Predictive business process monitoring (PBPM) is particularly valuable in dynamic business environments, and it can help organisations mitigate risks and optimise resource allocation. An interesting task in PBPM is next activity prediction (NAP), which allows the prediction of future activities that will be executed at a certain time based on ongoing business processes. Existing methods typically only utilise the order information of traces when predicting the next activity, without fully leveraging the attribute information present in the logs. Given the usefulness of these for NAP, combining them can help neural networks gain a deeper understanding of the actual business process. In this study, we propose a dual-view deep learning approach to fully extract and fuse the aforementioned two aspects of information. First, we treated traces as sequential texts and extracted the trace order information based on a long short-term memory based self-attention network. Then, we treated traces as unstructured images and captured the implicit attribute fusion information among events using a 12-layer residual network. Finally, two parts of information were fused for NAP. Experiments on 12 real-life event logs prove that the proposed approach is superior to state-of-the-art approaches, exhibiting good performance in accuracy, macro-precision, macro-recall, macro-F1-score, and macro-Gmean.

We report downhole microseismic monitoring field test results of a fiber-optic-based seismic sensor (FOSS) array in a multistage hydraulic fracturing stimulation and present, for the first time, its systematic comparison with the conventional moving-coil geophone array deployed on-site. Perforation shots’ analysis demonstrates that the FOSS has ~7.5 dB higher narrowband signal-to-noise ratio and, thus, 2.3 times smaller azimuth calibration error than the commercial moving-coil geophone. These benefits are mainly attributed to the intrinsic immunity to electromagnetic interference and a higher frequency resonance of FOSS. Field test results show that the FOSS can identify P- and S-waves of microseismic events, the temporal and spatial distributions of which are consistent with the geophone. In addition, the FOSS is found preferable to detect microseismic events with higher frequencies from some hundreds of Hz-to-kHz range. This superior ability contributes to distinct signatures in the identified P- and S-waves, including a shorter event duration and more concentrated frequency band, comparing to those collected by the geophone. The fracture interpretation results validate the application of fiber-optic seismic sensors on downhole microseismic monitoring.

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.