Spatial networks such as road networks, river networks, telephone networks, and power networks are ubiquitous spatial concepts deployed, for example, in route planning, communication services, high voltage grid topology analysis, and utility management. Current database systems are unable to efficiently handle, represent, store, query, and manipulate large spatial networks. Moreover, data models of spatial networks in a database context are rare due to their inherently complex nature. This paper offers a conceptual foundation called Spatial Network Algebra (SNAL) for designing, characterizing, and representing spatial networks. A general-purpose abstract model is proposed as a specification for a later implementation of spatial networks in different environments such as spatial database systems and GIS.
Data about moving objects is being collected in many different application domains with the help of sensor networks, and GPS-enabled devices. In most cases, the moving objects are not free to move, they are usually restricted by some spatial constraints such as Spatial Networks. Spatial networks are ubiquitous and have been widely used in transportation, traffic planning, navigation as well as in Geographical Information System (GIS) applications. In most scenarios, moving objects such as vehicles move along predefined spatial networks like transportation networks. Unfortunately, the concepts for modeling and querying objects in unconstrained spaces like an outdoor space cannot be transferred to constrained spaces like a road network due to the different features of the environments in which the spatial objects move. Further, modern positioning devices as well as mobile and sensor technology have led to large volumes of moving objects in spatial networks. Therefore, we need a database-friendly data model to explicitly model spatial networks and, more importantly, describe relative movements in these networks. In this paper, we propose a new two-layered data model called MONET (Moving Objects in NETworks) model. The lower layer is a data model for spatial networks. This data model is the prerequisite for the upper model that represents moving objects in these networks. This layered model fits well to formulate relationships between moving objects and a network in queries. A query language, called MONET QL (MONET Query Language), allows a clear description of and access to moving objects in spatial networks and to provides high-level operations on them.
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