Computing marginals using MapReduce

Foto N. Afrati, Shantanu Sharma, Jonathan R. Ullman, Jeffrey D. Ullman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We consider the problem of computing data-cube marginals by a single round of MapReduce, focusing on the relationship between the reducer size and the replication rate. Initially, we simplify the problem by making the extent of each dimension the same. Several recursive constructions meet or come close to the minimum possible replication rate for a given reducer size. These ideas extend in two directions. We relax the assumption that the extents are all equal, and we consider how to compute marginals from lower-order marginals rather than from the raw data cube.

Original languageEnglish (US)
Pages (from-to)98-117
Number of pages20
JournalJournal of Computer and System Sciences
StatePublished - Jun 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics


  • Data-cube
  • MapReduce
  • Marginals


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