1,720,997 research outputs found

    On the CALM principle for BSP computation

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    In recent times, considerable emphasis has been given to two apparently disjoint research topics: data-parallel and eventually consistent, distributed systems. In this paper we propose a study on an eventually consistent, dataparallel computational model, the keystone of which is provided by the recent finding that a class of programs exists that can be computed in an eventually consistent, coordination-free way: monotonic programs. This principle is called CALM and has been proven by Ameloot et al. for distributed, asynchronous settings. We advocate that CALM should be employed as a basic theoretical tool also for data-parallel systems, wherein computation usually proceeds synchronously in rounds and where communication is assumed to be reliable. We deem this problem relevant and interesting, especially for what concerns parallel workflow optimization, and make the case that CALM does not hold in general for dataparallel systems if the techniques developed by Ameloot et al. are directly used. In this paper we sketch how, using novel techniques, the satisfiability of the if direction of the CALM principle can still be obtained, although just for a subclass of monotonic queries

    Architettura di vetro. Progettare con il vetro

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    Rivista di informazione, attualità e cultura degli Ingegneri di Padova. Editore: Collegio degli Ingegneri della Provincia di Padova. Stampata a Padova da La Photograph; agosto-settembre

    Towards declarative imperative data-parallel systems ?

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    Pushed by recent evolvements in the field of declarative networking and data-parallel computation, we propose a first investigation over a declarative imperative parallel programming model which tries to combine the two worlds. We identify a set of requirements that the model should possess and introduce a conceptual sketch of the system implementing the foresaw model

    Using descriptions for explaining entity matches

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    Finding entity matches in large datasets is currently one of the most attractive research challenges. The recent interest of the research community towards Machine and Deep Learning techniques has led to the development of many and reliable approaches. Nevertheless, these are conceived as black-box tools that identify the matches between the entities provided as input. The lack of explainability of the process hampers its application to real-world scenarios where domain experts need to know and understand the reasons why entities can be considered as match, i.e., they represent the same real-world entity. In this paper, we show how data descriptions—a set of compact, readable and insightful formulas of boolean predicates—can be used to guide domain experts in understanding and evaluating the results of entity matching processes
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