{'fraction_non_alphanumeric': 0.01970954356846473, 'fraction_numerical': 0.0, 'mean_word_length': 5.52027027027027, '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}}
Distributed learning of human mobility patterns from cellular network data

The advent of ubiquitous mobile devices has provided us with an abundant spatio-temporal data source that helps us understand human mobility. The big data generated from mobile devices can be distributed at different locations and it is always infeasible to aggregate the data from multiple data collection centers into one location due to communication and privacy considerations. This paper studies human mobility patterns by learning data-adaptive representations for cellular network data that are distributed across a set of interconnected nodes. It proposes a distributed algorithm, termed cloud NN-K-SVD, for collaboratively learning a sparsifying dictionary (i.e., overcomplete basis) from the data without exchanging data samples between different nodes. The effectiveness of cloud NN-K-SVD is demonstrated through experiments on anonymized Call Detail Records from Columbus, OH.