用于处理基于度量元数据的体系结构的基于案例的组织内存

Maria de los Ángeles Martín, M. Diván
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引用次数: 8

摘要

为了对组织知识进行管理和检索,近年来出现了许多关于知识管理和知识表示的模型和工具。然而,它们大多以非结构化或半结构化的方式存储知识,阻碍了这些知识的语义和自动处理。本文对基于案例的组织记忆本体进行了综述,旨在为基于案例的组织记忆本体的设计做出贡献,使其能够用于学习、推理、解决问题,并为更好的决策提供支持。这种组织记忆的目标是在专门用于测量和评估的处理体系结构中作为组织知识交换的基础。与度量过程相关的一个关键方面是,为了正确地做出决策,度量必须在任何时刻保持一致和可比较。这样,处理体系结构基于C-INCAMI框架(上下文-信息需求、概念模型、属性、度量和指标)来定义度量项目。此外,提案体系结构使用大数据存储库来提供可供使用的数据,并管理组织内存,这允许与在线处理相关的反馈机制。揭示了数据流处理、大数据存储库和组织记忆之间的关系。为了说明它的效用,给出了一个与INTA Anguil (La Pampa State, Argentina)实验农业站(EAS)的天气雷达(WR)相关的实际案例。此外,还概述了未来的趋势和结束语。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Case based organizational memory for processing architecture based on measurement metadata
With the aim to manage and retrieve the organizational knowledge, in the last years numerous proposals of models and tools for knowledge management and knowledge representation have arisen. However, most of them store knowledge in a non-structured or semi-structured way, hindering the semantic and automatic processing of this knowledge. In this paper we present a summary of an case-based organizational memory ontology, which aims at contributing to the design of an organizational memory based on cases, so that it can be used to learn, reasoning, solve problems, and as support to better decision making as well. The objective of this Organizational Memory is to serve as base for the organizational knowledge exchange in a processing architecture specialized in the measurement and evaluation. One key aspect associated with the measurement process is that the measures must be consistent and comparable in any moment for making decisions properly. In this way, the processing architecture is based on the C-INCAMI framework (Context-Information Need, Concept model, Attribute, Metric and Indicator) to define the measurement projects. Additionally, the proposal architecture uses a big data repository to make available the data for consumption and to manage the Organizational Memory, which allows a feedback mechanism in relation with online processing. The relation between the data stream processing, the big data repository and the Organizational Memory will be shown. In order to illustrate its utility a practical case associated with the weather radar (WR) of the Experimental Agricultural Station (EAS) INTA Anguil (La Pampa State, Argentina) is shown. Also future trends and concluding remarks are outlined.
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