设计用于管理模型生命周期的仪表板

Jhilmil Jain, Ismail Ari, Jun Li
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引用次数: 3

摘要

数据挖掘被广泛用于分析大型数据集合。虽然之前有关于仪表板支持数据可视化挖掘的工作,但很少或根本没有关于仪表板支持管理数据挖掘模型本身的生命周期(例如运行状况)的工作。诸如快速性能衰减、大规模部署、协作使用以及模型的实时业务集成等问题都需要这种类型的支持。本文在长达一年的研究基础上,首先描述了模型生命周期的六个阶段,并初步设计了帮助用户管理挖掘模型的后端系统。接下来,我们将讨论模型生命周期的仪表板可视化要考虑的三个维度:自省、自定义和表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing dashboards for managing model lifecycle
Data mining is being extensively used for analysis of large data collections. While there is previous work on dashboard support for visual mining of the data, there is little or no work on dashboard support for managing the lifecycle (e.g. health) of the data mining models themselves. Issues such as quick performance decay, large scale deployments, collaborative use, and real-time business integration of models necessitate this type of support. In this paper, based on a year long study, we first describe the six stages of the model lifecycle and the preliminary design of the backend system that helps users manage mining models. Next, we discuss the three dimensions to be considered for dashboard visualization of the model lifecycle: introspection, customization, and presentation.
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