Data-parallel computing meets STRIPS

Erez Karpas, Tomer Sagi, Carmel Domshlak, Avigdor Gal, Avi Mendelson, Moshe Tennenholtz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The increased demand for distributed computations on big data has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, query plans on the user. In an attempt to ease this burden, we propose a formal model that allows for data-parallel program synthesis (DPPS) in a semantically well-defined manner. We show that this model generalizes existing frameworks for dataparallel computation, while providing the flexibility of query plan generation that is currently absent from these frameworks. In particular, we show how existing, off the- shelf, AI planning tools can be used for solving DPPS tasks.

Original languageEnglish
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Number of pages7
StatePublished - 2013
Externally publishedYes
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: 14 Jul 201318 Jul 2013

Publication series

NameProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013


Conference27th AAAI Conference on Artificial Intelligence, AAAI 2013
Country/TerritoryUnited States
CityBellevue, WA

ASJC Scopus subject areas

  • Artificial Intelligence


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