{'fraction_non_alphanumeric': 0.022653721682847898, 'fraction_numerical': 0.0, 'mean_word_length': 5.312925170068027, '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}}
Analysis for instances' staying-time distribution in the workflow

Performance analysis can help us reduce requests' congestions in a workflow network, cut servers' cost or strike a best balance between them. Analysis for requests' remaining time at a workflow network plays an important role in the per-formance analysis for time-constrained workflow. Because requests' remaining time has complex relations with the arrival rate of users' service requests, the service rate of each server for each activity, servers' number and the structure of the workflow network, it is difficult to give a quantificational analysis for requests' remaining time. We develop a method to determine probability density function of requests' remaining time at a workflow network so that we will know the accurate proportion of requests can be executed without delay. An experiment illustrates our method can be effectively utilized in practice.