The generation of inverted indexes is one of the most computationally intensive activities for information retrieval systems: indexing large multi‐gigabyte text databases can take many hours or even days to complete. We examine the generation of partitioned inverted files in order to speed up the process of indexing. Two types of index partitions are investigated: TermId and DocId.
We use standard measures used in parallel computing such as speedup and efficiency to examine the computing results and also the space costs of our trial indexing experiments.
The results from runs on both partitioning methods are compared and contrasted, concluding that DocId is the more efficient method.
The practical implications are that the DocId partitioning method would in most circumstances be used for distributing inverted file data in a parallel computer, particularly if indexing speed is the primary consideration.
The paper is of value to database administrators who manage large‐scale text collections, and who need to use parallel computing to implement their text retrieval services.
