Fast Incremental Indexing for Full-Text Information Retrieval

Full-text information retrieval systems have traditionally been designed for archival environments. They often provide little or no support for adding new documents to an existing document collection, requiring instead that the entire collection be re-indexed. Modern applications, such as information filtering, operate in dynamic environments that require frequent additions to document collections. We provide this ability using a traditional inverted file index built on top of a persistent object store. The data management facilities of the persistent object store are used to produce efficient incremental update of the inverted lists. We describe our system and present experimental results showing superior incremental indexing and competitive query processing performance.