(Re)designing Clustering Algorithms for Big Data

我们当前的数据生成速度与存储容量的增加相结合，产生了新的计算范式。例如，根据IBM的网站，Facebook上每分钟大约有69.5万条状态更新和1100万条即时消息被发送。然而，许多组织都面临着拥有大量数据，但对它们知之甚少的问题。聚类方法有助于自动识别一组数据对象中未被观察到的组，并且目前正在被可用数据的大小、种类和性质所彻底改变，即所谓的“大数据”。本研究项目的重点是开发用于大数据集群的可扩展算法。这将通过以下两种方式实现：(i)重新设计众所周知的成功串行算法的可扩展性，利用其主要理论思想；（ii）使用与大数据相关的新编程范例开发新的算法和启发式方法。

Our current speed of data generation combined with storage capacity increases have given rise to new paradigms in computing. For example, according to the IBM's website, there are approximately 695,000 status updates and 11 million instant messages sent every minute on Facebook. However, many organizations have faced the problem of having a lot of data, but poor knowledge about them. Clustering methods help to automatically identify unobserved groups for a set of data objects, and are currently being radically transformed by the size, the variety and the nature of the available data, i.e., by so-called “Big Data”. This research program focuses on the development of scalable algorithms for Big data clustering. This will be achieved both by: (i) redesigning well-known successful serial algorithms for scalability, leveraging their main theoretical ideas; and (ii) developing new algorithms and heuristics using new programming paradigms associated with Big Data.