Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Duen Horng Chau, Jilles Vreeken, M. Leeuwen, C. Faloutsos
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引用次数: 4

Abstract

We have entered the era of big data. Massive datasets, surpassing terabytes and petabytes in size are now commonplace. They arise in numerous settings in science, government, and enterprises, and technology exists by which we can collect and store such massive amounts of information. Yet, making sense of these data remains a fundamental challenge. We lack the means to exploratively analyze databases of this scale. Currently, few technologies allow us to freely "wander" around the data, and make discoveries by following our intuition, or serendipity. While standard data mining aims at finding highly interesting results, it is typically computationally demanding and time consuming, thus may not be well-suited for interactive exploration of large datasets. Interactive data mining techniques that aptly integrate human intuition, by means of visualization and intuitive human-computer interaction techniques, and machine computation support have been shown to help people gain significant insights into a wide range of problems. However, as datasets are being generated in larger volumes, higher velocity, and greater variety, creating effective interactive data mining techniques becomes a much harder task.
ACM SIGKDD交互式数据探索和分析研讨会论文集
我们已经进入了大数据时代。超过太字节和拍字节的大规模数据集现在很常见。它们出现在科学、政府和企业的许多环境中,而且我们可以通过现有技术收集和存储如此大量的信息。然而,理解这些数据仍然是一个根本性的挑战。我们缺乏探索性分析这种规模的数据库的手段。目前,很少有技术允许我们自由地在数据中“漫游”,并根据我们的直觉或意外发现来发现。虽然标准数据挖掘的目的是寻找非常有趣的结果,但它通常需要大量的计算和时间,因此可能不太适合对大型数据集进行交互式探索。交互式数据挖掘技术,通过可视化和直观的人机交互技术,以及机器计算支持,适当地集成了人类的直觉,已经被证明可以帮助人们对广泛的问题获得重要的见解。然而,随着数据集以更大的容量、更快的速度和更多的种类生成,创建有效的交互式数据挖掘技术变得更加困难。
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