Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological datasets are located on different clouds,that enhances the cost,executio...Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological datasets are located on different clouds,that enhances the cost,execution time,and energy consumption of execution meteorological tasks.Therefore,the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations.To the best of our knowledge,this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout.First,we use the FP-Growth-M(frequent-pattern growth for meteorological model datasets)method to mine the relationship between meteorological models and datasets.Second,based on the relation,we propose a heuristics algorithm for laying out the meteorological datasets and scheduling tasks.Finally,we use simulation results to compare our proposed method with other methods.The simulation results show that our method reduces the number of involved clouds,the sizes of files from outer clouds,and the time of transmitting files.展开更多
One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook,WhatsApp,Twitter,etc.Opinions about ...One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook,WhatsApp,Twitter,etc.Opinions about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social websites.The unique data analytics method cannot be applied to various social websites since the data formats are different.Several approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be improved.The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)approach.SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize.SA-MSVM is implemented,experimented with MATLAB,and the results are verified.The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)approach.SA-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.展开更多
基金funded in part byMajor projects of the National Social Science Fund(16ZDA054)of Chinathe Postgraduate Research&Practice Innovation Program of Jiansu Province(NO.KYCX18_0999)of Chinathe Engineering Research Center for Software Testing and Evaluation of Fujian Province(ST2018004)of China.
文摘Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological datasets are located on different clouds,that enhances the cost,execution time,and energy consumption of execution meteorological tasks.Therefore,the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations.To the best of our knowledge,this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout.First,we use the FP-Growth-M(frequent-pattern growth for meteorological model datasets)method to mine the relationship between meteorological models and datasets.Second,based on the relation,we propose a heuristics algorithm for laying out the meteorological datasets and scheduling tasks.Finally,we use simulation results to compare our proposed method with other methods.The simulation results show that our method reduces the number of involved clouds,the sizes of files from outer clouds,and the time of transmitting files.
文摘One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook,WhatsApp,Twitter,etc.Opinions about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social websites.The unique data analytics method cannot be applied to various social websites since the data formats are different.Several approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be improved.The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)approach.SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize.SA-MSVM is implemented,experimented with MATLAB,and the results are verified.The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)approach.SA-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.
