A Relative Study on Analytical Models

Vikas Attri, Isha Batra, A. Malik
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引用次数: 1

Abstract

The growth of Ecommerce and Online Social Network (OSN) sites has seen remarkable growth and people of all ages from diverse cultural background are using it for sharing knowledge. Even companies used many digital marketing techniques such as SEO, SEM, SMO and SMM to target the sales marketing funnel. Today customers have the choice because of the advocacy feature of sales funnel to put reviews on product. So Analytics modeling is essential for social big data comprehension, prediction generation and business decision making. Without models, it's almost impossible to gain insights from the data. Analytics modeling having primary objective is to use data mining and machine learning techniques to break down various kinds of information created inside these mind boggling networks, endeavoring to deliver usable information to assist companies how improve business performance. Business Analytics is a procedure by which significant data trends are identified, analyzed, communicated and resources are used to allow the whole organization to ask some question about any data in any environment on any system. This paper addresses current prescriptive and predictive analytics framework and key approaches for its execution, examines analytical techniques, synthesizes research articles to recognize current investigation issues, and describes future study directions.
分析模型的相关研究
电子商务和在线社交网络(OSN)网站的增长令人瞩目,来自不同文化背景的各个年龄段的人们都在使用它来分享知识。甚至公司使用许多数字营销技术,如SEO, SEM, SMO和SMM来定位销售营销渠道。今天,由于销售渠道的宣传功能,客户可以选择对产品进行评论。因此,分析建模对于社交大数据理解、预测生成和业务决策至关重要。没有模型,几乎不可能从数据中获得洞察力。分析建模的主要目标是使用数据挖掘和机器学习技术来分解这些令人难以置信的网络中产生的各种信息,努力提供可用的信息来帮助公司如何提高业务绩效。业务分析是一个过程,通过它可以识别、分析、交流重要的数据趋势,并使用资源,使整个组织能够对任何环境、任何系统中的任何数据提出一些问题。本文阐述了当前的规范性和预测性分析框架及其执行的关键方法,检查了分析技术,综合了研究文章以识别当前的调查问题,并描述了未来的研究方向。
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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