{'fraction_non_alphanumeric': 0.019809825673534072, 'fraction_numerical': 0.0, 'mean_word_length': 5.682539682539683, '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}}
Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record

With the impressive development of wireless devices and growth of mobile users, telecommunication operators are thirsty for understanding the characteristics of mobile network behavior. Based on the big data generated in the telecommunication networks, telecommunication operators are able to obtain substantial insights using big data analysis and computing techniques. This paper introduces the important aspects in this topic, including data set information, data analysis techniques, and two case studies. We categorize the data set in the telecommunication networks into two types, user-oriented and network-oriented, and discuss the potential application. Then, several important data analysis techniques are summarized and reviewed, from temporal and spatial analysis to data mining and statistical test. Finally, we present two case studies, using Erlang measurement and call detail record, respectively, to understand the base station behavior. Interestingly, the night burst phenomenon of college students is revealed by comparing the base stations location and real-world map, and we conclude that it is not proper to model the voice call arrivals as Poisson process.