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@INPROCEEDINGS{Plale00dquob:managing,
    author = {Beth Plale and Karsten Schwan},
    title = {dQUOB: Managing large data flows using dynamic embedded queries},
    booktitle = {In IEEE International High Performance Distributed Computing (HPDC},
    year = {2000},
    pages = {263--270}
}

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Abstract:

The dQUOB system satisfies clients in need of specific information from high-volume data streams. The data streams we speak of are the flows of data that exist in large-scale visualizations, video streaming to a large number of distributed users, and high volume business transactions. dQUOB introduces the idea of conceptualizing a data stream as a set of relational database tables. Within this model, a scientist can request information in an SQL-like query. Transformation or computation that often needs to be performed on the data before it arrives at a client can be conceptualized as computation performed on consecutive views of the data; computation is associated with each view. Additionally, the dQUOB system moves the query code into the data stream as a quoblet; an efficient compiled code. The relational database data model has the significant advantage of presenting opportunities for efficient reoptimizations of queries and sets of queries. Using examples from global atmospheric modeling, we illustrate the usefulness of the dQUOB system. We carry the examples to the experiments and establish the viability of the approach for high performance computing with a baseline benchmark. We define a cost-metric of end-to-end latency that can be used to determine realistic cases where optimization should be applied. Finally, we show that end-to-end latency can be controlled through a probability assigned to a query that a query will evaluate to true.

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