Practical Identification of Dynamic Precedence Criteria to Produce Critical Results from Big Data Streams

KAREN WORKS | April 26, 2016

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During periods of high volume, big data stream applications may not have enough resources to process all incoming tuples. To maximize the production of the most critical results under such resource shortages, a recent solution, PR (short for Preferential Result), utilizes both static criteria (defined at compile-time) and dynamic criteria (identified online at run-time) to prioritize the processing of tuples throughout the query pipeline. Unfortunately, locating the optimal criteria placement (i.e., where in the query pipeline to evaluate each prioritization criteria) is extremely compute-intensive and runs in exponential time. This makes PR impractical for complex big data stream systems.

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