Search results

Because of its effectiveness, process mining is rapidly becoming ubiquitous. A recent IBM Institute for Business Value (IBV) survey found that 65 percent of organizations report actively using process mining to improve processes. And in partnership with software-as-a-service (SaaS) providers to add even greater insight into their processes, 69 percent compare their organization’s data with other SaaS customers. And as many as 77 percent of supply chain executives say they are at least at the implementation stage of process and task mining.

The IBM Institute for Business Value and APQC, in cooperation with Oxford Economics, surveyed 2,000 C-level executives in first half of 2022 from 13 countries in all major geographies and across 22 industries. The IBV and APQC implemented an in-depth analysis of how organizations use benchmarking and process mining tools, the benefits they gain from use of these tools and how they anticipate using them in the future.

Big data and digital technologies also creates new possibilities for measuring performance and revealing process improvement opportunities through process mining ? a relatively new discipline that applies data science to discover, validate and improve workflows in real time.

By utilizing data from IT systems to create a process model and then examining the end-to-end process, process mining enables root causes of variations from norms to be identified using specialized algorithms, and these insights enable management to see if processes are functioning as intended and identify new opportunities to optimize them.

More recently, the scope of process mining initiatives has widened to encompass more sophisticated mission-critical functions, notably human capital, cybersecurity and sales. Organizations that embrace process mining outperform others across key business measures, including profitability, innovation, agility, customer satisfaction and technological sophistication. 10;

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

It is recognised by valuers that the effects of coal mining on land in Great Britain can have a significant impact oon the value of a particular site when it is being considered for potential surface development purposes. In this respect a great many buildings are erected successfully each year within mining areas after the effects of mining have been taken into account. If, however, the adverse effects of mining are not recognised and during the site development stage a problem suddenly appears, it is obvious that this may lead to considerable increases in cost and/or delay to the proposed development for which the land was originally purchased. Obviously with sufficient prior appreciation and investigation of the mining position such situations could be avoided. It is with this in mind that the following paper aims to indicate in broad outline, but in such detail relevant to the valuer, some of the major influences of coal mining which require to be recognised and equated in monetary terms when assessing potential site values for development purposes in mining areas.

This paper aims to develop a computer simulation processing method to simulate the mining operation of self-advancing semi-continuous mining technology and optimize the shift step of belt conveyor by using simulation modeling framework based on intelligent objects (SIMIO). The method would effectively solve the challenge of field testing such large-scale equipment.

The four operational modes of self-advancing semi-continuous mining technology at single bench had been illustrated. The operational system of this technology was analyzed and broken down to single units. By analyzing the time constitution of one operation cycle, the theoretical optimization model of shift step can be established and the optimization criteria is the time utilization ratio being maximum. Once the simulation flow was determined, a three-dimensional (3D) computer simulation model of this mining technology was developed by adapting the SIMIO simulating software to the theoretical model. The models were run to investigate the outputs from different operational modes using geological and mining data from East open-pit mine.

The result of these simulations showed that the four-mining-width one-shift (FMWOS) is at maximum production capacity during all operation modes. If transfer equipment is necessary, then this mode can adapt, but system will become more complex. There are minor differences between two-mining-width one-shift and three-mining-width one-shift. If transfer equipment is not necessary, then the two-mining-width one-shift can adapt during actual production.

The simulation results show that the proposed method can achieve the optimal shift step of a belt conveyor and effectively reduce the time loss caused by the coordination of multiple pieces of equipment while simultaneously improving operational efficiency.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The purpose of this paper is to examine the social sustainability strategies of multinational mining companies in addressing risks in areas of exploratory intensity and contributing to social capital in local communities.

This study is situated within an interpretivist paradigm and uses a qualitative research methodology, drawing on data from semi-structured interviews with multinational mining companies operating in Ghana and key stakeholder groups.

The results of this study show that multinational mining companies use strategies broadly categorised as social responsibility, social compliance, local content and relationship proximity to address challenges embedded in the extractive process.

By examining the strategies in addressing risks to sustainable social development in mining communities, this study contributes to fill the social sustainability gaps in mining research and adds to relevant literature.

Background: Amid increasing leadership failures in the global business context, the mining industry is one of the industries with many adverse incidents, affecting employee safety, the environment, and surrounding communities. Emerging economies tend to have unique socio-economic challenges and greater relative economic dependence on mining, presenting unique challenges to leaders. The purpose of this research was to study the realities of responsible leadership in the mining industry in an emerging economy.

Methods: A qualitative research study, consisting of semi-structured interviews was conducted. Nine senior mine managers were selected to represent perspectives from different operations and mining houses. Data was gathered from August to October 2020 in South Africa, an emerging economy with significant mining operations. A thematic analysis of interview transcripts was conducted through the use of software, rendering five themes, with 12 sub-themes.

Results: The research found that requirements on mining leaders in emerging economies demand consistent balancing of a complex set of competing risks, whilst attending to paradoxical requirements among operations, and internal and external stakeholders. Leaders face several competing requirements from stakeholders, the environment, mining practices, and time frames. Responsible leaders must navigate a paradoxical maze of needs and time horizons, with several conflicting forces and dilemmas, and dichotomous relationships. Responsible leadership in the mining industry of an emerging economy is a proverbial minefield of paradoxes and dilemmas between responsible intentions and practical realities. These paradoxes and dilemmas are specifically acute in the context of emerging economies due to the dire socio-economic situations. A total of 10 competencies emerged as essential responsible leadership requirements in this context.

Conclusions: The study provides an in-depth understanding of the intricacies of responsible leadership in the mining industry of an emerging economy. This understanding will contribute to capacitating leaders in the mining industries of emerging economies to act responsibly.

This paper aims to examine the compensation practices and the valuation methods valuers apply in the context of the current legal framework for expropriation to assess compensation for farms impacted by mining in Ghana.

Compensation reports and archival materials were examined to identify the issues related to the valuation methods, compensation practices and expropriation procedures in the mining sector. Interviews were then conducted with 35 farmers and farmers' representatives, officials of mining companies, representatives of the Land Valuation Division of the Lands Commission and valuers/researchers on the issues identified through the document analysis.

The results reveal that the lack of express standards for assessing compensation for mining-impacted crops has occasioned variations in the valuation methods and the standard crop population for compensation. The study further reveals the impacts of exchange rate distortions on crop compensation values.

The study empirically substantiates the arguments for a revised compensation regime in Ghana's mining sector. Valuers, mining companies and policymakers' awareness of this research will impact farm compensation valuation practices in the future.

The adequacy of compensation for mining-impacted farmers remains a topical issue, especially in African countries. This research contributes to the literature and reveals the socio-economic impacts of the current compensation regime on the livelihoods of expropriated farmers.

This paper is the first to analyse the valuation methods, the compensation values and the key parameters valuers apply in assessing compensation for mining-impacted crops in Ghana.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

This study aims to examine the socio-economic and environmental impacts of mining activities as perceived by communities in Ghana, with data being drawn from primary and secondary sources.

A total of 90 community residents were interviewed, with 15 from each of the six selected different communities.

The findings revealed a positive perception that corporate social responsibility (CSR) practices of mining companies contribute to the development of mining communities in Ghana by creating jobs and generating income. However, it became clear that mining activities, particularly small-scale mining, create many social and environmental challenges as well. This includes land degradation, which reduces the fertility of community-owned land suitable for agricultural use. In addition, pollution of waterways and streams intensifies the plight of community residents living in mining areas.

Since 2011, the mining industry has invested between US$12m (in 2013) and US$44m (in 2011) in Ghana’s communities. The amount spent in 2019 was US$24m. The funds were spent by the industry in areas such as roads, education, health and electricity, among others. Still, it seems more effort is needed by the mining companies to harmonise the CSR practice and gain better impression by local people. In spite of the mining industry’s investment levels, more than half of the community respondents said it was insufficient. One-third of the respondents went as far as suggesting the mining companies had a negative impact on infrastructure improvement and community development.