Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By us...Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By using examples in the field of geosciences, we analyze challenges raised by ontology dynamics, such as heavy reworking of data, semantic heterogeneity among data providers and users, and error propagation in cross-discipline data discovery and re-use. We also make recommendations to address these challenges： （1） communities of practice on ontologies to re- duce inconsistency and duplicated efforts; （2） use ontologies in the procedure of data collection and make them accessible to data users; and （3） seek methods to speed up the reworking of data in a Semantic Web context.展开更多
Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)research.However,the boundary of the ML-BEM research has not been clearly defined,and no thorough revie...Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)research.However,the boundary of the ML-BEM research has not been clearly defined,and no thorough review of ML applications in BEM during the whole building life-cycle has been published.This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions.An integrated framework of ML-BEM,composed of four layers and a series of driving factors,is proposed.Then,based on the hype cycle model,this paper analyzes the current development status of ML-BEM and tries to predict its future development trend.Finally,five research directions are discussed:(1)the behavioral impact on BEM,(2)the integration management of renewable energy,(3)security concerns of ML-BEM,(4)extension to other building life-cycle phases,and(5)the focus on fault detection and diagnosis.The findings of this study are believed to provide useful references for future research on ML-BEM.展开更多
文摘Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By using examples in the field of geosciences, we analyze challenges raised by ontology dynamics, such as heavy reworking of data, semantic heterogeneity among data providers and users, and error propagation in cross-discipline data discovery and re-use. We also make recommendations to address these challenges： （1） communities of practice on ontologies to re- duce inconsistency and duplicated efforts; （2） use ontologies in the procedure of data collection and make them accessible to data users; and （3） seek methods to speed up the reworking of data in a Semantic Web context.
基金This work was supported by the National Key R&D Program of China(Grant No.2020YFD1100604)the Science and Technology Commission of Shanghai Municipality(Grant No.19DZ1202800).
文摘Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)research.However,the boundary of the ML-BEM research has not been clearly defined,and no thorough review of ML applications in BEM during the whole building life-cycle has been published.This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions.An integrated framework of ML-BEM,composed of four layers and a series of driving factors,is proposed.Then,based on the hype cycle model,this paper analyzes the current development status of ML-BEM and tries to predict its future development trend.Finally,five research directions are discussed:(1)the behavioral impact on BEM,(2)the integration management of renewable energy,(3)security concerns of ML-BEM,(4)extension to other building life-cycle phases,and(5)the focus on fault detection and diagnosis.The findings of this study are believed to provide useful references for future research on ML-BEM.
