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As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

The paper explores the roles of involved organizations in the designation of Batu Arang in Gombak, Selangor, Malaysia, as the Coal Mining and Geological Heritage Site, the heritage significance selected as the elements of its designation and the challenges faced by the involved stakeholders during the designation process.

The primary research material is derived from the documentation review on the final draft of the Special Area Plan (RKK) of Batu Arang and the final draft of the Geopark Management Plan, field investigation on the heritage significances and interviews with the involved organizations: the Selayang Municipal Council (MPS), the State of Selangor Malay Custom and Heritage Corporation (PADAT) and the Village Community Management Council (MPKK) of Batu Arang.

This paper found that Batu Arang has the potential to be developed as an important heritage mining site and heritage tourism destination; however, many of the heritage significances are being demolished, invaded and abandoned due to human aggression or effects of nature. In addition, it reveals the roles of each involved organization, issues that occurred and challenges faced by the organizations during the designation process as a heritage site, namely in terms of management, property ownership and promotion.

The paper outlines that a heritage mining site like Batu Arang should be considered an important heritage as well as other heritage sites in Malaysia, and issues and challenges faced during the designation process should be discussed critically to ensure that these heritage significances will not be neglected and abandoned.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.

An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.

The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.

The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.

The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

This research is intended to assess the nickel smelter industry’s investment competitiveness in Indonesia and identify ways to improve its competitive advantage for the nation.

This research uses a sequential mixed-methods approach, expanding on a first qualitative phase with a second quantitative phase. Interviews are used in the qualitative phase to identify the underlying causes of issues and potential solutions to increase the competitiveness of the nickel smelter industry, while a system dynamics (SD) model is used to conduct the quantitative phase. This study uses the idea of a country’s competitive advantage from Porter’s diamond model (PDM). The model was tested and validated using SD simulation resulting in a new policy scenario, which was evaluated in metallurgy expert conferences and high policymaker discussion forums.

The results reveal the complexity of the nickel smelter industry in Indonesia and conclude that the integrated export duty beneficence policy is the most effective way to boost competitiveness. This policy gives a significant improvement both in the number of smelters and state revenue compared to the current policy. The industry’s investment competitiveness is enhanced by the six factors of the diamond model, with the first three factors being integrated strategy, limited export of excess production and export duty beneficence, while the remaining factors are metal price fluctuation, domestic demand and mineral supply which are related to mining conditions uncertainty.

The research creates a SD model to support Indonesia’s competitive advantage in the smelter industry. Despite limitations like interpretations and distorted semantic analysis, it provides a useful framework for exploring complex industry themes, excluding social factors due to limited data and knowledge requirements.

The findings of this research offer a framework for policymaking by the government to enhance the competitiveness of investments in Indonesia’s nickel smelter industry.

This study delves into Indonesia’s nickel industry competitiveness using PDM. Using a more detailed SD model with quantitative analysis, it goes beyond strategy development to provide a comprehensive approach to the nickel smelter industry.

This paper proposes a methodology based on an uncertain mining technology that identifies the linguistic relationships of ESG and its components with a financial performance metric to help the sustainability diagnosis of a region, specifically Latin America.

First, based on a relevant dataset of companies in a region, a procedure is formulated whereby an uncertain mining technology extracts the mathematically significant linguistic relationships of ESG and its components with a financial performance metric. Second, a knowledge management process is designed based on the linguistic summaries obtained from the mining process. As a final step and drawing upon the two preceding processes, a diagrammatic system of signals is proposed for diagnosing the sustainability of the region as contributed by its companies.

After this methodology is instantiated on a group of Multilatinas, it is observed that their sustainability contributions to the region are limited and that none of the identified linguistic relationships between ESG and the financial performance metric are favorable for the region.

This is the first proposal of its kind and it can be applied to any region of the world to assess the financial performance of its companies regarding their ESG commitments. In addition, it enables the region to comprehensively monitor compliance with the 2030 SDG agenda.

The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Flood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.

This investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.

Topic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.

This study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.

Worldwide challenges impose public organizations to rethink their processes and satisfactorily meet citizens’ needs. Process mining (PM) techniques enable organizations to objectively analyse and improve their processes, by providing higher process transparency and efficiency. However, extant literature on PM applications in the public sector reveals there is still limited evidence on the opportunities and challenges perceived from PM introduction in the public sector, and on PM potential to enhance public sector digital transformation: this study aims to fill these gaps.

Based on Business Process Management and digital innovation fields of research, we administered a questionnaire to a sample of Italian civil servants working in different public organizations to investigate their perceptions of PM opportunities and challenges and the extent to which it can support public sector digital transformation. A three-level analysis was conducted to inspect findings with different levels of granularity, and results were analysed both descriptively and quantitatively.

We found a positive attitude towards PM introduction in the public sector, and perceived opportunities and challenges related to both the technical and the social systems. The triangulation between close-ended and open-ended questions suggests that PM could be the missing link between public sector digitalization and digital transformation. These findings can be used by policymakers to develop the best strategies to introduce PM into public organizations and support its adoption, and by researchers to further explore PM role in public sector digital transformation.

Despite PM claiming to push digital transformation, it is not clear if it is also true for public sector organizations. This paper addresses this gap and it is among the first attempts to explore PM from civil servants’ viewpoint to investigate their perceptions of PM opportunities and challenges, as well as the variables that influence these perceptions.