Analyzing Cilck Stream Data Using Hadoop

2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2016-08-18 DOI:10.1109/CICT.2016.28

Pulkit Sharma, Komal Mahajan, Vishal Bhatnagar

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引用次数: 2

Abstract

Big data is a voluminous and complex collection of data that is difficult to process using the day to day database management techniques and traditional data processing tools. Analysis of such data by organizations can discover previously unknown trends and opportunities which can be used for optimization of services and even help executives in decision making. One such source of data is clickstream data which can be captured and analyzed by online retailers and similar conglomerates to optimize the websites and increase sales. In today's world, online retail has become a huge industry which boasts of a huge number of retailers, over a billion customers and worldwide sales of over 22 trillion U. S. D., a number which is set to increase in coming years. This brings with it the need to provide a great browsing experience to customers to keep afloat in the huge market of online retailers. Hadoop is the easy to use framework that helps the user in processing of large data sets over a cluster of commodity computers using simple programming techniques.

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使用Hadoop分析点击流数据

大数据是一个庞大而复杂的数据集合，使用日常的数据库管理技术和传统的数据处理工具很难处理。组织对这些数据的分析可以发现以前未知的趋势和机会，这些趋势和机会可以用于优化服务，甚至可以帮助管理人员做出决策。其中一种数据来源是点击流数据，可以被在线零售商和类似的企业集团捕获和分析，以优化网站并增加销售额。在当今世界，在线零售已经成为一个庞大的行业，拥有大量的零售商，超过10亿的客户和超过22万亿美元的全球销售额，这一数字在未来几年将会增加。这就需要为顾客提供良好的浏览体验，以便在庞大的在线零售商市场中保持竞争力。Hadoop是一个易于使用的框架，它可以帮助用户使用简单的编程技术在一组普通计算机上处理大型数据集。

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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2016 Second International Conference on Computational Intelligence & Communication Technology (CICT)

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