costs prohibitive. The widely accepted solution consists in generating a compressed representation of the target data cube, with the goal of reducing its size (given an input space bound) while admitting loss of information and approximation that are considered irrelevant for OLAP analysis goals (e.g., see (Cuzzocrea, 2005a)). Compressed representations are also referred-in-literature under the term "synopsis data structures", i.e. succinct representations of original data cubes introducing a limited loss of information. Benefits deriving from the data cube compression approach can be summarized in a relevant reduction of computational overheads needed to both represent the data cube and evaluate resource-intensive OLAP queries, which constitute a wide query class allowing us to extract useful knowledge from huge amounts of multidimensional data repositories in the vest of summarized information (e.g., aggregate information based on popular SQL aggregate operators such as SUM, COUNT, AVG etc), otherwise infeasible by means of traditional OLTP approaches. Among such queries, we recall: (i) range-queries, which extract a sub-cube bounded by a given range; (ii) top-k queries, which extract the k data cells (such that k is an input parameter) having the highest aggregate values; (iii) iceberg queries, which extract the data cells having aggregate values above a given threshold. This evidence has given raise to the proliferation of a number of Approximate Query Answering (AQA) techniques, which, based on data cube compression, aim at providing approximate answers to resource-intensive OLAP queries instead of computing exact answers, as decimal precision is usually negligible in OLAP query and report activities (e.g., see (Cuzzocrea, 2005a)). Starting from these considerations, in this article we first provide a comprehensive, rigorous survey of main data cube compression techniques, i.e. histograms, wavelets, and sampling. Then, we complete our analytical contribution with a detailed theoretical review focused on spatio-temporal complexities of these techniques. Request access from your librarian to read this chapter's full text.

The collision-free path planning is crucial for an autonomous vehicle. It saves life and helps to complete the task in time. The computational intelligence mimics human intelligence and solves these types of problems in which conventional techniques fail to provide optimal solutions. In literature, computational intelligence techniques except evolutionary techniques have not been utilized for land vehicles. In this regard, we employ clonal selection algorithms for collision avoidance in an autonomous vehicle. To check the effectiveness of our proposed scheme, we compare the performance of the clonal selection algorithm with the genetic algorithm. The results show that the clonal selection algorithm performs better than the genetic algorithm in terms of computation time and also avoids accident within the required time to avoidance.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

For female college students, due to individual differences such as age, physical fitness and sports foundation, a unified Latin aerobics exercise plan is difficult to meet everyone’s needs. Genetic algorithm, with its powerful search ability and global optimization performance, can be applied to the planning of Latin aerobics exercise plans for female college students and can generate optimal solutions for Latin aerobics exercise plans. Therefore, this study proposes a planning method for female college students’ Latin aerobics exercise plan based on a genetic algorithm. This method is based on Bluetooth power consumption technology to collect heart rate data from Latin aerobics exercises, collect images of female college students’ Latin aerobics exercises and reconstruct them, recognize Latin aerobics exercise postures based on K-nearest neighbor algorithm, and implement motion trajectory monitoring based on Kinovea software. On this basis, a model of Latin aerobics exercise plan for female college students is constructed, and the Latin aerobics exercise plan is optimized through a genetic algorithm. By using the search and optimization ability of the genetic algorithm, the optimal combination of exercise plans can be found, and an exercise plan that meets the physiological and psychological characteristics of female college students, effectively enhances physical fitness and improves dance skills can be formulated. Through experimental verification, it has been found that the proposed genetic algorithm has good global search ability. The planned Latin aerobics exercise plan can ensure that the heart rate falls within a safe and effective range, reduce the deviation of the body’s left and right swing amplitude during Latin aerobics exercise, and improve the personalized satisfaction of female college students. By applying genetic algorithms, female college students can achieve their goals of physical health and body shaping while enjoying the fun of Latin aerobics.

The key parameters for Simulated Annealing Algorithm (SAA) and Genetic Algorithm (GA) were determined by test experiments. With optimal parameters obtained for both algorithms, the multilayer antireflection coatings in the 7.7-12.3μm band and a neutral beam splitter in the visible region were designed. Results showed that, for infrared multilayer antireflection coatings, the evaluation function value of SAA was 18.7% lower than that of GA, and the average transmittance of SAA was 0.4% higher than that of GA. Compared to GA, SAA achieved a higher average transmittance and a more flat transmittance curve. And for the neutral beam splitter, the running time of GA was 0.407s less than that of SAA, whereas the convergence effect of SAA was superior to that of GA. Therefore, the design with SAA has certain advantages for both the infrared multilayer antireflection coating and the beam splitter.

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a Big Data scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive.

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

Artificial immune systems are classified as computational systems inspired by theoretical immunology and are mechanisms that can solve complex problems. Clonal selection algorithm is a selection algorithm, which is derived as a special selection algorithm to this artificial intelligence concept. In this work, Clonal selection algorithm is applied to design a simple microwave matching network for a load with arbirtary impedance. The results obtained were tested in a microwave simulator and indicated that clonal selection algorithm can be very effectively applied for microwave design problems. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:991–993, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25935

Chaos theory describes complex motion and the dynamics of nonlinear systems. As a complex nonlinear system, the immune system is chaotic. This paper introduces chaos into the clonal selection algorithm by a novel mutation method, self-adaptive chaotic mutation (SACM). In detail, based on the logistic chaotic sequence, SACM extracts antibody's affinity and distribution to adjust the mutation scale. Compared with the clonal selection algorithm using random mutation and standard genetic algorithm, the improved clonal selection algorithm can enhance the precision and stability, and overcome prematurity effectively with a high convergence speed.