{'fraction_non_alphanumeric': 0.02186421173762946, 'fraction_numerical': 0.010356731875719217, 'mean_word_length': 4.76158940397351, '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}}
Enron Data Revisited - Neighborhood Queries with FastBit Win over Popular Commercial Database System

In our previous work we analyzed the Enron data set and showed how FastBit [1] can be used for speeding up multi-dimensional queries [3]. We demonstrated that our approach is between a factor of 100 to 1000 faster than one of the most commonly used open-source database systems called MySQL. In this article we evaluate the performance of neighborhood queries (nested queries) that are important for studying communication patterns and the flow of information. Consider, for instance, that one might want to know all the recipients of emails that were sent by person P . However, some people never communicate directly with certain people and rely on either messengers or the fact that emails get forwarded to them. In SQL, this kind of query is expressed as follows: