Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal v...Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries simultaneously.This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs.The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint handling.These two constraints helped the proposed model select the finest views that minimize the objective function.Further,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal MVs.Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population.By combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized costs.Several cost functions are described to enable the algorithm to choose the finest solution from the problem space.Finally,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms.The proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.展开更多
The condition of weightes non-dictatorship is extended and a comprehensive evaluae method emboding self-determinate which is combined with competitive view optimization principles is built. The basic process includes ...The condition of weightes non-dictatorship is extended and a comprehensive evaluae method emboding self-determinate which is combined with competitive view optimization principles is built. The basic process includes simulating the model of economic man's self-benefit bahaviors, taking the place of experts to evaluate, bringing in the model of minimizing the sum of included angles to integrate the information of multiple objects and put the objects in order finally. The method has the advangtages of less dependendence on the subjective information, plenty of information, fair process and simple caculating. Finally, an application example is given to illustrate the effectiveness of the proposed method.展开更多
文摘Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries simultaneously.This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs.The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint handling.These two constraints helped the proposed model select the finest views that minimize the objective function.Further,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal MVs.Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population.By combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized costs.Several cost functions are described to enable the algorithm to choose the finest solution from the problem space.Finally,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms.The proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.
基金supported by the National Natural Science Foundation of China(70801013)LNSTF for doc-tor(20081020).
文摘The condition of weightes non-dictatorship is extended and a comprehensive evaluae method emboding self-determinate which is combined with competitive view optimization principles is built. The basic process includes simulating the model of economic man's self-benefit bahaviors, taking the place of experts to evaluate, bringing in the model of minimizing the sum of included angles to integrate the information of multiple objects and put the objects in order finally. The method has the advangtages of less dependendence on the subjective information, plenty of information, fair process and simple caculating. Finally, an application example is given to illustrate the effectiveness of the proposed method.