It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in...It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.展开更多
提出并描述了一个基于树型层次结构的计算资源共享与聚集系统(tree-based layered sharing and aggregation,TLSA)。TLSA系统由对等网络环境下的空闲节点组成,形成一个类似B树的层次结构,使在节点加入和退出的时候可以自动的维持平衡。...提出并描述了一个基于树型层次结构的计算资源共享与聚集系统(tree-based layered sharing and aggregation,TLSA)。TLSA系统由对等网络环境下的空闲节点组成,形成一个类似B树的层次结构,使在节点加入和退出的时候可以自动的维持平衡。树型结构的网络拓扑通过自组织的可用性协议来维护,保证了系统的比较低的消息通信量和平衡的处理器负载。通过内部的资源发现协议,节点可以寻找到系统中最近最合适的空闲计算资源来完成大量的子任务。通过模拟测试结果表明对于大规模的子任务,TLSA可以在很短的时间内寻找到空闲资源,而且网络消息通信量不超过O(logmN),具有低消息通信量、非集中性、可扩展性、自组织等特性。展开更多
文摘It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.
文摘提出并描述了一个基于树型层次结构的计算资源共享与聚集系统(tree-based layered sharing and aggregation,TLSA)。TLSA系统由对等网络环境下的空闲节点组成,形成一个类似B树的层次结构,使在节点加入和退出的时候可以自动的维持平衡。树型结构的网络拓扑通过自组织的可用性协议来维护,保证了系统的比较低的消息通信量和平衡的处理器负载。通过内部的资源发现协议,节点可以寻找到系统中最近最合适的空闲计算资源来完成大量的子任务。通过模拟测试结果表明对于大规模的子任务,TLSA可以在很短的时间内寻找到空闲资源,而且网络消息通信量不超过O(logmN),具有低消息通信量、非集中性、可扩展性、自组织等特性。
