A statistics-based approach to incrementally update inverted files

Many information retrieval systems use the inverted file as indexing structure. The inverted file, however, requires inefficient reorganization when new documents are to be added to an existing collection. Most studies suggest dealing with this problem by sparing free space in an inverted file for incremental updates. In this paper, we propose a run-time statistics-based approach to allocate the spare space. This approach estimates the space requirements in an inverted file using only a little most recent statistical data on space usage and document update request rate. For best indexing speed and space efficiency, the amount of the spare space to be allocated is determined by adaptively balancing the trade-offs between reorganization reduction and space utilization. Experiment results show that the proposed space-sparing approach significantly avoids reorganization in updating an inverted file, and in the meantime, unused free space can be well controlled such that the file access speed is not affected.