A Smart Innovation of Business Intelligence Based Analytical Model by Using POS Based Deep Learning Model

Sharath Kumar Jagannathan, Gulhan Bizel, Thomas Abraham J V, G. Kannan, Veeramalai Sankaradass, Durgaprasad Navulla
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Abstract

In analytics and business intelligence, there are a lot of things that can go wrong. Be it a report or a plan in the data based results must be consistent, verifiable, accurate to emerge and most importantly, acceptable to the end user. Contrary to the popular belief that the coexistence of multiple BI tools poses some major impediment to the advancement of high-quality analytics output, allowing multiple tools at the same time has several serious advantages in many ways. These are all examples of BI software and are more intelligent than relatively sophisticated tools. In this paper innovative business intelligence based analytics model was proposed to enhance the point of sale in commercial retail market. The practice of continuous integration, invented by software developers and borrowed by the analytics and business intelligence community, is an attempt to detect mistakes or errors early. The Point of sale systems occupies every retail space. Any company that has employees has some software that manages payroll, Sales reports are almost universal.
基于POS的深度学习模型对商业智能分析模型的智能创新
在分析和商业智能中,有很多事情可能出错。无论是报告还是计划,基于数据的结果必须是一致的、可验证的、准确的,最重要的是,最终用户可以接受。人们普遍认为,多种BI工具的共存对高质量分析输出的进步构成了一些主要障碍,与此相反,允许多种工具同时使用在许多方面都有一些重要的优势。这些都是BI软件的例子,比相对复杂的工具更智能。本文提出了一种基于商业智能的创新分析模型,以提升商业零售市场的销售点。持续集成的实践是由软件开发人员发明的,并被分析和商业智能社区所借鉴,它是一种早期检测错误或错误的尝试。销售点系统占据了每一个零售空间。任何有员工的公司都有一些管理工资的软件,销售报告几乎是通用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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