Special Meeting: Workshop on Algorithms for Modern, Massive Datasets

这次特别会议将汇集来自计算统计和数值线性代数社区的人们与来自理论计算机科学社区的人们，以促进思想的交流。这将有望导致对现代海量数据集的更好理解，以及对其建模和分析的新技术和算法的发明。大多数关于信息检索和数据挖掘的会议和研讨会主要是对传统数据集和传统算法技术的研究。然而，现代数据集已经变得极其复杂，在许多情况下，传统的处理方法已经失效。困难主要发生在两个方面：现代数据集通常是高维和大量的，因此必须采用新颖的降维或基于采样的技术来处理它们，现代数据集通常具有复杂的非线性结构，必须用适当的数学对象（如张量，对称空间，李群等）进行建模。这次特别会议将研究具有如此复杂结构的数据集，并探索分析这些数据的新技术。会议将讨论的技术在许多实际和重要的领域具有适用性。例如：(1)生物信息学和医学科学的应用包括改进的方法来分析大型微阵列和高通量化学数据集，用于开发新的药物，鉴定新的基因产物，阐明蛋白质折叠途径，以及改进的癌症检测和分类。(2)在社会科学领域的应用包括改进了大型社会网络组合结构的建模方法；这种方法已经并将继续用于识别隐藏在通信网络中的潜在恐怖分子。(3)地球科学的应用包括空中和航天器上的遥感器，它们产生的数据比目前存储的要多，但必须对这些数据进行分析，以用于环境规划、天气预报和公共卫生污染问题。(4)计算机视觉的应用包括识别目标和人脸的噪声图像的新方法，以改进目标识别和更现实环境中的个体识别。

This special meeting will bring together people from the ComputationalStatistics and Numerical Linear Algebra communities with people from theTheoretical Computer Science community to promote cross-fertilization ofideas. This will hopefully lead to a better understanding of modernmassive data sets as well as invention of new techniques and algorithmsfor their modeling and analysis. Most conferences and workshops oninformation retrieval and data mining are dominated by studies oftraditional data sets and traditional algorithmic techniques.Nevertheless, modern data sets have become extremely complex and haverendered traditional approaches for processing them ineffective in manyinstances. The difficulties occur primarily in two ways: modern data setsare often high-dimensional and massive, and thus novel dimensionalityreduction or sampling-based techniques have to be employed in processingthem, and modern data sets often have complicated nonlinear structures,and must be modeled with appropriate mathematical objects such as tensors,symmetric spaces, Lie groups, etc. This special meeting will study datasets with such complex structures and explore novel techniques foranalyzing such data.The techniques that will be discussed at the meeting have applicability innumerous fields of practical and important interest. For example:(1) Applications in Bioinfomatics and the Medical Sciences includeimproved methods to analyze large microarray and high-throughput chemicaldata sets for developing new medications, identifying novel gene products,elucidating protein folding pathways, and the improved detection andclassification of cancer.(2) Applications in the Social Sciences include improved methods to modelthe combinatorial structure of large social networks; this has been usedand will continue to be used to, e.g., identify potential terrorist cellshidden in a communications network.(3) Applications in the Geosciences include remote sensors in air-andspacecraft that produce more data than can currently be stored, but whichmust be analyzed for environmental planning, weather forecasting, andpublic health contamination issues.(4) Applications in Computer Vision include novel ways to identify noisyimages of targets and faces for improved target identification andindividual identification in more realistic settings.