Alexander Bianchi, Reza Karegar, P. Godfrey, Lukasz Golab, M. Kargar, D. Srivastava, Jaroslaw Szlichta
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引用次数: 0
Abstract
In this demonstration paper, we describe iORDER, a tool that identifies implicit domain orders in data, such as Small Medium Large. iORDER extends the machinery of order dependency discovery to identify and rank interesting orders. Using real-world data, we showcase how implicit orders help users interpret the semantics of ordered data, how to interactively validate implicit orders to aid in the discovery process, and how to apply implicit orders to applications including data profiling, data mining and knowledge bases.
在这篇演示论文中,我们描述了iORDER,一个识别数据中隐式领域顺序的工具,例如Small Medium Large。iORDER扩展了订单依赖项发现机制,以识别感兴趣的订单并对其进行排序。使用真实世界的数据,我们展示了隐式顺序如何帮助用户解释有序数据的语义,如何交互式地验证隐式顺序以帮助发现过程,以及如何将隐式顺序应用于包括数据分析、数据挖掘和知识库在内的应用程序。