MSPP: A Highly Efficient and Scalable Algorithm for Mining Similar Pairs of Points

MOMENTI：多维时间序列中可扩展的模式挖掘

MOMENTI: Scalable Motif Mining in Multidimensional Time Series

本文提出了一种可扩展的算法，用于在多维时间序列中挖掘模式，提供概率保证的质量结果，算法时间复杂度低于二次，且能自适应调整参数。

PMV: Pre-partitioned Generalized Matrix-Vector Multiplication for Scalable Graph Mining

How can we analyze enormous networks including the Web and social networks which have hundreds of billions of nodes and edges? Network analyses have been conducted by various graph mining methods including shortest path computation, PageRank, connected component computation, random walk with restart, etc. These graph mining methods can be expressed as generalized matrix-vector multiplication which consists of few operations inspired by typical matrix-vector multiplication. Recently, several graph processing systems based on matrix-vector multiplication or their own primitives have been propose…

可扩展的保序模式挖掘

Scalable Order-Preserving Pattern Mining

针对时间序列保序（OP）匹配下的频繁模式挖掘（FPM）问题，提出基于保序后缀树（OPST）的高效构建算法及精确挖掘算法，实验证明其比现有方法快数个数量级且聚类有效。

Scalable Boolean Tensor Factorizations using Random Walks

Tensors are becoming increasingly common in data mining, and consequently, tensor factorizations are becoming more and more important tools for data miners. When the data is binary, it is natural to ask if we can factorize it into binary factors while simultaneously making sure that the reconstructed tensor is still binary. Such factorizations, called Boolean tensor factorizations, can provide improved interpretability and find Boolean structure that is hard to express using normal factorizations. Unfortunately the algorithms for computing Boolean tensor factorizations do not usually scale wel…

Scalable Construction of Text Indexes

The suffix array is the key to efficient solutions for myriads of string processing problems in different applications domains, like data compression, data mining, or Bioinformatics. With the rapid growth of available data, suffix array construction algorithms had to be adapted to advanced computational models such as external memory and distributed computing. In this article, we present five suffix array construction algorithms utilizing the new algorithmic big data batch processing framework Thrill, which allows us to process input sizes in orders of magnitude that have not been considered b…

An Improved and Parallel Version of a Scalable Algorithm for Analyzing Time Series Data

Today, very large amounts of data are produced and stored in all branches of society including science. Mining these data meaningfully has become a considerable challenge and is of the broadest possible interest. The size, both in numbers of observations and dimensionality thereof, requires data mining algorithms to possess time complexities with both variables that are linear or nearly linear. One such algorithm, see Comput. Phys. Commun. 184, 2446-2453 (2013), arranges observations into a sequence called the progress index. The progress index steps through distinct regions of high sampling d…