With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependenc...With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.展开更多
Enterprises in today＇s fast-paced business environment are always puzzled with billions of bytes of data flowing into their computers. In this paper, the new technology to solve this problem called ＂data warehousing...Enterprises in today＇s fast-paced business environment are always puzzled with billions of bytes of data flowing into their computers. In this paper, the new technology to solve this problem called ＂data warehousing＂ is introduced. Benefits which can be achieved from this technology for enterprises are also discussed. In addition, this paper describes ＂SAP Business Information Warehouse＂ （SAP BW）, especially its characteristics, which is the data warehousing solution from SAP. Finally, advantages and shortcomings of SAP BW are given.展开更多
Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for suc...Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.展开更多
基金supported by the National Natural Science Foundation of China (No. 61502043, No. 61132001)Beijing Natural Science Foundation (No. 4162042)BeiJing Talents Fund (No. 2015000020124G082)
文摘With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.
文摘Enterprises in today＇s fast-paced business environment are always puzzled with billions of bytes of data flowing into their computers. In this paper, the new technology to solve this problem called ＂data warehousing＂ is introduced. Benefits which can be achieved from this technology for enterprises are also discussed. In addition, this paper describes ＂SAP Business Information Warehouse＂ （SAP BW）, especially its characteristics, which is the data warehousing solution from SAP. Finally, advantages and shortcomings of SAP BW are given.
文摘Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.
