This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed...This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.展开更多
Starting from the transformation and upgrading of traditional culture,the optimization and development of traditional industries,and the innovation of traditional models,this paper used cultural entrepreneurship to tr...Starting from the transformation and upgrading of traditional culture,the optimization and development of traditional industries,and the innovation of traditional models,this paper used cultural entrepreneurship to trigger the thematic business engine,and explored Zhucheng City with its unique dinosaur culture.It proposed"Three-integration"rolling development mode to build Zhucheng into a new highland of cultural entrepreneurship and create new competitiveness under the background of agricultural and rural modernization development.This paper also explored from the perspective of multi-subject collaboration of cultural entrepreneurship.Government needs to establish rolling development pilot projects and promote their application;enterprises need to play the engine role of thematic business through the mode of"getting larger to help smaller ones";the masses need to pay attention to the reaction of culture to the economy,so as to promote the wave of high-level promotion of cultural inheritance and urban-rural integration.展开更多
文摘This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.
文摘Starting from the transformation and upgrading of traditional culture,the optimization and development of traditional industries,and the innovation of traditional models,this paper used cultural entrepreneurship to trigger the thematic business engine,and explored Zhucheng City with its unique dinosaur culture.It proposed"Three-integration"rolling development mode to build Zhucheng into a new highland of cultural entrepreneurship and create new competitiveness under the background of agricultural and rural modernization development.This paper also explored from the perspective of multi-subject collaboration of cultural entrepreneurship.Government needs to establish rolling development pilot projects and promote their application;enterprises need to play the engine role of thematic business through the mode of"getting larger to help smaller ones";the masses need to pay attention to the reaction of culture to the economy,so as to promote the wave of high-level promotion of cultural inheritance and urban-rural integration.
