Global vs. localized search: A comparison of database selection methods in a hierarchical environment

In this work, we compare standard global IR searching with more localized techniques to address the database selection problem. We conduct a series of experiments to compare the retrieval effectiveness of three separate search modes using a hierarchically structured data environment of textual databse representations. The data environment is represented as a tree-like structure containing over 15,000 unique databases and approximately 100,000 total leaf nodes. The search modes consist of varying degrees of browse and search, from a global search at the root node to a refined search at a sub-node using dynamically-calculated inverse document frequencies (idfs) to score the candidate databases for probable relevance. Our findings indicate that a browse plus search approach that relies upon localized searching from sub-nodes in this environment produces the most effective results.