Streamable regular transductions

Rajeev Alur, Dana Fisman, Konstantinos Mamouras, Mukund Raghothaman, Caleb Stanford

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Motivated by real-time monitoring and data processing applications, we develop a formal theory of quantitative queries for streaming data that can be evaluated efficiently. We consider the model of unambiguous Cost Register Automata (CRAs), which are machines that combine finite-state control (for identifying regular patterns) with a finite set of data registers (for computing numerical aggregates). The definition of CRAs is parameterized by the collection of numerical operations that can be applied to the registers. These machines give rise to the class of streamable regular transductions (SR), and to the class of streamable linear regular transductions (SLR) when the register updates are copyless, i.e. every register appears at most once in the right-hand-side expressions of the updates. We give a logical characterization of the class SR (resp., SLR) using MSO-definable transformations from strings to DAGs (resp., trees) without backward edges. Additionally, we establish that the two classes SR and SLR are closed under operations that are relevant for designing query languages. Finally, we study the relationship with weighted automata (WA), and show that CRAs over a suitably chosen set of operations correspond to WA, thus establishing that WA are a special case of CRAs.

Original languageEnglish
Pages (from-to)15-41
Number of pages27
JournalTheoretical Computer Science
Volume807
DOIs
StatePublished - 6 Feb 2020

Keywords

  • Cost Register Automata
  • MSO transductions
  • Quantitative automata
  • Regular functions
  • Stream processing
  • Weighted automata

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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