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An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

机译:一种支持数据到数据关系工作流注释的增量学习方法

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摘要

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations.
机译:工作流形式化通常集中于流程的表示,其主要目的是支持执行。但是,在某些情况下,需要表示的是过程对所涉及的数据工件的影响,例如,在推理相应的数据策略时。这可以通过在工作流中使用在这些数据工件之间出现的语义关系进行注释来实现。然而,手动产生这样的注释是困难且耗时的。在本文中,我们介绍了一种基于建议的方法来支持用户执行此任务。我们的方法集中在增量规则关联挖掘技术上,该技术可以弥补由于缺少带注释的工作流训练集而引起的冷启动问题。我们将讨论依赖于此方法的工具的实现,以及它在现有工作流存储库上的应用如何有效地实现此类注释的生成。

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