智慧城市中的大数据

智慧城市的功能之一是构建可智能感知的、泛在化的空间信息服务,并将这些服务按需求进行组合提供灵活的服务,这就要求空间信息服务能更好地适应不断变化的地理上下文环境。设计了上下文感知的空间信息服务的语义模型,该模型扩展了OWL-S本体,增加了地理上下文类、上下文前提条件类、上下文效果类和上下文绑定类以支持地理上下文和地理上下文适应性,并使用智能规划技术和语义增强技术,将上下文感知的空间信息服务组合转化为智能规划的求解过程,提高了服务组合精度。最后通过智慧旅游验证了上下文感知的空间信息服务组合方法的可行性和有效性。

Big Data in Smart City

One of the challenges to build a smart city is to create intelligent and ubiquitous geospatial Web services, and provide a composition of these services available to users. These geospatial Web services must be better-tuned to a given context. Using AI planning techniques and semantic enhancement, this paper presents a dynamic, context-aware service composition method which is achieved by transforming the service composition problem into a planning problem described in a standardized fashion using PDDL. Semantic representation of a geospatial Web service is modeled by extending OWL-S ontology with GeoContext class, GeoContextPrecondition class, GeoContextEffect class and GeoContextBinding class, which support geo-context and geo-context adaptation. Semantic information is used for the enhancement of the composition process as well as for approximating the optimal composite service when exact solutions are not found. Independence from specific planners is maintained. The generating plan is transformed to a WS-BPEL compatible representation, which is executable on the business process execution engine. A case study about smart travel is also presented to demonstrate the functionality, effectiveness and potential of the approach.