Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the quality.Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours.This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints.By investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance analysis.Furthermore,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the system.The configuration of the system and the conformance checking for further enhancement is also addressed in this research.To achieve the objectives,this research uses process mining techniques,i.e.process discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and enhancement.Initially,the identified executed process is reconstructed by using the process discovery techniques.Following the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and redesigning.Finally,some recommendations are made to improve the enterprise management system processes.

Classification and quantification of timestamp data quality issues and its impact on data quality outcome

Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling.The timestamp in process mining gives an insight on process performance,conformance,and modelling.This therefore means problems with the timestamp will result in misrepresentations of the mined process.A few articles have been published on the quantification of data quality problems but just one of the articles at the time of this paper is based on the quantification of timestamp quality problems.This article evaluates the quality of timestamps in event log across two axes using eleven quality dimensions and four levels of potential data quality problems.The eleven data quality dimensions were obtained by doing a thorough literature review of more than fifty process mining articles which focus on quality dimensions.This evaluation resulted in twelve data quality quantification metrics and the metrics were applied to the MIMIC-ll dataset as an illustration.The outcome of the timestamp quality quantification using the proposed typology enabled the user to appreciate the quality of the event log and thus makes it possible to evaluate the risk of carrying out specific data cleaning measures to improve the process mining outcome.

Valorization of mining waste and tailings through paste backfilling solution, Imiter operation, Morocco

Mine waste and process tailings storage is one of important challenge for which mining operations are increasingly confronted. Treatment discharges of plants and main part of waste rock development are generally stored on surface areas. The volume and chemical characteristics of these materials generate serious problem for required storage spaces and mainly environmental degradation. Paste backfill(PBF) is one of ingenious solutions to minimize the quantity of tailings to store. PBF is basically defined as a combination of mine processing tailings, binder, and water mixing. The purpose of this paper is to present backfilling components characterization and formula verification for a waste valorization solution through paste backfilling technology in Imiter operation. Obtained results and realized analysis demonstrate PBF conformity and adequacy with assigned underground functions. However the studied recipe can be more ameliorated to obtain an optimal mixture ensuring the required mechanical strength.

Optimization of clay material mixture ratio and filling process in gypsum mine goaf

Because there is neither waste rock nor mill tailings in the gypsum mine, and the buildings on the goaf of gypsum mine are needed to be protected, the research proposed the scheme of the clay filling technology. Gypsum, cement, lime and water glass were used as adhesive, and the strength of different material ratios were investigated in this study. The influence factors of clay strength were obtained in the order of cement, gypsum, water glass and lime. The results show that the cement content is the determinant influence factor, and gypsum has positive effects, while the water glass can enhance both clay strength and the fluidity of the filing slurry. Furthermore, combining chaotic optimization method with neural network, the optimal ratio of composite cementing agent was obtained. The results show that the optimal ratio of water glass, cement, lime and clay (in quality) is 1.17:6.74:4.17:87.92 in the process of bottom self-flow filling, while the optimal ratio is 1.78:9.58:4.71:83.93 for roof-contacted filling. A novel filling process to fill in gypsum mine goaf with clay is established. The engineering practice shows that the filling cost is low, thus, notable economic benefit is achieved.

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

A Workflow Process Mining Algorithm Based on Synchro-Net

Sometimes historic information about workflow execution is needed to analyze business processes. Process mining aims at extracting information from event logs for capturing a business process in execution. In this paper a process mining algorithm is proposed based on Synchro-Net which is a synchronization-based model of workflow logic and workflow semantics. With this mining algorithm based on the model, problems such as invisible tasks and short-loops can be dealt with at ease. A process mining example is presented to illustrate the algorithm, and the evaluation is also given.

APPLYING PROCESS MINING APPROACH TO SUPPORT THE VERIFICATION OF A MULTI-AGENT SYSTEM

Using agent development tools to construct an agent-based system is a well applied approach. However, the development tools usually do not have the function to check the feasibility about the workflow of the agent system during it implementation stage. Therefore, to develop an evaluation approach to analyze the feasibility of a developing agent system such that the improper workflow of an agent system can be found in the early design stage is a necessary task to reduce the risk of implementation. In this research, a Petri Net （PN） based three-stage evaluation approach was developed. In the conceptual stage, the pitfall of the current agent system developing process was examined and an improvement analysis process was specified. Then, in the system design stage, an evaluation approach which extracted the process log file from a developing agent system into a PN model in terms of a process mining approach-or algorithm was proposed. This model was simulated in a PN simulation package. The agent system performance was evaluated in terms of analyzing the deadlock phenomena of the PN model. Finally, in the implementation stage, the proposed concept was implemented by using an agent developing tool JADE and a PN simulation tool CPN. An agent-based robotic assembly system was used to examine the possible deadlock of the agent system.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.

Baise Mining Group's Annual 1-million-ton Deep Aluminum Processing Project Commenced

The Baise Municipal Government held a commencement ceremony for Baise Mining Group’s1-million-ton deep aluminum processing project('Project')at its base in the Poverty Alleviation Industrial Park in Pingguo County.It’s learnt that the Project includes an annual450,000-ton medium and thick plates and plate belts for transportation project,an annual 160。