Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010

TL;DR

The paper summarizes the 2010 MMDS workshop focused on developing novel algorithms and techniques for analyzing large-scale scientific and Internet data, emphasizing interdisciplinary collaboration and recent advancements in the field.

Contribution

It highlights the workshop's goals of fostering innovative methods for high-dimensional data analysis and promoting cross-disciplinary ideas in large-scale data applications.

Findings

Exploration of new modeling techniques for massive data sets

Promotion of interdisciplinary collaboration among researchers

Review of recent algorithmic and statistical challenges

Abstract

The 2010 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2010) was held at Stanford University, June 15--18. The goals of MMDS 2010 were (1) to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and Internet data sets; and (2) to bring together computer scientists, statisticians, applied mathematicians, and data analysis practitioners to promote cross-fertilization of ideas. MMDS 2010 followed on the heels of two previous MMDS workshops. The first, MMDS 2006, addressed the complementary perspectives brought by the numerical linear algebra and theoretical computer science communities to matrix algorithms in modern informatics applications; and the second, MMDS 2008, explored more generally fundamental algorithmic and statistical challenges in modern large-scale data analysis.