{'fraction_non_alphanumeric': 0.014405762304921969, 'fraction_numerical': 0.003601440576230492, 'mean_word_length': 5.2706766917293235, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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