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On probabilistic fixpoint and Markov chain query languages

Published: 06 June 2010 Publication History

Abstract

We study highly expressive query languages such as datalog, fixpoint, and while-languages on probabilistic databases. We generalize these languages such that computation steps (e.g. datalog rules) can fire probabilistically. We define two possible semantics for such query languages, namely inflationary semantics where the results of each computation step are added to the current database and noninflationary queries that induce a random walk in-between database instances. We then study the complexity of exact and approximate query evaluation under these semantics.

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  • (2023)Generative Datalog with Stable NegationProceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3584372.3588656(21-32)Online publication date: 18-Jun-2023
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  • (2022)Probabilistic DatabasesundefinedOnline publication date: 2-Mar-2022
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cover image ACM Conferences
PODS '10: Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
June 2010
350 pages
ISBN:9781450300339
DOI:10.1145/1807085
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 June 2010

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Author Tags

  1. Markov chains
  2. probabilistic fixpoint

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SIGMOD/PODS '10
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SIGMOD/PODS '10: International Conference on Management of Data
June 6 - 11, 2010
Indiana, Indianapolis, USA

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PODS '10 Paper Acceptance Rate 27 of 113 submissions, 24%;
Overall Acceptance Rate 642 of 2,707 submissions, 24%

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Cited By

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  • (2023)Generative Datalog with Stable NegationProceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3584372.3588656(21-32)Online publication date: 18-Jun-2023
  • (2022)Generative Datalog with Continuous DistributionsJournal of the ACM10.1145/355910269:6(1-52)Online publication date: 17-Nov-2022
  • (2022)Probabilistic DatabasesundefinedOnline publication date: 2-Mar-2022
  • (2020)Generative Datalog with Continuous DistributionsProceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3375395.3387659(347-360)Online publication date: 14-Jun-2020
  • (2018)Efficient provenance tracking for datalog using top-k queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0496-727:2(245-269)Online publication date: 1-Apr-2018
  • (2017)Declarative Probabilistic Programming with DatalogACM Transactions on Database Systems10.1145/313270042:4(1-35)Online publication date: 27-Oct-2017
  • (2016)ENFrameACM Transactions on Database Systems10.1145/287720541:1(1-44)Online publication date: 18-Mar-2016
  • (2014)Computing Refined Ordering Relations with Uncertainty for Acyclic Process ModelsIEEE Transactions on Services Computing10.1109/TSC.2013.197:3(415-426)Online publication date: Jul-2014
  • (2013)Simulation of database-valued markov chains using SimSQLProceedings of the 2013 ACM SIGMOD International Conference on Management of Data10.1145/2463676.2465283(637-648)Online publication date: 22-Jun-2013
  • (2012)Whom to ask?Proceedings of the VLDB Endowment10.14778/2350229.23502645:11(1495-1506)Online publication date: 1-Jul-2012
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