SGER: Stochastic Methods for Information Retrieval Systems

这项研究正在探索使用随机过程来开发新的技术，这些技术可以作为信息检索系统、模式匹配系统以及一般需要揭示索引但以其他方式无组织的信息集合中的隐藏联系的任何应用的理论和计算基础。该方法的基础是构建底层信息的马尔可夫模型，并利用平均首次通过时间作为非对称空间度量来衡量信息中的邻接程度。正在研究以下主题：-建立平均首次通过时间揭示不同类型和不同大小的不同信息体中隐藏连通性的理论程度。-开发和实施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方法未能识别的隐藏连接，并评估增加的计算工作量与简单的PageRank计算之间的内在权衡。-鉴于平均首次通过时间可以被证明在理论上和计算上是可行的，将研究与更新底层信息相关的问题。对信息的添加、删除或更改几乎总是创建、销毁或更改连接(直接的和潜在的)，在实时(或接近)实时运行的大型系统中处理这些影响是需要克服的重大障碍。目前正在评估比当前方法更好的平稳概率和平均首次通过时间的有效更新技术。

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.