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Data Centric Workflows for Crowdsourcing

Pierre Bourhis 1 Hélouët Loïc 2 Zoltan Miklos 3 Rituraj Singh 3
1 LINKS - Linking Dynamic Data
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 SUMO - SUpervision of large MOdular and distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : Crowdsourcing consists in hiring workers on internet to perform large 4 amounts of simple, independent and replicated work units, before assembling the 5 returned results. A challenge to solve intricate problems is to define orchestrations 6 of tasks, and allow higher-order answers where workers can suggest a process to 7 obtain data rather than a plain answer. Another challenge is to guarantee that 8 an orchestration with correct input data terminates, and produces correct out-9 put data. This work proposes complex workflows, a data-centric model for crowd-10 sourcing based on orchestration of concurrent tasks and higher order schemes. We 11 consider termination (whether some/all runs of a complex workflow terminate) and 12 correctness (whether some/all runs of a workflow terminate with data satisfying FO 13 requirements). We show that existential termination/correctness are undecidable 14 in general excepted for specifications with bounded recursion. However, universal 15 termination/correctness are decidable when constraints on inputs are specified in 16 a decidable fragment of FO, and are at least in co−2EXP T IM E.
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https://hal.inria.fr/hal-02508838
Contributor : Loic Helouet <>
Submitted on : Monday, March 16, 2020 - 9:42:21 AM
Last modification on : Wednesday, March 18, 2020 - 1:14:56 AM

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  • HAL Id : hal-02508838, version 1

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Pierre Bourhis, Hélouët Loïc, Zoltan Miklos, Rituraj Singh. Data Centric Workflows for Crowdsourcing. Petri Nets 2020, Jun 2020, Paris, France. ⟨hal-02508838⟩

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