English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While services such as Amazon AWS make computing power abundantly available,adding more computing nodes can incur high costs in, for instance,pay-as-you-go plans while not always significantly improving the net runningtime (aka wall-clock time) of queries. In this work, we provide algorithms forparallel evaluation of SGF queries in MapReduce that optimize total time, whileretaining low net time. Not only can SGF queries specify all semi-joinreducers, but also more expressive queries involving disjunction and negation.Since SGF queries can be seen as Boolean combinations of (potentially nested)semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator thatenables the evaluation of a set of semi-joins in one job. We use this operatorto obtain parallel query plans for SGF queries that outvalue sequential plansw.r.t. net time and provide additional optimizations aimed at minimizing totaltime without severely affecting net time. Even though the latter optimizationsare NP-hard, we present effective greedy algorithms. Our experiments, conductedusing our own implementation Gumbo on top of Hadoop, confirm the usefulness ofparallel query plans, and the effectiveness and scalability of ouroptimizations, all with a significant improvement over Pig and Hive.

Parallel Evaluation of Multi-Semi-Joins