Data skyline query protocol based on parallel genetic improvement decision tree

Query optimization of database requires high speed and high efficiency. In order to solve the low efficiency problem and difficulty in obtaining optimal solution existing in the current query optimization algorithm of database, a query optimization of database based on multi-group firefly algorithm (MGFA) is proposed, combining with characteristics of database query and advantage of firefly algorithm. Firstly, the firefly group is divided into multiple groups with different parameters, and each group of fireflies followed the optimal firefly in the same group for optimizing. Then, mutual learning mechanism is established among various groups of optimal fireflies to realize inter-group information exchange. At last, query optimization data of database are adopted for the simulation experiment. Experiment results indicate that MGFA is a query optimization method of database with good performance. It can obtain better query result than other algorithms do.

[1]  Gang Wang,et al.  Modeling the mitochondrial cardiomyopathy of Barth syndrome with iPSC and heart-on-chip technologies , 2014 .

[2]  M. Hallek,et al.  Ibrutinib combined with bendamustine and rituximab compared with placebo, bendamustine, and rituximab for previously treated chronic lymphocytic leukaemia or small lymphocytic lymphoma (HELIOS): a randomised, double-blind, phase 3 study. , 2016, The Lancet. Oncology.

[3]  Ludovic Duponchel,et al.  Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. , 2014, Food chemistry.

[4]  Edward Z. Zhang,et al.  Deep in vivo photoacoustic imaging of mammalian tissues using a tyrosinase-based genetic reporter , 2015, Nature Photonics.

[5]  Manuel Hidalgo,et al.  Patient-derived xenograft models: an emerging platform for translational cancer research. , 2014, Cancer discovery.

[6]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[7]  Vincent Noireaux,et al.  Linear DNA for rapid prototyping of synthetic biological circuits in an Escherichia coli based TX-TL cell-free system. , 2014, ACS synthetic biology.

[8]  J. G. Burleigh,et al.  Building the avian tree of life using a large-scale, sparse supermatrix. , 2015, Molecular phylogenetics and evolution.

[9]  Zibin Zheng,et al.  Web Service Recommendation via Exploiting Location and QoS Information , 2014, IEEE Transactions on Parallel and Distributed Systems.

[10]  Gary D Bader,et al.  Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance , 2016, Cell.

[11]  Pablo Tamayo,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[12]  Scott A. Mahlke,et al.  Paraprox: pattern-based approximation for data parallel applications , 2014, ASPLOS.

[13]  Alex Olshevsky,et al.  Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control , 2014, 1411.4186.

[14]  F. Cappuzzo,et al.  First-line crizotinib versus chemotherapy in ALK-positive lung cancer. , 2014, The New England journal of medicine.

[15]  Dhundy Bastola,et al.  Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis , 2014, Briefings Bioinform..