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Project Summary

Data-intensive systems and applications transfer large volumes of data and meta- data to highly distributed users separated by geographic distance and organiza- tional boundaries. A dominating factor in these large volume data transfers is the selection of the appropriate software connector that satisfies user constraints on the required data distribution scenarios.

We present a software architecture- based systematic framework for selecting software connectors based on eight key dimensions of data distribution that we use to represent the data distribution scenarios. Our framework, dubbed DISCO, accurately, efficiently, and reliably captures a guru’s domain knowledge and allows a user to automatically leverage that knowledge to drive connector selection.

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architecture bayesian learning machine software

In a Nutshell, Data Intensive Software Connectors (D...

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30 Day Summary

Dec 24 2018 — Jan 23 2019

12 Month Summary

Jan 23 2018 — Jan 23 2019

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