基于大数据环境的预警系统框架方案

G. Klepac, R. Kopal, Leo Mršić
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引用次数: 0

摘要

建立预警系统的目的是为了尽早有效地识别与公司业务相关的异常和潜在危险趋势。大数据环境为分析过程提供了新的机会和新的方法。在公司内部建立预警系统的方法有很多种。大数据环境迫使企业采用新的思维方式,并使用新的一次性数据源。本文为企业内部预警系统的设计提供了一种新颖的概念,适用于不同的行业。该框架的核心是一个混合模糊专家系统,除了传统的规则块之外,该系统还可以包含负责某些特定领域的各种数据挖掘预测模型。它还可以包含基于语言变量的社交网络分析指标,并将其合并到规则块中。作为该框架的一部分,SNA方法也被解释和介绍为现代预警系统中使用的强大和独特的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Warning System Framework Proposal, Based on Big Data Environment
Early warning systems are made with the purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible. The Big Data environment gives new opportunities and new approaches in analytical processes. There are numerous ways how to set up early warning systems within a company. The Big Data environment forces companies to apply new ways of thinking and use new disposable data sources. This article gives a novel concept for an early warning system design within a company, which is applicable in different industries. The core of the proposed framework is a hybrid fuzzy expert system which can contain a variety of data mining predictive models responsible for some specific areas as addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within the rule blocks. As a part of this framework, SNA methods are also explained and introduced as powerful and unique tool to be used in modern early warning systems.
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