Human behavior based particle swarm optimization for materialized view selection in data warehousing environment

Because of the Materialized View (MV) space value and repair cost limitation in Data Warehouse (DW) environment, the materialization of all views was practically impossible thus suitable MV selection was one of the smart decisions in building DW to get optimal efficiency, at the same time in the modern world, techniques for enhancing DW quality were appeared continuously such as swarm intelligence. Therefore, this paper presents first framework for speeding up query response time depending on Human Particle Swarm Optimization (HPSO) algorithm for determining the best locations of the views in the DW. The results showed that the proposed method for selecting best MV using HPSO algorithm is better than other algorithms via calculating the ratio of query response time on the base tables of DW and compare it to the response time of the same queries on the MVs. Ratio of implementing the query on the base table takes 14 times more time than the query implementation on the MVs. Where the response time of queries through MVs access equal to 106 milliseconds while by direct access queries equal to 1066 milliseconds. This outlines that the performance of query through MVs access is 1471.698% better than those directly access via DW-logical

Optimized Generation and Maintenance of Materialized View using Adaptive Mechanism

Data Warehouse is storage of enormous amount of data gathered from multiple data sources, which is mainly used by managers for analysis purpose. Hence to make this data available in less amount of time is essential. Using Materialize view we can have result of query in less amount of time compared to access the same from base tables. To materialize all of the views is not possible since it requires storage space and maintenance cost. So it is required to select materialized view which minimizes response time of query and cost of maintenance. In this paper, effective approach is suggested for selection and maintenance of materialize view. DOI: 10.17762/ijritcc2321-8169.15050

Clustering-Based Materialized View Selection in Data Warehouses

Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited

Avaliação de algoritmos para a selecção de vistas materializadas em ambientes de data warehousing

A competição no mundo empresarial obriga a uma monitorização mais apertada de todas as variáveis envolvidas nas actividades de negócio. Com o objectivo de suportar o processo de tomada de decisão em factos, e não apenas na intui-ção dos agentes de decisão, surgiram os sistemas de suporte à decisão. Estes sistemas são hoje uma ferramenta chave no processo de tomada de decisão, pois conciliam e integram toda a informação disponível numa única plataforma tec-nológica. Assim, todas as técnicas de optimização do desempenho desses siste-mas são bem-vindas. De entre as diversas técnicas disponíveis, este trabalho concentra-se na materialização de vistas como método de optimização do pro-cessamento de interrogações. A materialização de vistas consiste na antecipação do processamento e armazenamento dos tuplos resultantes do processamento da sua definição numa tabela. De facto, o tempo de reposta a uma interrogação é menor, se as operações intermédias como selecções, projecções, junções e a-gregações se encontrarem já armazenadas numa tabela. Desta forma, o tempo de resposta limita-se ao varrimento da vista materializada. Este artigo apresenta um estudo preliminar para o desenvolvimento de um sistema de gestão de vistas materializadas em ambientes de data warehousing. Neste trabalho comparam-se, basicamente, os comportamentos de dois algoritmos de selecção de vistas materializadas: o BPUS e o A*, ambos algoritmos de procura exaustiva (deter-minísticos)

A Novel Hybrid Optimization With Ensemble Constraint Handling Approach for the Optimal Materialized Views

The datawarehouse is extremely challenging to work with, as doing so necessitates a significant investment of both time and space. As a result, it is essential to enable rapid data processing in order to cut down on the amount of time needed to respond to queries that are sent to the warehouse. To effectively solve this problem, one of the significant approaches that should be taken is to take the view of materialization. It is extremely unlikely that all of the views that can be derived from the data will ever be materialized. As a result, view subsets need to be selected intelligently in order to enable rapid data processing for queries coming from a variety of locations. The Materialized view selection problem is addressed by the model that has been proposed. The model is based on the ensemble constraint handling techniques (ECHT). In order to optimize the problem, we must take into account the constraints, which include the self-adaptive penalty, the Epsilon ()-parameter, and the stochastic ranking. For the purpose of making a quicker and more accurate selection of queries from the data warehouse, the proposed model includes the implementation of an innovative algorithm known as the constrained hybrid Ebola with COATI optimization (CHECO) algorithm. For the purpose of computing the best possible fitness, the goals of "processing cost of the query," "response cost," and "maintenance cost" are each defined. The top views are selected by the CHECO algorithm based on whether or not the defined fitness requirements are met. In the final step of the process, the proposed model is compared to the models already in use in order to validate the performance improvement in terms of a variety of performance metrics

In-memory caching for multi-query optimization of data-intensive scalable computing workloads

In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work. Instead of optimizing jobs independently, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub) expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Experiments on a prototype implementation of our system show significant benefits of worksharing for TPC-DS workloads

Research on Materialized View Selection

定义了数据仓库领域的视图选择问题,并讨论了与该问题相关的代价模型、收益函数、代价计算、约束条件和视图索引等内容;介绍了3大类视图选择方法,即静态方法、动态方法和混合方法,以及各类方法的代表性研究成果;最后展望未来的研究方向.Definition of view selection issue in the field of data warehouses is presented, followed by the discussion of related problems, such as cost model, benefit function, cost computation, restriction condition, view index, etc. Then three categories of view selection methods, namely, static, dynamic and hybrid methods are discussed. For each method, some representative work is introduced. Finally some future trends in this area are discussed.Supported by the National Natural Science Foundation of China under Grant No.60473051 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2007AA01Z191, 2006AA01Z230 (国家高技术研究发展计划(863)