Mining Process Research Articles

Organization processes and artificial intelligence (AI) for healthcare processes reorganization: a case study

PurposeThis study aims to provide a methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units.Design/methodology/approachProcess design and process data-driven simulation: the processes are designed by the business process modeling and notation and the unified modeling language standards. Data processing is performed by Correlation matrix analysis and by Fuzzy c-Means data clustering. The matching between the two methods provides the most indicated final corrective actions of the “TO BE” organizational model.FindingsThis proposed method, experimentally applied in this work merging the lean management model (LMM), process mining (PM) and AI methods, named process mining organization (PMO) model (Rosa et al., 2023 (b)), is able to improve organizational processes of a hospitalization unit (HU) by developing three propaedeutic phases: (1) analysis of the current state of the processes (“AS IS”) by identifying the critical issues as bottlenecks of processes, (2) AI data processing able to provide additional classified and predicted information allowing the “TO BE” workflow process and (3) implementation of corrective actions suggested by the PMO in order to support strategic decision-making processes in the short, medium and long term by classifying an order of priority about the healthcare procedures/protocols to perform.Research limitations/implicationsThe main limitation of the proposed case study is in the limited number of available digital data to process. This aspect reduces the capability to interpret result. In any case, the proposed methodology is a “launch” work to define a new approach to integrate organizational processes including workflow design and AI scoring. Future work will be focused on managerial implications due to use of the discussed method: design and development of new human resource (HR) organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Practical implicationsMain implications are in healthcare managerial scenarios: design and development of new HR organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Social implicationsCare services optimization is addressed on HUs.Originality/valueThe design of HR organizational processes integrates AI-driven data decision-making processes. This case study examines AI-based innovation analytics addressed on resource efficiency.

Developing analytics-driven maintenance data mining processes: a design science approach

PurposeData mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service.Design/methodology/approachThis study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based on the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment.FindingsFirstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection.Originality/valueThe study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.

Prevention and Control of Spontaneous Combustion of Coal in the Goaf Under Z+I Ventilation Based on 2GN00 mining Process

ABSTRACT Traditional coal pillar mining technology will cause the waste of resources, and the roadway excavation. The N00 process eliminates the need for roadway excavation and coal pillar storage. The ventilation mode under the N00 construction method differs from conventional U-shaped ventilation. It is difficult to determine the danger area of spontaneous combustion. Therefore, this paper takes 21607 goaf with Z+I ventilation under the 2GN00 construction method in a mine in Guizhou province as the engineering background and adopts experimental research and numerical simulation methods to prevent and control coal spontaneous combustion hazards. Firstly, the spontaneous combustion tendency experiment determines the grade of spontaneous combustion tendency of coal samples. Secondly, the Fluent numerical simulation method is used to screen the optimal wind speed ratio of the two air inlets. Finally, numerical simulation is used to select the optimal spraying distance. The results show that the spontaneous combustion grade of coal samples makes it easy to produce spontaneous combustion. By analyzing the numerical simulation results of 6 groups of wind speed ratios, the best wind speed ratio of the two inlets is 1:1. Currently, the risk area of spontaneous combustion in the goaf is 5440.8 m2. Then the numerical simulation of the best slurry spraying distance is carried out with the wind speed ratio 1:1. The optimum distance of 90 m was finally determined. The area of the spontaneous combustion hazards area was reduced to 2223.8 m2. The results provide theoretical guidance and technical support for engineering applications.

Surgery as Process: Do Little Things Matter?

Using the techniques of cognitive task analysis and process mining may allow an increased understanding of both the action and decision components of a surgical procedure. Prior research has demonstrated that these are not intuitive and seemingly small, insignificant elements may have a large impact on success. This increased understanding may allow a more effective transfer of skills by facilitating the transfer of explicit knowledge into tacit knowledge.

Research on Roof Cutting and Pressure Relief Technology of Gob-side Entry Retaining with Hard Roof

Traditional coal pillar roadway protection is easy to cause roadway stress concentration, roadway deformation instability, and low recovery rate of coal resources. In order to solve the stability of roadway mining process and coal mine efficiency. Aiming at the problem of gob-side entry protection by deep hole pre-splitting blasting with hard roof, taking 1207 working face of Jiegou Coal Mine as the engineering background, through theoretical analysis, numerical simulation, field industrial test and other methods, the technology of roof cutting and pressure relief and constant resistance anchor cable reinforcement and roadway protection is put forward, and the key parameters such as appropriate roof cutting height and roof cutting angle are analyzed to achieve the effect of roof cutting and pressure relief, and the roadway reinforcement support scheme is designed. The field research shows that the roof cutting and roadway protection technology has a good control effect on 1207 working face roadway, realizes the safe production of working face, and provides certain reference value for this similar condition.

RECLAMATION COST CALCULATION OF PT. X IN SORONG CITY, SOUTHWEST PAPUA PROVINCE

Reclamation is an effort carried out throughout the mining process to organize, restore, and enhance the quality of the environment and ecosystem, so that the area can function again according to its intended purpose. In the mining industry, companies are required to conduct reclamation. Before carrying out reclamation, the company must calculate the required reclamation costs. PT. X is one of the companies engaged in andesite mining, which is administratively located in Saoka Village, Maladumes District, Sorong City, Southwest Papua Province. After completing the mining activities, PT. X will carry out reclamation by planting pine trees during the period from 2024 to 2028. Based on this, it is necessary to calculate the reclamation costs to determine the expenses the company will incur for this activity. The reclamation cost calculations are based on the Minister of Energy and Mineral Resources Decree Number 1827/K/30/MEM/2018, which outlines the guidelines for implementing Good Mining Engineering Principles. Reclamation costs consist of both direct and indirect costs. Based on the research findings, the total direct and indirect costs that PT. X must incur for reclamation activities from 2024 to 2028 are IDR 359,238,257 and IDR 119,972,490, respectively. Therefore, the total reclamation costs PT. X will incur for the reclamation activities from 2024 to 2028 amount to IDR 479,210,747

Improving Vulnerability Management Through Process Mining

With the number of cyber-attacks growing rapidly and the opportunities to attack companies widening, vulnerability management is gaining increased importance. It needs to reduce the variety of possibilities by remediating vulnerabilities found in IT infrastructures. Process mining is an established method used to discover, analyze, and manage data on (implemented) business processes. This paper proposes utilizing process mining for business-critical processes, including IT processes. This work explores how vulnerability management processes can be improved through process monitoring, targeting the operation time of processes, remediation, and the continuous management of the vulnerability process design. Thereby, process mining is applied for monitoring following the CRISP data mining approach. Process analysis includes process discovery, conformance checking, and process enhancement. Improving the vulnerability management process is based on the discovered vulnerability process, which is enhanced with throughput time data as well as priority, source, and assignment group data. Improvements concern changing the configuration of a source and better training for certain assignment groups. Process discovery can also be used to identify the cause for improper remediation of vulnerabilities. In this case, unwanted activity flows, which indicate that a major part of the vulnerability management process is being skipped, need to be identified. Finally, the continuous use of process mining allows for the monitoring of process modifications, including the impact of implemented improvements.

Preparation of Composite Materials with Slow-Release Biocides and Solidifying Agents for Remediation of Acid Pollution in Coal Gangue

The processes of coal mining and washing generate a substantial amount of coal gangue. During prolonged outdoor storage, this waste can lead to both direct and indirect environmental pollution, as well as geological hazards. Recent research has indicated that the redox processes of coal gangue are regulated by microorganisms. Techniques such as the application of biocides and the facilitation of microbial interactions have proven effective in controlling the acidic pollution of coal gangue in the short term. However, conventional doping methods that couple sulfate-reducing bacteria with biocides face challenges, including a short effective duration and poor stability. To address these issues, this study utilized corn straw biochar as a microbial attachment material and incorporated water-retaining agents as slow-release biocide carriers, resulting in the development of an environmentally friendly microbial remediation material. This study selected 0.6 g of biochar produced from the pyrolysis of corn straw at 700 °C to immobilize sulfate-reducing bacteria. Additionally, 0.6 g of polyacrylamide was used to prepare a slow-release bactericide with 100 mL of a sodium dodecyl sulfate solution at a concentration of 50 mg·L−1. The composite remediation material successfully raises the pH of weathered coal gangue leachate from 4.32 to 6.88. Its addition notably reduces the sulfate ion concentration in the weathered coal gangue, with sulfate content decreasing by 86.45%. Additionally, the composite material effectively lowers the salinity of the weathered coal gangue. The composite immobilizes heavy metal ions within the weathered coal gangue, achieving an approximate removal rate of 80% over 30 days. Following the introduction of the composite material, significant changes were observed in the dominant microbial communities and population abundances on the surface of the coal gangue. The composite demonstrated the ability to rapidly, sustainably, and effectively remediate the acidification pollution associated with coal gangue.

A Tertiary Study for Process Mining

Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available secondary studies in process mining, respond to research questions relating to the thematic areas covered in the studies, as well as trends regarding their quality, and report on findings for publication venues, citations, guidelines used, and demographics. Method: A tertiary study based on systematic secondary studies published up to March 2023. A total of 25 secondary studies related to process mining have been identified following the application of inclusion/exclusion criteria and quality assessment. Results: The most popular thematic areas addressed are technologies and applications for process mining and healthcare. The secondary studies in process mining have a medium quality score of 3.5. The guidelines introduced by Kitchenham over the years are preferred in secondary studies in this field. There is no trend related to the number of primary studies included in secondary studies in process mining. Conclusion: Although numerous secondary studies exist for process mining, there is still room for more research, specifically in the areas highlighted in this study. Future researchers can use this study for reference, and they can also use the listed research topics to dive deep into the issues identified.

Wettability and physical modification of coal under vacuum saturation invasion of SiO2 nanofluids

Coal seam water injection technology could improve the water content of coal seam, which is an effective technical measure to reduce dust generation in the mining process. Water-based silica nanofluids are a green wetting agent for coal seam water injection. To understand the wetting mode of nanofluids in coal, it is necessary to explore the physico-chemical properties of nanofluid-modified coals. First, a new idea of saturation and intrusion of nanofluid into coal was proposed by using vacuum pressurized saturation device. Then, the physical and chemical properties of the modified coal were studied by Fourier transform infrared spectrometer, low-temperature liquid nitrogen adsorption experiments, and uniaxial compression mechanics experiments. The results showed that the content of oxygen-containing functional groups in the modified coal increased, which was positively correlated with the concentration of nanofluids. The pore structure of the modified coal sample changed from complex to simple, and the nanoparticles blocked the micropores to make the coal surface smooth. The saturation invasion of SiO2 nanofluids changed the mechanical properties of coal samples, and the compressive resistance of coal was weakened, and the minimum strength of the coal invaded by 1.5 wt. % SiO2 nanofluid saturation was 13.68 MPa. The saturation intrusion of SiO2 nanofluids has a negative effect on the surface adsorption of coal samples and the blockage of microporous structures, and makes the coal seam easier to be wetted, which contributes to the application and development of nanofluids in the field of coal seam water injection.

Modelling and optimization of trajectory deviation for compound directional drilling in coal mines

During the sliding mode of compound directional drilling process in coal mines, the inclined angle and azimuth angle need to be changed to minimize the trajectory deviation between the actual trajectory and the designed trajectory. The inclined angle and azimuth angle can be changed by adjusting the tool face angle. In this article, a modelling and optimization method is proposed to obtain optimal tool face angle for sliding mode. Firstly, the gaussian process regression method is employed to build calculation model of trajectory deviation. The optimization model of trajectory deviation consists of the established calculation model of trajectory deviation and the set constraints. Then, the optimal inclination angle and azimuth angle are obtained by solving the constitutive optimization model using an improved particle swarm optimization algorithm. Next, the calculation model of tool face angle is built to represent the relationship between inclination angle, azimuth angle and tool face angle. The optimal tool face angle is calculated based on optimal inclined angle and azimuth angle. Finally, an industrial case study based on the actual drilling data verifies that the proposed method is beneficial to track designed trajectory during drilling process. The proposed method meets the requirements of on-site construction accuracy and ensures the safety and stability of drilling.

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective.

Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.g., object detection and point cloud learning), and natural language processing (e.g., relation extraction and sequence learning), to name a few. With the emergence of Transformers in natural language processing and computer vision, graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation while avoiding strict structural inductive biases. In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective. Specifically, we divide their applications in computer vision into five categories according to the modality of input data, i.e., 2D natural images, videos, 3D data, vision + language, and medical images. In each category, we further divide the applications according to a set of vision tasks. Such a task-oriented taxonomy allows us to examine how each task is tackled by different GNN-based approaches and how well these approaches perform. Based on the necessary preliminaries, we provide the definitions and challenges of the tasks, in-depth coverage of the representative approaches, as well as discussions regarding insights, limitations, and future directions.

Integrated vertical mining and processing for Critical Mineral: A case study in Obi Island, North Maluku Province

Abstract Harita Nickel Group in Obi Island, Indonesia pioneered the utilization of limonite nickel ore (Ni <1.5%) for electric car batteries in the country. Nickel mining in Obi Island transitioned from nickel mining in 2010 to integrated mining by establishing downstream processing of raw materials for stainless steel in 2016 and electric car battery in 2018. Pyrometallurgical and hydrometallurgical technologies are employed in factories within Harita Nickel’s mining concession area, notably PT Trimegah Bangun Persada Tbk (PTTBP). This vertically integrated mining and downstream processing in Obi Island, South Halmahera Regency, North Maluku Province, marks a significant advancement in Indonesia’s mining industry. The close proximity (<3km) of mining and factory locations ensures efficiency and low costs. In vertically integrated mining, companies seek to maximize the use of all materials involved in production processes, minimizing waste and optimizing resource utilization. This research focusing on the waste generated during nickel processing of RKEF can be repurposing into new products, reducing environmental impact and increasing sustainability. Nickel, a critical mineral, has garnered global attention, particularly since Indonesia emerged as the world’s largest nickel ore reserve. The extractive industry, employing pyrometallurgy technology, has rapidly produced stainless steel raw materials from nickel mining since 2016. Indonesia’s nickel ore, known as saprolite nickel with high nickel content (>1.7%), has been exploited since the 1970s, while the low-grade limonite nickel layer gained traction only around mid-2018. In the future, Harita Nickel project in western Obi Island will be developed as an Eco Industrial Park for a greater integration, highlighting the comprehensive benefits of vertically integrated mining and providing a more thorough and insightful evaluation of its potential.

Forensic Discrimination of Lipstick Stains Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy and Pattern Recognition Techniques

Lipstick stains discovered on objects (such as drinking cups) may be utilized as evidence and/or lead in forensic investigations by chemical analysis. Hence, this study analyzed two brands of lipstick stains' organic composition on paper cups over 0, 24, and 48 h of exposure using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), coupled with pattern recognition techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Spectral analysis revealed several functional group differences between the two brands of lipstick, viz. Wardah (W) and Silkygirl (S) stains, particularly in the fingerprint region of 1800–650 cm−1. Application of PCA (variance of ≥ 70%) and LDA successfully provided the suitable means for categorically classifying (a) the different brands of lipstick, (b) the male and female volunteers, as well as (c) the different periods of exposure. The fact that the cross-validation correct classification rates for all the LDA models exceeded 90.0%, their suitability for forensic provenance application of lip stains appears statistically supported. Hence, such an approach to combine the non-destructive ATR-FTIR analysis with PCA and LDA is not only easy and cost-effective but also forensically relevant for enabling the data mining process for forensic intelligence.

Enabling knowledge discovery through low utility itemset mining

While high utility mining aims to identify patterns that maximize profits, low utility mining (LUIM) focuses on uncovering exceptional patterns, enabling proactive interventions before issues escalate. Existing high utility itemset mining methods use upper-bound pruning to reduce the search space. However, this approach is ineffective for LUIM, as it inadvertently eliminates low utility itemsets (LUIs). In addition, LUIM struggles with distinguishing between LUIs and zero-utility candidates. These challenges in LUIM can be summarized as follows: (1) reducing the search space of LUIM, and (2) distinguishing genuine LUIs from zero-utility candidates. To address these issues, we propose LUIMiner, an algorithm designed to accurately and efficiently find the complete set of LUIs. LUIMiner incorporates two lower-bound pruning strategies—depth and width—to reduce search space and streamline the mining process. We also introduce a redesigned search tree that processes candidates from long to short, enabling effective lower-bound-based pruning. Additionally, a preprocessing phase filters out zero-utility itemsets early in the process by calculation of maximal non-mutually contained itemsets. The Bgenerator structure uses bitwise operations to accelerate computations and reduce database scans. Experimental comparisons with state-of-the-art LUG-Miner & LUIMA demonstrate the effectiveness and efficiency of our algorithm.

A feasibility study on using tire-recycled steel fibers to enhance the toughness of constructional backfill body

Constructional backfill body (CBB) commonly faces the issue of high brittleness and low toughness. Herein, a novel approach is proposed to improve the toughness of CBB using tire-recycled steel fibers (RSF). Thus, the impact of different RSF dosages on the performance of CBB was investigated. The results showed that compared to the mixtures without RSF, the slump values of the fresh mixtures decreased by 5.49%, 12.94%, and 23.14%, respectively, when the RSF dosages were 0.5%, 1%, and 1.5%. However, the slump values of all test groups remained above 190mm, meeting the requirements for workability. The incorporation of RSF led to a decrease in uniaxial compressive strength, with the degree of decrease following the order of 1.5%, 0.5%, and 1% dosages. However, all test groups met the requirements for the strength of CBB. Toughness increased with the increase in the RSF dosage, but the toughening effect diminished with the increase in the curing age. Hydration products encapsulated RSF, and RSF of different diameters and lengths jointly inhibited the expansion of CBB cracks, which is the fundamental reason for the improvement in toughness. The geometric variability of RSF enhanced mechanical interlocking, bridging, and inlaying effects, further contributing to the toughness of CBB. Industrial test results demonstrated that throughout the entire mining process, CBB did not experience fracturing or instability, proving the feasibility of using RSF to toughen CBB. The research findings are conducive to improving underground space safety and exploring large-scale reuse pathways for waste tires.

Deep-Learning Model for Mortality Prediction of ICU Patients with Paralytic Ileus

Paralytic Ileus (PI) patients in the Intensive Care Unit (ICU) face a significant risk of death. Current predictive models for PI are often complex and rely on many variables, resulting in unreliable outcomes for such a serious health condition. Predicting mortality in ICU patients with PI is particularly challenging due to the vast amount of data and numerous features involved. To address this issue, a deep-learning predictive framework was developed using the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset, which includes data from 1017 ICU patients with PI. By employing SHAP (SHapley Additive exPlanations) analysis, we were able to narrow down the features to six distinct clinical lab items. The proposed framework, called DLMP (Deep Learning Model for Mortality Prediction of ICU Patients with PI), utilizes these six unique clinical lab items: Anion gap, Platelet, PTT, BUN, Total Bilirubin, and Bicarbonate, along with one demographic variable as inputs to a neural network consisting of only two neuron layers. DLMP achieved an outstanding prediction performance with an AUC score of 0.887, outperforming existing predictive models for ICU patients with PI. The DLMP framework significantly enhances the prediction of mortality for PI patients compared to traditional process mining and machine learning models. This model holds considerable potential for prognosis, enabling families to be better informed about the severity of a patient’s condition and to prepare accordingly. Furthermore, the model is valuable for research purposes and clinical trials.

The Performance of Waste Marble Instead of Coarse Aggregate

Now a days we are faced with an important consumption and a growing need for aggregates because of the growth in industrial production, this situation has led to a fast decrease of available resources. On the other hand, a high volume of marble production has generated a considerable amount of waste materials; almost 70%of this mineral gets wasted in the mining processing and polishing stages which have a serious impact on the environment. The processing waste is dumped and threatening the aquifer. Therefore, it has become necessary to reuse these wastes particularly in the manufacture of concrete products for construction purposes. This experimental study presents the variation in the strength of concrete when replacing aggregates by waste marble from 0% to 100% in steps of 25%, 50%, 75% & 100%. M25 grade of concrete were taken for study of slump value is reducing for more % of replacing the stone dust. The compressive strength of concrete cubes at the age of 7, 14 and 28 days were obtained at room temperature. From test result it was found that the maximum compressive strength is obtained only at 50% replacement of coarse aggregate by waste marble at room temperature. This result gives a clear picture that waste marble can be utilized in concrete mixtures as a good substitute for coarse aggregate giving higher strength.

A Case Study on the Data Mining-Based Prediction of Students’ Performance for Effective and Sustainable E-Learning

The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. Student performance prediction (SPP) offers potential benefits, including personalized learning and early interventions. However, challenges such as (1) data quality and availability and (2) incomplete and inconsistent data complicate this process. Moreover, to support the fourth Sustainable Development Goal (SDG), we focus on the quality of education. A case study approach is used using data mining techniques, particularly classification, regression, and clustering, to predict student performance. The case presented aims to predict risks and ensure academic success and quality. The cross-industry standard process for data mining (CRISP-DM) methodology is used to structure and guide the prediction process. The study shows that using data from student learning processes within an academic success prediction model and data mining can identify at-risk students.

Towards the Implementation of Heuristics Miner in .Net Framework

Process mining is an interdisciplinary field that bridges the gap between data mining and business process analysis. It revolves around the core concept of leveraging recorded event logs to automate the generation, validation, and refinement of process model. A majority of currently accessible process mining solutions are distributed in form of black box systems - software with no openly accessible source code - or as libraries for scripting languages like Python or R. In this sense, it is necessary to design and build an understandable foundation of a process mining library with the intention of presenting the possibility of incorporating process mining into many programs written in the language or aiding other programmers interested in the potential of such technologies. Therefore, this paper describes and demonstrates the usability of an advanced algorithm for process mining in the .NET platform, namely, the Heuristic miner for process modelling.