Special Meeting: Workshop on Algorithms for Modern, Massive Datasets

这次特别会议将把计算统计和数值线性代数社区的人们与理论计算机科学界的人们聚集在一起，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，以及为其建模和分析而发明的新技术和算法。大多数关于信息检索和数据挖掘的会议和研讨会都是由对传统数据集和传统算法技术的研究主导的。然而，现代数据集已经变得极其复杂，并且在许多情况下传统方法处理它们是无效的。困难主要出现在两个方面：现代数据集通常是高维和海量的，因此必须采用新的降维或基于采样的技术来处理它们，并且现代数据集往往具有复杂的非线性结构，必须使用适当的数学对象如张量、对称空间、李群等来建模。这次特别会议将研究具有如此复杂结构的数据集，并探索分析此类数据的新技术。会议将讨论的技术在许多具有实用和重要兴趣的领域具有适用性。例如：(1)在生物信息学和医学科学中的应用包括分析大型微阵列和高通量化学数据集的改进方法，以开发新的药物，识别新的基因产品，阐明蛋白质折叠途径，以及改进癌症的检测和分类。(2)在社会科学中的应用包括改进的方法来模拟大型社会网络的组合结构；它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细胞。(3)地球科学中的应用包括空气和航天器中的远程传感器，这些传感器产生的数据比目前可以存储的更多，但必须为环境规划、天气预报和公共卫生污染问题进行分析。(4)计算机视觉中的应用包括识别目标和人脸的噪声图像的新方法，以改进目标识别和在更现实的环境中进行个人识别。

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.