Selecting Materialized Views based on Genetic Algorithm

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

A data warehouse is designed for answering analytical queries. These queries are usually long, complex and ad hoc in nature and their response time is high when processed against an exponentially growing data warehouse. Materialising views has been found to be an effective tool for reducing this response time. All views cannot be materialised on account of resource constraints. Further, optimal view selection is shown to be an NP-complete problem. This necessitates selection of an appropriate set of views, from amongst all possible views that reduce the query response time. Most of the view selection algorithms are greedy or evolutionary. In this paper, a differential evolution view selection algorithm (DEVSA) that selects the Top-K views from a multi-dimensional lattice is proposed. Further, it is shown that DEVSA when compared to the greedy, genetic and memetic-based view selection algorithms selects comparatively better quality views for higher dimensional datasets.

Reducing the Multidimensionality of OLAP Cubes with Genetic Algorithms and Multiple Correspondence Analysis

The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data warehouses (DW) operate over static data. However multidimensional (MD) data modeling approach can be dynamically extended by defining both the schema and instances of MD data as RDF graphs. The importance and applicability of MD data warehouses over RDF is widely studied yet none of the works support a spatially enhanced MD model on the SW. Spatial support in DWs is a desirable feature for enhanced analysis, since adding encoded spatial information of the data allows to query with spatial functions. In this paper we propose to empower the spatial dimension of data warehouses by adding spatial data types and topological relationships to the existing QB4OLAP vocabulary, which already supports the representation of the constructs of the MD models in RDF. With QB4SOLAP, spatial constructs of the MD models can be also published in RDF, which allows to implement spatial and metric analysis on spatial members along with OLAP operations. In our contribution, we describe a set of spatial OLAP (SOLAP) operations, demonstrate a spatially extended metamodel as, QB4SOLAP, and apply it on a use case scenario. Finally, we show how these SOLAP queries can be expressed in SPARQL.

Materialized view selection under the maintenance time constraint

A Data warehouse is a structure that stores big amount of data. This data is exploited in the best possible ways in order to improve the efficiency of decision-making. The huge volume of data makes answering queries complex and time-consuming. Therefore, materialized views are used in order to reduce the query processing time. Since materializing all views is not possible, due to space and maintenance constraints, materialized view selection became one of the crucial decisions in designing a data warehouse for optimal efficiency. In this paper, we propose a discrete - quantum based - version of Differential Evolution DE algorithm named QDE algorithm to solve the materialized view selection (MVS) problem with space constraint. This algorithm is a merging of the original DE with Quantum Evolutionary (QEA) algorithm. The experimental results show the efficiency of the proposed algorithm compared to well known algorithms used to solve MVS problem with space constraint such as HRUA and GEA.

Publicly accessible data warehouses are an indispensable resource for data analysis. But they also pose a significant risk to the privacy of the clients, since a data warehouse operator may follow the client's queries and infer what the client is interested in. Private Information Retrieval (PIR) techniques allow the client to retrieve a cell from a data warehouse without revealing to the operator which cell is retrieved. However, PIR cannot be used to hide OLAP operations performed by the client, which may disclose the client's interest. This paper presents a solution for private data warehouse queries on the basis of the Boneh-Goh-Nissim cryptosystem which allows one to evaluate any multi-variate polynomial of total degree 2 on ciphertexts. By our solution, the client can perform OLAP operations on the data warehouse and retrieve one (or more) cell without revealing any information about which cell is selected. Furthermore, our solution supports some types of statistical analysis on data warehouse, such as regression and variance analysis, without revealing the client's interest. Our solution ensures both the server's security and the client's security.

Development of a Dashboard to Improve Safety in the Imaging Setting

In this paper the image compression problem is analyzed using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs the clustering efficiently. The possibility of solving clustering problems with genetic algorithms provides optimal solution. The present work makes use of genetic clustering algorithms to obtain an ordered representation of the image and then applies the DWT (Discrete Wavelet Transformation) to compress the image. The image quality is measured using PSNR measure. The genetic algorithm (GA)-based compression image has more PSNR value compared to the image without GA. It shows that the quality of the image is good for GA based compression. Also it reduces the problem of memory utilization efficiently due to compression and at the same time quality of the image is maintained due to GA. Keywords: Genetic algorithms, clustering, image compression, discrete wavelet transformation.

Selecting the materialized views optimally is very important in designing a data warehouse and is NP-hard problem. Various evolutionary algorithms exist in literature for the appropriate selection of materialized views. In this paper, we have examined the application of backtracking search optimization algorithm (BSA), for selecting the materialized views in data warehouse. According to our experiments, the results obtained by our proposed backtracking search optimization-based materialized view selection algorithm (BSMVSA) are superior to those found using particle swarm optimization and genetic algorithm. The solution obtained by BSMVSA greatly reduces the total cost within the storage constraint.

Data warehouse is an inseparable part of modern business but the amount of data it stores is huge and growing so fast, the queries of the business leaders it processes have become very sophisticated as well. Data warehouse needs to response to those queries with high quality data having less response time but the major challenge for that is the scale of data and nature of the queries involved. It’s very hard to access the required data set with minimum response time in data processing as the data warehouse needs to perform analytical operation on a large set of data analyzing sophisticated queries. This paper introduces a new data warehousing schema that constructs a multidimensional relation tree hierarchy, changing the traditional use of relational database, with partitioned database in its every level instead of any individual data. This schema allows data warehouse to partition and process a large data part by part in a small batch to minimize the response time and stores summarized data to increase data availability in query processing. Allowing easy and fast access to the required set of data, this schema improves the performance and the decision making ability of the data warehouse.

A company, majorly company that active in commercial (profit orientation) need to analyze their sales performance. By analyzing sales performance, company can increase their sales performance. One of method to analyze sales performance is by collecting historical data that relates to sales and then process that data so that produce information that show company sales performance. A data warehouse is a set of data that has characteristic subject oriented, time variant, integrated, and nonvolatile that help company management in processing of decision making. Design of data warehouse is started from collecting data that relate to sales such as product, customer, sales area, sales transaction, etc. After collecting the data, next is data extraction and transformation. Data extraction is a process f or selecting data that will be loaded into data warehouse. Data transformation is making some change to the data afte r extracted to be more consistent. After transformation processing, data are loaded into data warehouse. Data in data warehouse is processed by OLAP (On Line Analytical Processing) to produce information. Information that are produced from data processing by OLAP are chart and query reporting. Chart reporting are sales chart based on cement type, sales chart based on sales area, sales chart based on plant, monthly and year ly sales chart, and chart based on customer feedback. Query reporting are sales based on cement type, sales area, plant and customer. Keywords: Data warehouse; OLAP; Sales performance analysis; Ready mix market

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed over the past decade by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are not immediately obvious. First, Web technologies make data warehouse data more easily available to a much wider variety of users. Second, data warehouse technologies can be used to analyze traffic to a Web site in order to gain a much better understanding of the visitors to the Web site. It is this second synergy that is the focus of this article.

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

A Data Warehouse (DW) can be abstractly seen as a set of materialized views defined over a set of remote data sources. A DW is intended to satisfy a set of queries. The views materialized in a DWrelate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries or for performance reasons while old queries are dropped. The evolution of a DWcan result in a redundant set of materialized views.In this paper we address the problem of detecting redundant views in a given DW view selection, that is, views that can be removed from the DW without negatively affecting the query evaluation or the view maintenance process. Using an AND/OR dag representation for multiple queries and views, we first provide a method for detecting materialized views that are not needed in the process of propagating source relation changes to the DW. Then, we use this method to detect materialized views that are redundant. As a side effect, our approach shows how source relation changes can be propagated to the DW materialized views by exploiting common subexpressions between views and by using other materialized views that are not affected by these changes.