2,353 research outputs found
Communication Efficient Checking of Big Data Operations
We propose fast probabilistic algorithms with low (i.e., sublinear in the
input size) communication volume to check the correctness of operations in Big
Data processing frameworks and distributed databases. Our checkers cover many
of the commonly used operations, including sum, average, median, and minimum
aggregation, as well as sorting, union, merge, and zip. An experimental
evaluation of our implementation in Thrill (Bingmann et al., 2016) confirms the
low overhead and high failure detection rate predicted by theoretical analysis
A resource-frugal probabilistic dictionary and applications in (meta)genomics
Genomic and metagenomic fields, generating huge sets of short genomic
sequences, brought their own share of high performance problems. To extract
relevant pieces of information from the huge data sets generated by current
sequencing techniques, one must rely on extremely scalable methods and
solutions. Indexing billions of objects is a task considered too expensive
while being a fundamental need in this field. In this paper we propose a
straightforward indexing structure that scales to billions of element and we
propose two direct applications in genomics and metagenomics. We show that our
proposal solves problem instances for which no other known solution scales-up.
We believe that many tools and applications could benefit from either the
fundamental data structure we provide or from the applications developed from
this structure.Comment: Submitted to PSC 201
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