Rise of Big Data – Issues and Challenges

Bayan Alabdullah, N. Beloff, Martin White
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引用次数: 9

Abstract

The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data.
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大数据的兴起——问题与挑战
最近,由于社交媒体平台和移动设备等基于互联网的技术,大数据的可用性迅速增加,这使得许多市场领导者对处理非常大、随机和高速的数据毫无准备。传统上,技术最初是在实验室开发和测试的,然后通过新闻发布和广告等媒体向公众展示。这些技术随后被大众所采用。就大数据技术而言,大数据的快速发展和用户群体对大数据的欣然接受,使得学术界几乎没有时间对其进行审视。尽管专业人士和作者发表了许多关于大数据的书籍和电子媒体文章,但学术文献中仍然缺乏基础工作。本文通过调研的方法,探讨了大数据在数据源、内容格式、数据分期、数据处理、流行数据存储等方面面临的挑战。讨论了与大数据相关的问题和挑战,特别是隐私攻击和反技术,如k-匿名、t-接近、l-多样性和差分隐私。各种组织存储不同类型大数据所采用的工具和技术也得到了强调。本研究确定了需要解决的不同研究领域,例如缺乏非结构化大数据的匿名化技术,开发可扩展数据存储解决方案的数据流量模式确定以及高速数据的控制机制。
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