Research On Several Key Technologies Of Data Service In Cloud Environment

In cloud computing environment,data resources stored in different departments and enterprises have the characteristics of cross-domain,distributed,diverse and data sensitivity.As a new cloud computing service model,data as a service(DaaS)has drew widely attention in the integration,processing,mining and analysis of cross-domain heterogeneous data.Data service is a data model with a unified description mode,which is the core technology of DaaS.It can publish data resources in the form of services and provide users with unified data access interfaces.However,the diversity of data resources and the dynamics of user requirements pose significant challenges to data service technologies.This dissertation studies the automatic data service encapsulation and composition,data composition view generation and real-time update in cloud environment.The main research contents include:(1)A data service generation approach for cross-origin datasets is proposed.An attribute dependency graph(ADG)is constructed by using inherent data dependency.Based on the ADG,an automatic data service extraction algorithm is implemented.Then,a data service encapsulation framework is designed.Via a flexible RESTful service template,this framework can automatically encapsulate the extracted data services into the RESTful services which can be accessed by the exposed interfaces.Experimental results show that the proposed approach improves the automation of data service generation and obtains the data services with highly mature.(2)A data composition view generation approach is proposed.Data services are organized into a data service dependence graph(DSDG)based on the inherent data dependence,and automatic data service composition is implemented based on the DSDG according to user's data requirements.Then,the data composition view is automatically generated by performing the composite data service(CDS).Experimental results show that the proposed approach has high efficiency and can get the optimal composition.(3)A data composition view positioning update approach with incremental logs is proposed.The latest data changes of data source are captured according to the incremental logs.The attributes and tuples in data composition view are indexed.The index numbers that differ from the data sources can be calculated with positioning attributes.The corresponding composition view update operations are performed according to the type of data changes.Experimental results show that the proposed approach is more efficient than existing update approaches.Taking the elevator data ftom different departments as an example,a cross-origin heterogeneous elevator data service system has been developed.The main function modules of system include data services generation,elevator service dependency graph visualization,elevator composite data service composition and elevator data combination view generation.The system can effectively integrate and share elevator data,and provide a basis for elevator data mining and analysis.