Nithya N. Vijayakumar

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Data streams flowing from the physical environment are as unpredictable as the environment itself. Radars go down, long haul networks drop packets, and readings are corrupted on the wire. Yet the data driven scientific models and data mining algorithms do not necessarily account for the inaccuracies when assimilating the data. Low overhead provenance(More)
Each year across the United States, destructive weather events disrupt transportation and commerce, resulting in both loss of lives and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure through which scientists can monitor data for specific weather events and launch focused modeling(More)
Grids are built by communities who need a shared cyberinfrastructure to make progress on the critical problems they are currently confronting. A Grid portal is a conventional Web portal that sits on top of a rich collection of web services that allow a community of users access to shared data and application resources without exposing them to the details of(More)
The use of real-time data streams in data-driven computational science is driving the need for stream processing tools that work within the architectural framework of the larger application. Data stream processing systems are beginning to emerge in the commercial space, but these systems fail to address the needs of large-scale scientific applications. In(More)
Advances in numerical modeling, computational hardware, and problem solving environments have driven the growth of computational science over the past decades. Science gateways, based on service oriented architectures and scientific workflows, provide yet another step in democratizing access to advanced numerical and scientific tools, computational resource(More)
We have architected and evaluated a new kind of data resource, one that is composed of a logical collection of ephemeral data streams that could be viewed as a collection of publish-subscribe "channels" over which rich data-access and semantic operations can be performed. This paper contributes new insight to stream processing under the highly asynchronous(More)
Continuous query systems are an intuitive way for users to access streaming data in large-scale scientific applications containing many hundreds of streams. A challenge in these systems is to join streams in such a way that memory is conserved. Storing events that could not possibly participate in a join any longer wastes memory and limits scalability of(More)
Workflow-driven, dynamically adaptive e-Science is a form of scientific investigation often using a Service-Oriented Architecture (SOA) paradigm, designed to use large-scale computational resources on-the-fly to execute workflows consisting of parallel models, analysis, and visualization tasks. In the Linked Environments for Atmospheric Discovery (LEAD)(More)