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
SaaS(software as a service,软件即服务)是一种全球兴起的创新的软件服务模式,它的出现对中小企业的信息化产生了深远影响。目前面向SaaS应用的业务逻辑在线定制方法存在着定制复杂性高、可定制内容有限等缺点。为了解决这些问题,采用...SaaS(software as a service,软件即服务)是一种全球兴起的创新的软件服务模式,它的出现对中小企业的信息化产生了深远影响。目前面向SaaS应用的业务逻辑在线定制方法存在着定制复杂性高、可定制内容有限等缺点。为了解决这些问题,采用了基于领域工程的业务规则模板的方法,提出了适合SaaS应用的业务逻辑定制框架,兼顾了应用的易用性及性能。案例表明了此框架的有效性。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SaaS(software as a service,软件即服务)是一种全球兴起的创新的软件服务模式,它的出现对中小企业的信息化产生了深远影响。目前面向SaaS应用的业务逻辑在线定制方法存在着定制复杂性高、可定制内容有限等缺点。为了解决这些问题,采用了基于领域工程的业务规则模板的方法,提出了适合SaaS应用的业务逻辑定制框架,兼顾了应用的易用性及性能。案例表明了此框架的有效性。
