CISE Postdoctoral Rsch Associates in Experimental Computer Science: Incremental Nearest Neighbor Algorithms for Similarity Retrivai in Multimedia Databases and Medical Applications

9901636Samet，哈南马里兰大学CISE 实验计算机科学博士后：多媒体数据库和医疗应用中相似性检索的增量最近邻算法相似性搜索是多媒体数据库和涉及复杂对象的其他数据库应用程序中非常重要的操作。 两个对象之间的相似性用距离度量来表示。 解决了处理相似性搜索的两种现有方法。 第一种是基于映射到低维几何空间（利用 R 树等数据结构），而第二种是根据距离直接对对象进行索引（利用 M 树等数据结构）。 对于这两种方法，正在研究用于最近邻搜索的新增量算法的开发。 这些算法将具有按照相似性顺序逐一报告其结果的特性，并且花费尽可能少的精力来生成每个新的结果对象。 在预先未知所需邻居数量的情况下，增量方面提供了显着的好处。 正在研究这些算法在多媒体数据库、医学应用等领域问题的应用。

9901636Samet, HananUniversity of MarylandCISE Postdoctoral Associates in Experimental Computer Science: IncrementalNearest Neighbor Algorithms for Similarity Retrieval in MultimediaDatabases and Medical ApplicationsSimilarity search is a very important operation in multimedia databases and other database applications involving complex objects. The similarity between two objects is represented with a distance metric. Two existing approaches for handling similarity search are addressed. The first is based on mapping to low-dimensional geometric space (making use of data structures such as the R-tree), while the second directly indexes the objects based on distances (making use of data structures such as the M-tree). For both approaches, the development of new incremental algorithms for nearest neighbor search are being investigated. The algorithms will have the property of reporting their result one-by-one in order ofsimilarity, with as little effort as possible expended to produce each new result object. The incremental aspect provides significant benefits in situations when the number of desired neighbors is unknown in advance. The application of the algorithms to problems in fields such as multimedia databases, medical application, as well as others, are being investigated.