SGER: Stochastic Methods for Information Retrieval Systems

SGER：信息检索系统的随机方法

• 批准号: 0318575
• 负责人: Carl Meyer
• 金额: $ 10万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0318575&HistoricalAwards=false
• 关键词: SGER; Stochastic; Methods; Information; Retrieval

This research is exploring the use of stochastic processes to develop new techniques that can serve as a theoretical and computational basis for information retrieval systems, pattern matching systems, and generally any application that requires revealing hidden connections in an indexed but otherwise unorganized collection of information. The methodology is predicated on the idea of constructing a Markovian model of the underlying information and utilizing mean first passage times as an asymmetric aspatial metric to gauge degrees of contiguity in the information. The following topics are being studied:- Establishing the theoretical extent to which mean first passage times reveal hidden connectivity in different bodies of information of varying type and varying size. - Developing and implementing fast algorithms for computing mean first passage times. This includes determining the computational feasibility of using the mean first passage time metric on different kinds of large-scale data sets.- Use of a Markov model to capture hidden connections that the Google PageRank approach fails to identify, and assess the inherent tradeoffs between increased computational effort over simple PageRank computations.- Given that mean first passage times can be demonstrated to be theoretically and computationally feasible, the problems associated updating and downdating the underlying information will be investigated. Additions, deletions, or changes to information almost always create, destroy, or change connections (direct as well as latent), and dealing with these effects in large-scale systems running in (or near) real time is a significant hurdle to overcome. Efficient updating techniques for stationary probabilities as well as mean first passage times that are superior to current methods are being evaluated.

这项研究正在探索使用随机过程开发新技术，可以作为信息检索系统，模式匹配系统的理论和计算基础，以及通常需要揭示索引但无组织信息集合中隐藏连接的任何应用程序。该方法是基于这样的想法，构建一个马尔可夫模型的基本信息和利用平均首次通过时间作为一个不对称的空间度量，以衡量程度的连续性的信息。正在研究以下主题：-建立理论上的程度，平均首次通过时间揭示隐藏的连接在不同类型和不同大小的信息的不同机构。 - 开发和实现计算平均首次通过时间的快速算法。这包括确定在不同类型的大规模数据集上使用平均首次通过时间度量的计算可行性。-使用马尔可夫模型来捕获Google PageRank方法无法识别的隐藏连接，并评估增加的计算工作量与简单PageRank计算之间的固有权衡。鉴于平均首次通过时间可以证明是理论上和计算上可行的，相关的问题更新和更新的基础信息将进行调查。对信息的添加、删除或更改几乎总是会创建、破坏或改变连接（直接的和潜在的），在真实的时间内（或接近真实的时间）运行的大规模系统中处理这些影响是一个需要克服的重大障碍。目前正在评估有效的更新技术的平稳概率以及平均首次通过时间是上级到目前的方法。