While process mining has gained much attention in the past decade, surprisingly, discovering structural errors (i.e., deadlock and lack of synchronization) from event logs has seldom been studied. Since event logs may involve erroneous event occurrences caused by unsynchronized activities, discovering deadlocks and lack of synchronization errors may influence each other. To this end, we first extract from the original event log two independent event logs which are employed to discover deadlocks and lack of synchronization errors, respectively. We then discard the erroneous event occurrences in the two event logs, from which our event relation based mining rules can discover the corresponding structural errors. We have implemented our approach, and the experimental results corroborate that our approach can effectively and efficiently discover process structural errors from the event logs involving sufficient event sequences.

A new modeling idea of interval type-2 fuzzy logic system (IT2 FLS) is proposed on the basis of matrix semitensor product (STP) fuzzy representation technique, to construct an interval type-2 fuzzy relationship matrix (IT2 FRM) model. The model naturally inherits the advantages of STP theory, which enables the fuzzy logic reasoning process to be translated into algebraic form for computation. In most cases, the fuzzy rules in the IT2 FRM are generally unknown. To address the above situation, the parameters of this model can be trained with observed data pairs. First, the fuzzy rules are generated by the space division method, and the initial parameters of the model are determined. Second, the parameters in the model are trained using the steepest descent algorithm. Finally, the IT2 FRM model is validated by simulation, and the results demonstrate that the IT2 FLS can be designed using the STP method.

Calibrating the top-of-atmospheric reflectance measurements of spaceborne hyperspectral atmospheric composition monitoring instruments is necessary to ensure accuracy during cloud and aerosol retrieval. Here, we present a method for calibrating full-pixel reflectance data collected by the Environmental Trace Gases Monitoring Instrument (EMI). This method compares EMI reflectance data with simulation using a radiative transfer model (RTM). To simplify the radiative transfer calculations, a clear observation situation without clouds or aerosols was chosen. Correction coefficients for each spatial dimensional pixel were obtained by comparing the EMI Earth reflectance with the simulated reflectance, under clear sky conditions. The corrected reflectance data were cross-compared with TROPOMI reflectance data, highlighting that there were relative errors of >20% at 345, 388, 425, and 440 nm before correction. After correction, however, the errors of all selected wavelength bands were <5%, demonstrating that the developed method can help in calibrating EMI in-flight reflectance measurements.

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences.