Collaborative Research: ASES: An Approximate Search Engine for Structure

合作研究：ASES：结构近似搜索引擎

• 批准号: 9988345
• 负责人: Dennis Shasha
• 金额: $ 28.24万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9988345&HistoricalAwards=false
• 关键词: Collaborative; Research; ASES; Approximate; Search

The goal of this research project is to make it possible to process approximate queries on combinatorial structures such as trees and graphs in a fast (i.e., sublinear with respect to the number of objects being searched) and efficient manner. The ultimate goal is to be as fast as approximate keyword search in text documents. The approach consists of: (1) discovering the best heuristics for answering these NP-complete problems in polynomial time with high quality, and (2) discovering data structures to make queries on thousands or millions of such objects fast. The results of this project will provide techniques and software to search for patterns among graph and tree structured data. Possible applications of such a search engine include searches among proteins, chemical compounds, neuroanatomical structures, Web/text filters and XML documents. The techniques developed in this research are in particular suitable for bioinformatics and biocomputing applications. Professors Shasha and Wang plan to collaborate with five additional researchers in these areas: Jack Collins, National Cancer Institute working in small molecule-protein docking for drug design; Michael Donohue, Professor of Biology and Director of the Harvard University Herbaria planning to apply this research to phylogenic trees; Bruce Shapiro, National Cancer Institute developing algorithms and computational systems for determining structure/function of nucleic acids; Cathy Wu, National Biomedical Research Foundation doing research in analysis and classification of protein sequences; and Daniel Zaharevitz, National Cancer Institute whose goal is to make biological and structural data more available to the research community via better search capabilities. These collaborators help to motivate and validate the tree and graph matching tools and algorithms development for biological applications.http://www.cis.njit.edu/~jason/sigmod.html

该研究项目的目标是使在快速（即，相对于被搜索的对象的数量是次线性的）和有效的方式。最终目标是在文本文档中像近似关键字搜索一样快。该方法包括：（1）发现在多项式时间内高质量地回答这些NP完全问题的最佳算法，以及（2）发现数据结构以快速地对数千或数百万这样的对象进行查询。这个项目的结果将提供技术和软件来搜索图形和树结构数据中的模式。这种搜索引擎的可能应用包括在蛋白质、化合物、神经解剖结构、Web/文本过滤器和XML文档之间进行搜索。在这项研究中开发的技术特别适合于生物信息学和生物计算应用。Shasha教授和Wang教授计划在这些领域与另外五名研究人员合作：Jack柯林斯，国家癌症研究所，从事药物设计的小分子-蛋白质对接; Michael Donohue，生物学教授和哈佛大学植物标本馆主任，计划将这项研究应用于植物基因树;布鲁斯夏皮罗，国家癌症研究所开发的算法和计算系统，用于确定核酸的结构/功能; Cathy Wu，国家生物医学研究基金会，从事蛋白质序列分析和分类的研究;丹尼尔Zaharevitz，国家癌症研究所，其目标是通过更好的搜索能力使研究界更容易获得生物和结构数据。这些合作者帮助激励和验证生物学的树和图匹配工具和算法开发applications.http://www.cis.njit.edu/~jason/sigmod.html