检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]浙江大学机械工程学系,浙江杭州310027 [2]浙江农林大学工程学院,浙江临安311300
出 处:《浙江大学学报(工学版)》2014年第11期2017-2024,共8页Journal of Zhejiang University:Engineering Science
基 金:国家自然科学基金资助项目(61175125);浙江省自然科学基金资助项目(Y1110414);浙江农林大学校科研发展基金(2012FR069)
摘 要:针对供应链环境下制造企业基于海量感知数据的业务处理存在语义异构,同时难以进行集成和高效应用的难题,提出一种本体驱动的分布式信息处理方法.构建基于海量感知数据的供应链事件本体,完善定义和表达企业业务处理粒度,通过本体映射实现异构信息源到统一描述事实组的转换.定义并构建基于事件本体的语义规则语言(SWRL)处理规则,实现企业决策应用.提出一种规则分解和事实分发策略,采用基于MapReduce和Rete算法相结合的分布式处理架构,实现大规模数据的高效处理.通过企业实例对比分析,结果表明,以推理结果准确性和处理效率为指标,验证了该方法的可行性.In order to solve the semantic heterogeneity problems about business event processing of manu- facturing enterprises based on massive perception data in supply chain environment, which made it hard for integrated and efficient application, an ontology driven method for distributed information processing was proposed. Firstly, The perception-based supply chain event ontology was presented and built, which could express enterprise business process preferably, and the conversion from heterogeneous information sources to fact triples described by unified semantics was realized by ontology mapping; then according to the event ontology, the SWRL (Semantic Web Rule Language) based event processing rules were defined and built, which could realize the enterprise decision application. Next a strategy about rule splitting and fact distri- bution was presented, and a distributed processing framework based on rule matching was built by using MapReduee and Rete algorithms, which could handle big data efficiently. At last, taking the result accura- cy and processing efficiency as key indexes, an example based on enterprise data was comparatively ana- lyzed to show that this method is viable.
关 键 词:海量数据 事件本体 规则分解 MAPREDUCE 分布式
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.117