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

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

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

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 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

Mining Query Plans for Finding Candidate Queries and Sub-Queries for Materialized Views in BI Systems Without Cube Generation

Materialized views are important for optimizing Business Intelligence (BI) systems when they are designed without data cubes. Selecting candidate queries from large number of queries for materialized views is a challenging task. Most of the work done in the past involves finding out frequent queries from the past workload and creating materialized views from such queries by either manually analyzing workload or using approximate string matching algorithms using query text. Most of the existing methods suggest complete queries but ignore query components such as sub queries for creation of materialized views. This paper presents a novel method to determine on which queries and query components materialized views can be created to optimize aggregate and join queries by mining database of query execution plans which are in the form of binary trees. The proposed algorithm showed significant improvement in terms of more number of optimized queries because it is using the execution plan tree of the query as a basis of selection of query to be optimized using materialized views rather than choosing query text which is used by traditional methods. For selecting a correct set of queries to be optimized using materialized views, the paper proposes efficient specialized frequent tree component mining algorithm with novel heuristics to prune search space. These frequent components are used to determine the possible set of candidate queries for creation of materialized views. Experimentation on standard, real and synthetic data sets, and also the theoretical basis, proved that the proposed method is able to optimize a large number of queries with less number of materialized views and showed a significant improvement in performance compared to traditional methods