III-CXT: Spatio-temporal Graph Databases for Transportation Science
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چکیده
Project Summary The recent loss of lives, and traffic jams stretching for tens of miles as hurricanes Rita and Katrina approached the Gulf Coast demonstrate the enormous difficulty of evacuating urban areas. Mass evacuations are among the most difficult problem areas in Transportation Science because they violate key assumptions underlying traditional theories, e.g., Wardrop equilibrium among selfish commuters. A key challenge in this domain is to develop an understanding of non-equilibrium traffic dynamics over transportation networks towards the design of emergency traffic management techniques. This is a formidable task due to the data-intensive nature of the problem, and the semantic gap between current database management systems and transportation science. The goal of this project is to research novel and scalable data management concepts to aid in the development of novel transportation science models and theories to understand emergency traffic. New col-laborative computer science research is proposed to probe innovative database concepts underlying network non-equilibrium dynamics data and queries. New fundamental Computer Science research is proposed on database support for time-variant graphs and flow networks. The researchers on this multidisciplinary team not only have strong track records in both spatial databases and transportation science but they have also already worked collaboratively for the past two years. This proposal complements individual projects of the PIs by stimulating multidisciplinary research in science informatics. Intellectual Merit: The proposed approaches to time-variant graphs and spatio-temporal database support for flow networks significantly differ from the traditional approaches in the database literature. This project is expected to result in III-CXT innovations in the following areas. First, graph-aggregates, a novel representation of time-varying graphs, will be explored along with appropriate conceptual, logical and physical data models. Second, database support for flow network operations, e.g. min-cut, and max-flow, will be investigated. For example, we will examine I/O-scalability of alternative flow algorithms to large spatio-temporal graph datasets using implementations on common database server architectures as well as direct implementation using file systems. Third, the proposed database concepts will be designed and evaluated in collaboration with domain scientists and professionals using grand challenge problems (e.g., emergency traffic management) and datasets (e.g. large urban evacuation scenarios, population distributions, and flow networks). We believe that the proposed research will significantly enhance domain scientists' ability to understand and manage non-equilibrium network behavior, not only in Transportation Science, but also in many other important domains including logistics, telecommunication networks, electric power grids, and distribution networks …
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