Applying evolutionary algorithms to materialized view selection in a data warehouse

[[abstract]]Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. A data warehouse [14,16] plays an important role for storage and analysis for genome sequence and functional data. A data warehouse stores lots of materialized views to provide an efficient decision-support or OLAP queries. The view-selection problem addresses to select a fittest set of materialized views from a variety of MVPPs 0 forms a challenge in data warehouse research. In this paper, we present genetic algorithm to choose materialized views. We also use experiments to demonstrate the power of our approach

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses

EFFICIENT APPROACH FOR VIEW SELECTION FOR DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTATION

Selection of a proper set of views to materialize plays an important role indatabase performance. There are many methods of view selection which uses different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient, scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. Tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. Query Cost model achieves the objective of maximizing the performance benefits from the final view set which is derived from the frequent view set given by tree mining algorithm. Performance benefit of a query is defined as a function of queryfrequency, query creation cost, and query maintenance cost. The experimental results shows that the proposed method is successful in recommending a solution which is fairly close to optimal solution

Materializing views in data warehouse: an efficient approach to OLAP.

Gou Gang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Acknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Warehouse and OLAP --- p.4Chapter 1.2 --- Computational Model: Dependent Lattice --- p.10Chapter 1.3 --- Materialized View Selection --- p.12Chapter 1.3.1 --- Materialized View Selection under a Disk-Space Constraint --- p.13Chapter 1.3.2 --- Materialized View Selection under a Maintenance-Time Con- straint --- p.16Chapter 1.4 --- Main Contributions --- p.21Chapter 2 --- A* Search: View Selection under a Disk-Space Constraint --- p.24Chapter 2.1 --- The Weakness of Greedy Algorithms --- p.25Chapter 2.2 --- A*-algorithm --- p.29Chapter 2.2.1 --- An Estimation Function --- p.36Chapter 2.2.2 --- Pruning Feasible Subtrees --- p.38Chapter 2.2.3 --- Approaching the Optimal Solution from Two Directions --- p.41Chapter 2.2.4 --- NIBS Order: Accelerating Convergence --- p.43Chapter 2.2.5 --- Sliding Techniques: Eliminating Redundant H-Computation --- p.45Chapter 2.2.6 --- Examples --- p.50Chapter 2.3 --- Experiment Results --- p.54Chapter 2.3.1 --- Analysis of Experiment Results --- p.55Chapter 2.3.2 --- Computing for a Series of S Constraints --- p.60Chapter 2.4 --- Conclusions --- p.62Chapter 3 --- Randomized Search: View Selection under a Maintenance-Time Constraint --- p.64Chapter 3.1 --- Non-monotonic Property --- p.65Chapter 3.2 --- A Stochastic-Ranking-Based Evolutionary Algorithm --- p.67Chapter 3.2.1 --- A Basic Evolutionary Algorithm --- p.68Chapter 3.2.2 --- The Weakness of the rg-Method --- p.69Chapter 3.2.3 --- Stochastic Ranking: a Novel Constraint Handling Technique --- p.70Chapter 3.2.4 --- View Selection Using the Stochastic-Ranking-Based Evolu- tionary Algorithm --- p.72Chapter 3.3 --- Conclusions --- p.74Chapter 4 --- Conclusions --- p.75Chapter 4.1 --- Thesis Review --- p.76Chapter 4.2 --- Future Work --- p.78Chapter A --- My Publications for This Thesis --- p.81Bibliography --- p.8

Математична модель задачі проектування гібридних сховищ даних з врахуванням структур джерел даних

У статті розглядається питання формулювання математичної моделі задачі проектування гібридних сховищ даних (ГСД). Приводиться опис існуючих рішень та описується придатність їх застосування. Пропонується математична модель, яка включає опис джерел даних і сховища даних, а також оптимізаційних параметрів та рівнянь.In paper mathematical model formulation problem for hybrid data warehouse (HDW) building is discussed. An overview of existing solutions is described and their applicability is stated. Author introduces a mathematical model which includes data sources and warehouses description. Also, this model includes optimization parameters and equations

A solution to the materialized view selection problem in data warehousing

One of the most important decisions in the physical designing of a data warehouse is the selection of materialized views and indexes to be created. The problem is to select an appropriate set of views and indexes to storage that minimizes the total query response time, as long as the cost of maintaining them, given a constraint of some resource like storage space, is kept as low as possible.In this work, we have developed a new algorithm for the general problem of se-lection of views considering indexes, as an extension to a well-known algorithm. We present a heuristic for selection of views and indexes to optimize total que-ry response under a materialization time constraint. Finally, we present an ex-perimental comparison of our proposal with the considered state-of-art ap-proach.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI

A solution to the materialized view selection problem in data warehousing

One of the most important decisions in the physical designing of a data warehouse is the selection of materialized views and indexes to be created. The problem is to select an appropriate set of views and indexes to storage that minimizes the total query response time, as long as the cost of maintaining them, given a constraint of some resource like storage space, is kept as low as possible.In this work, we have developed a new algorithm for the general problem of se-lection of views considering indexes, as an extension to a well-known algorithm. We present a heuristic for selection of views and indexes to optimize total que-ry response under a materialization time constraint. Finally, we present an ex-perimental comparison of our proposal with the considered state-of-art ap-proach.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI