CNPq: IMiMD-Indexing and Data Mining in Multimedia Databases

这是与巴西圣保罗大学的Caetano Traina教授共同努力的结果。它加强了CMU的ChristianFaloutsos教授和Traina教授及其团队之间的现有合作，该团队已经为度量和视频数据集提供了快速索引方法。CMU带来了视频索引（Informedia DL-II项目），幂律和数据挖掘方面的专业知识。合作的好处将是更快的方法来索引多媒体和度量数据集，并在这些集合中找到模式。该项目的重点是为多媒体数据编制索引，并开发新的工具，以发现这些数据中的模式和相关性。多媒体对象通常可以通过特征提取映射到n维点。如果不是，那么当我们提供成对距离函数时，它们可以被视为度量数据。该方法将适用于多媒体，度量和空间数据。典型问题包括：“找到与给定视频剪辑相似的视频剪辑”;“学校位置和图书馆位置之间的相关性（或反相关性）有多强？“;“有多少学校离图书馆5英里以内？".对于索引，目标是（a）提供公式来估计相似性查询的选择性和（B）建立更快的搜索结构。初步的联合工作表明，空间和度量数据集中的距离分布往往遵循“幂律”，这有助于设计更好的搜索策略。对于数据挖掘，目标是提供空间相关性检测工具，并为空间和多媒体数据集开发快速可视化算法。开发的工具将能够显示数据集中是否有聚类，它们有多少，以及两组点（例如“学校”和“图书馆”）是否相互“吸引”或“排斥”。

This is a joint effort with Prof. Caetano Traina from the University of Sao Paulo, Brazil. It strengthens the existing collaboration between Prof. Christos Faloutsos at CMU and Prof. Traina and his group, which has already contributed fast indexing methods for metric and video datasets. CMU brings expertise in video indexing (the Informedia DL-II project), in power laws, and in data mining. The benefit of the collaboration will be faster methods for indexing multimedia and metric datasets, and for finding patterns in such collections. This project focuses on indexing multimedia data and on developing new tools to find patterns and correlations in such data. Multimedia objects can often be mapped to n-dimensional points through feature extraction. If not, then they can be treated as metric data, when we are provided a pair-wise distance function. The methods will be applicable to multimedia, metric and spatial data alike. Typical questions include: "find video clips similar to a given video clip"; "how strong is the correlation (or anti-correlation) between the locations of schools and the locations of libraries?"; "how many schools are within 5 miles from libraries?". For indexing, the goals are (a) to provide formulas to estimate the selectivities for similarity queries and (b) to build faster searching structures. Preliminary joint work showed that the distribution of distances in spatial and metric datasets often follows a "power-law", which are useful to design better search strategies. For data mining, the goals are to provide tools for detection of spatial correlations and to develop fast visualization algorithms for spatial and multimedia datasets. The developed tools will be able to show whether there are clusters in a dataset, how many they are, and whether two groups of points (e.g. "schools" and "libraries") are "attracting" or "repelling" each other.