期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2013
卷号：36
期号：3
出版社：IEEE Computer Society
摘要：Tensor decompositions are increasingly gaining popularity in data science applications. Albeit ex-tremely powerful tools, scalability to truly large datasets for such decomposition algorithms is stilla challenging problem. In this paper, we provide an overview of recent algorithmic developmentstowards the direction of scaling tensor decompositions to big data. We present an exact Map/Reducebased algorithm, as well as an approximate, fully parallelizable algorithm that is sparsity promoting.In both cases, careful design and implementation is key, so that we achieve scalability and efficiency.We showcase the effectiveness of our methods, by providing a variety of real world applications -whose volume previously rendered their analysis very hard, if not impossible- where our algorithmswere able to discover interesting patterns and anomalies
