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With the increase of mining depth, disaster degree of rock burst in fault-controlled area is becoming more and more serious. To analyze mechanism and control method of fault rock burst, No. 3316 mining face controlled by No. 3DF56 fault in Jining mine, China, is selected as case study. First, by analysis of general situation of the mining face, mechanical characteristics of coal rock, and micro-seismic events, it is determined that the coal seam has the possibility of rock burst in the process of the face mining towards the fault. Second, based on strength theory of rock, criterion of fault shear instability is obtained, and influence of cohesion and friction angle on the stability of fault is analyzed, and it is concluded that improving the cohesion of rock can control the fault slip. Third, by borehole detection recorder, it is determined that fracture areas of fault surrounding rock distribute in the range of 0–4 m from the surface of roadway surrounding rock and 0–5.0 m from the fault. Finally, control method of "precise grouting to couple shallow and deep boreholes" is introduced, and the grouting borehole scheme is optimized. Field test results show that the method can improve the compressive strength of coal rock and distribution laws of micro-seismic events, weaken the possibility of fault slip, effectively control the deformation of roadway surrounding rock and improve the control effect of rock burst. It can provide a reference for other coal mines with similar geological conditions. [ABSTRACT FROM AUTHOR]

The consensus protocol is the core of blockchain systems. The existing hybrid committee-based consensus protocol is one of the important ways to improve the permissionless blockchain system. But the presence of malicious nodes, it can interfere with the normal operation of the committee. However, there is a deficiency in the existing mechanism to ensure the efficient and secure operation of the committee. Therefore, a reliable committee consensus protocol (DCCP) for permissionless blockchain is proposed. The protocol guarantees the operation of the committee in three ways. First, a reliability evaluation model is designed based on the performance of committee members. Second, a reliable node selection strategy is proposed by combining the mining process and node reliability value. Third, a malicious node replacement mechanism is proposed to optimize the committee operation. Finally, a delayed reward mechanism is designed in order to encourage node participation. The experiment shows that the DCCP protocol can effectively inhibit the entry of malicious nodes and can improve the throughput by 12% in the presence of malicious nodes, with high throughput and security. [ABSTRACT FROM AUTHOR]

Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper. [ABSTRACT FROM AUTHOR]

Dependency graph discovery is a substantial step in heuristic mining algorithms which are among the most prevalent process discovery methods in the healthcare domain due to their ability to deal with noisy event logs derived from unstructured and highly variable healthcare processes. However, in many healthcare applications, the current dependency graph discovery methods are still likely to create complex (spaghetti-like) and low-quality results. Hence, this paper improves the heuristic mining methods for healthcare applications by introducing a novel integer linear programming model for selecting the optimum set of dependency graph activities and arcs. The proposed method can lead to the extraction of dependency graphs that are simultaneously high-quality and non-complex. According to the assessments, when dealing with artificial and real-life healthcare event logs, the proposed method results in outputs with significantly less complexity and higher quality than the most prominent heuristic mining methods. [ABSTRACT FROM AUTHOR]

Due to the increasing use of sensors/devices in smart cities, IoT/cloud data centers must provide adequate computing resources. Efficient resource management is of the biggest challenges in distributed computing. This research proposes a solution to use the activity log of sensors to extract their activity patterns. These patterns contribute to the resource management to predict future resource requirements and act accordingly. In this framework, called sensor mining at resource management framework, the pattern extraction can be performed by the sensor mining algorithm at cloud-only data centers (SM@RMFCLOUD) or cloud/fog servers (SM@RMFFOG). Experiments apply both CityPulse and ideal datasets to evaluate the presented frameworks. The sensor mining in both cloud/fog and cloud-only frameworks improves throughput, response time, and execution delay without increasing costs, energy, and bandwidth consumption compared to the frameworks with no sensor mining. [ABSTRACT FROM AUTHOR]

Immunization based antibody discovery is plagued by the paucity of antigen-specific B cells. Identifying these cells is akin to finding needle in a haystack. Current and emerging technologies while effective, are limited in terms of capturing the antigen-specific repertoire. We report on the bulk purification of antigen-specific B-cells and the benefits it offers to various antibody discovery platforms. Using five different antigens, we show hit rates of 51–88%, compared to about 5% with conventional methods. We also show that this purification is highly efficient with loss of only about 2% antigen specific cells. Furthermore, we compared clones in which cognate chains are preserved with those from display libraries in which chains either from total B cells (TBC) or antigen-specific B cells (AgSC) underwent combinatorial pairing. We found that cognate chain paired clones and combinatorial clones from AgSC library had higher frequency of functional clones and showed greater diversity in sequence and paratope compared to clones from the TBC library. This antigen-specific B-cell selection technique exemplifies a process improvement with reduced cycle time and cost, by removing undesired clones prior to screening and increasing the chance of capturing desirable and rare functional clones in the repertoire. The authors developed a methodology for bulk enrichment of antigen-specific B cells to improve the diversity of antibody sequences and increase the number of functional antigen-specific antibodies coming from different antibody generation platforms. [ABSTRACT FROM AUTHOR]

The volume of published linked open datasets in RDF format is growing exponentially in the last decades. With this continuous proliferation of this growth, demands for managing, accessing, and compressing the RDF dataset have become increasingly important. Most approaches are focused on the structured compression technique while a very few researches have been done for compact representation of the RDF dataset. In this paper, we have proposed an efficient rule mining and compression approach for RDF datasets through various meaningful semantic association rules determined from the RDF graph. We have introduced grammar-based pattern system, clustering of rules, rules pruning, and Top-k scheme to improve the expressiveness of rule patterns, identify the similarity within the random pair of rules, extract the most delicate rules, find the accurate mining threshold, and efficiently learn the rules during the rule mining process from RDF Knowledge Base. Our proposed system uses Horn rules to achieve better compression through storing the triples matched with the precedent part while deleting the triples matched with the head part of the rules. For decreasing the mining time, we have introduced the ranking of the rules. The experimental result on the benchmark dataset asserts that our proposed rule mining and compression scheme has achieved approximately 22.10%, 40.5%, and 44% better compression than the exiting AMIE+, Rule-based compression, and TripleBit approaches, respectively. Our system also has achieved better performance both in terms of compression time and rule mining cost. [ABSTRACT FROM AUTHOR]

Characterizing the permeability evolution and methane release is of great significance for the safe mining of the high gas outburst seams, as well as coal and gas simultaneous extraction. It contributes to reduce methane emissions from coal mining for greenhouse effect control. Theoretical analysis, laboratory testing, and numerical simulation are widely used methods to characterize the permeability and methane release with the treatment process of pressure-relief mining. However, these methods cannot fully reflect the complexity of filed practice. In this study, we report the effectiveness of protective coal seam (PCS) mining and the pressure-relief area in the protected coal seam (PDCS) based on detailed and integrated field measurements in a Chinese coal mine. To the best of our knowledge, it is the first time to measure the permeability coefficient and gas pressure evolution in the PDCS during the process of PCS longwall mining. The evolution of the permeability coefficient in the pressure-relief area during PCS mining can be divided into four stages: slowly decreasing, sharply increasing, gradually decreasing, and basically stable. The maximum permeability coefficient is 322 times of the initial value and stabilized at 100 times after the goaf compacted. The gas pressure evolution in the PDCS indicates that the strike pressure relief angle is 52.2° at the active longwall face zone, and 59.3° at the installation roadway side. The inclined pressure relief angles at the lower and upper sides of the longwall face are 75° and 78.9°, respectively. The residual gas content and gas pressure of the PDCS in the pressure-relief area are reduced to less than 6 m3/t and within 0.4 MPa, respectively. The field measurements further prove that pressure-relief mining can prevent coal and gas outbursts in PDCSs. The field observations in this paper can serve as benchmark evidence for theoretical analysis and numerical simulations, and also provide insights into realizing safety mining in similar conditions. [ABSTRACT FROM AUTHOR]

Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest. [ABSTRACT FROM AUTHOR]

Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users. [ABSTRACT FROM AUTHOR]

With the advent of smart manufacturing, companies are adapting their business processes and procedures to match concepts and requirements related to this industrial revolution. In this scenario, maintenance sector should evolve to keep pace with new technological trends by shifting from traditional approaches to a smart paradigm. Some consolidated methodologies rely on standard evaluation, like Risk Priority Number, to plan maintenance actions on machines and components. However, contemporary and smart approaches should be characterized by data-driven (quantitative knowledge) as well as operator’s experience (qualitative knowledge) in order to provide a whole understanding and visibility of the system health status. In this context and with a view of digital twin concepts (under the Industry 4.0 technology), a decision support methodology (or framework) plays an essential role by providing knowledge and assisting operators in the decision-making process. With this concept in mind, this paper focuses on integrating several and not alike fields of study (process mining, multicriteria decision-making (MCDM), and data fusion) to enable the assessment of a risk and criticality analysis to rank industrial machines in order to carry out/plan maintenance tasks on them. Therefore, merging those fields of study aims at accomplishing a dynamic, flexible, and real-time evaluation to perform a robust, accurate, and responsive analysis of the system. The results from different analysis scenarios show the influence of maintenance indicators on the machine ranking process, highlighting the feasibility and flexibility of the approach, as well as expanding the application in modern systems by optimizing quality of maintenance decisions. [ABSTRACT FROM AUTHOR]

A real challenge for manufacturing industry is to be able to control not only the manufacturing process but also the production quality. Products that are suspected to be faulty are deviated from their nominal path in the production line and inspected more closely. The fact that some products deviate from the nominal path and others fail at some check operations can be used as an indicator of poor product quality. Based on this idea, this paper proposes a method to compute a product quality index or more exactly a penalty index taking into account both product path and production batches. The method relies on categorizing the products according to how they follow the production path and process mining techniques. The originality of the proposed index is to be built from advanced data analysis techniques enhanced by expert know-how. The quality index highlights risk of customer return, which is highly relevant information for the after sales service. The significance of the method is illustrated on a printed circuit board production line using surface mount technology at Vitesco Technologies. Data is collected from the real manufacturing execution system. The results obtained over more than 10,000 single electronic boards show that 91.7% of the products are in good compliance with respect to the requirements. For the other products, the method identifies the root causes of poor quality that may call for maintenance or reconfiguration actions. [ABSTRACT FROM AUTHOR]

Process mining is becoming an indispensable method in workflow model reconstructions, offering insights into mission critical systems. The efficacy of process mining depends on whether the underlying data mining algorithms can accurately classify or predict future events from process logs. However, exceptional events are scarce in most operational processes. Hence, the process logs generated from these processes are highly imbalanced. It is quite often that any model learned from imbalanced data tends to be overly generalized toward the normal classes but under-trained to recognize the rare classes. In this paper, we propose 3 methods to rectify this class imbalance problem. They are founded upon a meta-heuristic–swarm intelligence algorithm. The first method, and also the base of the remaining 2 methods, is Dynamic Multi-objective Rebalancing Algorithm, which considers both high accuracy and high confidence level of classification in its objective function, and it is draw upon the particle swarm optimization algorithm. The other two algorithms are hybrid methods by combining the first base method with over-sampling and under-sampling techniques. Experiments are conducted using the three above-mentioned methods to process rebalanced dataset, as well as using other classic resampling methods for comparison. According to the results, our proposed methods show satisfactory performance over other comparison methods, and we extracted meaningful decision rules from a rebalanced dataset in process mining. [ABSTRACT FROM AUTHOR]

In industrial automation, there are numerous use cases for a model representing the equipment's sequence of operations. Not only can such a model be used as a debugging aid, allowing users to observe the current state of the machinery at a glance, but it also can serve as a foundation for process improvements. Annotated process models can help pinpoint bottlenecks within the system, and a combination of models obtained from identical machines can be used to create a best-case baseline to which the devices can be optimised. Typically, the models used for such activities have been created during the equipment's design phase, which means that many of the changes made during commissioning, start-up, and production are not reflected. The research domain of process mining suggests that an accurate model can be obtained from an activity log. Since most industrial processes are controlled by Programmable Logic Controllers (PLCs), which offer the capability of external access through Open Platform Communications (OPC), such a log can be created automatically. The theory is that process mining algorithms can then be used to discover the desired model from the data recorded. Unfortunately, it turns out that this whole procedure is a game of chance. The quality of the discovered models strongly depends on the logged data. Not only can the records be flawed, but also the information needed to discover a complete model might not have been observed at all. To better judge the quality of the discovered models, numerous quality metrics have been proposed, which in the authors' opinion are a good guess at best. The industrial automation domain demands complete models, which cannot be guaranteed by any process mining algorithm unless it is known that the log contains all the data needed. This paper shows that for industrial equipment, it is possible to reason through the evaluation of a single recorded case, which case traces are required to guarantee that the discovered model will be complete. In the next step, the authors then introduced the concept of 'trace induction', which takes advantage of the fact that a PLC controls the observed processes. A minor change within the PLC's logic is used to force the process to execute the desired traces on demand. The resulting, minimalistic log can then be analysed by the αLC-algorithm to create a guaranteed complete, model. [ABSTRACT FROM AUTHOR]

Data centres have become the backbone of large Cloud services and applications, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining techniques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces. [ABSTRACT FROM AUTHOR]

Hybrid metaheuristics—developed based on the combination of metaheuristics with concepts and techniques from other research areas—represent an important subject in combinatorial optimization research. Data mining techniques have been coupled with metaheuristics in order to obtain patterns of suboptimal solutions, which are used to guide the search for better-cost solutions. In this paper, we incorporate a data mining procedure into a state-of-the-art heuristic for a specific problem in order to give evidences that, when a technique is able to reach an optimal solution, or a near-optimal solution with little chance of improvements, the mined patterns could be used to guide the search for the optimal or near optimal solution in less computational time. We developed a data mining hybrid version of a previously proposed and state-of-the-art multistart heuristic for the classical p-median problem. Computational experiments, conducted on a set of instances from the literature, showed that the new version of the heuristic was able to reach optimal and near-optimal solutions, on average, 27.32 % faster than the original strategy. [ABSTRACT FROM AUTHOR]

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach. [ABSTRACT FROM AUTHOR]