PIGA: Partitioned Inverted Index Using Genetic Algorithm

The dramatic increase in the amount of content available in digital forms gives rise to large-scale information systems, targeted to support millions of users and terabytes of data. Retrieving information from a system of this scale in an efficient manner is a challenging task due to the size of the collection as well as the index. In this paper, we propose partitioned inverted index using genetic algorithm (PIGA) that determines a near-optimal partitioning of an inverted index across nodes in a system to support searching of information in a large-scale information system, implemented atop a network of workstations. Simulation experiments on 512 Gigabytes of text show that this organization outperforms previously proposed techniques over a wide range of conditions