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Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web traversal pattern mining discovers most of the users’ access patterns from web logs. This information can provide the navigation suggestions for web users such that appropriate actions can be adopted. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behaviors may be changed when the new web data is inserted into and the old web data is deleted from web logs. Besides, it is considerably difficult to select a perfect minimum support threshold during the mining process to find the interesting rules. Even though the experienced experts, they also cannot determine the appropriate minimum support. Thus, we must constantly adjust the minimum support until the satisfactory mining results can be found. The essences of incremental or interactive data mining are that we can use the previous mining results to reduce the unnecessary processes when the minimum support is changed or web logs are updated. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns and make the mining results to satisfy the users’ requirements. The experimental results show that our algorithms are more efficient than the other approaches.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

The purpose of this paper is to develop a holistic model for educational purposes using data‐mining techniques for exploring the effects of probable changes in processes related to admissions, course delivery and recruitments.

The paper proposes a framework for an effective educational process using data‐mining techniques to uncover the hidden trends and patterns and making accuracy based predictions through a higher level of analytical sophistication in the process of counselling students.

Data‐mining tools are used in academia for capitalizing on the advances of information technology. This process improves research and academic decision making through uncovering hidden trends and patterns that predict using a combination of explicit knowledge base, sophisticated analytical skills and academic domain knowledge.

The paper presents a model using a data‐mining approach for academics.

The extraction of relevant knowledge from data is called knowledge discovery (KD). The KD process requires a large amount of data and it must be reliable before mining. Complexity is not only in deriving knowledge from data but also in improving system performance with a psycho-cognitive approach. KD demands a high level of human cognition and mental activity to generate and retrieve knowledge. Therefore, this study aims to explain how psychological knowledge is involved in KD.

By understanding the cognitive processes that lead to knowledge production, KD can be improved through interventions that target psychological processes, such as attention, learning and memory. In addition, psycho-cognitive approaches can help us to better grasp the process of KD and the factors that influence its effectiveness. The study attempted to correlate interdependence by interpreting cognitive approaches to KD from a psychological perspective. The authors of this paper draw on both primary and secondary literary warrants to empirically prove psychological bending in KD.

Understanding the psychological aspects of data and KD can identify the development of tools, process and environments that support individual and teams in making sense of data and extracting valuable knowledge. The study also finds that interdisciplinary collaboration, bringing together expertise in psychology, data science and domain specific knowledge fosters effective KD processes.

The KD system cannot function well and will not be able to achieve its full potential without psycho-cognitive foundation. It was found that KD in the KD system is influenced by human cognition. The authors made a contribution to KD by fusing psycho-cognitive approaches with data-driven technology and machine learning.

Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules).

The proposed approach is based on case‐based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics' power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures.

The proposed approach combines several interestingness measures with complementary properties to improve the detection of the interesting rules. Based on a CBR process, it, also, offers three main advantages to users in a remote sensing field: automatism, integration of the users' expectations and combination of several interestingness measures while taking into account the reliability of each one. The performance of the proposed approach is evaluated and compared to other approaches using several real‐world datasets.

This study reports a valuable decision support tool for engineers, environmental authority and personnel who want to identify relevant discovered rules. The resulting rules are useful for many fields such as: disaster prevention and monitoring, growth volume and crops on farm or grassland, planting status of agricultural products, and tree distribution of forests.

The purpose of this paper is to propose a research model exploring the link between knowledge management processes and customer relationship management (CRM) performance. It seeks to answer two research questions: What are the effective drivers of knowledge management processes in the context of a CRM initiative? Do these processes make a real impact on CRM performance?

The paper is based on data obtained from a sample of 93 service companies located in Spain. The authors conducted a structural equation modeling analysis using PLS to test the proposed hypotheses.

It was observed that both technological and organizational readiness were effective drivers of knowledge management. However, it was contrasted also that the usage of social media tools was not significantly related to knowledge management. Results show a real impact of knowledge management processes on CRM performance, so companies can understand how to implement successfully those initiatives.

The main limitations of the study are that it was based on cross-sectional data and that variables were measured based on the perceptions of general managers.

Service companies need to invest in technological infrastructures, and create an appropriate organizational climate (top management support, employees commitment) in order to promote effective knowledge management processes, that will enable CRM success, paving the way for the development of marketing innovations.

This is the first empirical work that examines in confirmatory way what are the main drivers of knowledge management processes, including in the analysis the impact of both organizational and technological readiness, and considering also the usage of social media tools, in the context of a CRM initiative.

The evolution of technologies has unleashed a wealth of challenges by generating massive amount of data. Recently, biological data has increased exponentially, which has introduced several computational challenges. DNA short read alignment is an important problem in bioinformatics. The exponential growth in the number of short reads has increased the need for an ideal platform to accelerate the alignment process. Apache Spark is a cluster-computing framework that involves data parallelism and fault tolerance. In this article, we proposed a Spark-based algorithm to accelerate DNA short reads alignment problem, and it is called Spark-DNAligning. Spark-DNAligning exploits Apache Spark ’s performance optimizations such as broadcast variable, join after partitioning, caching, and in-memory computations. Spark-DNAligning is evaluated in term of performance by comparing it with SparkBWA tool and a MapReduce based algorithm called CloudBurst. All the experiments are conducted on Amazon Web Services (AWS). Results demonstrate that Spark-DNAligning outperforms both tools by providing a speedup in the range of 101–702 in aligning gigabytes of short reads to the human genome. Empirical evaluation reveals that Apache Spark offers promising solutions to DNA short reads alignment problem.

The purpose of this paper is to determine factors influencing customers’ usage of electronic banking (e-banking) services.

A survey was conducted to collect information from bank customers regarding their perceptions about e-banking services and their demographics. Multiple regression analysis is used to test the hypothesised relationships.

E-banking usage is found to be high for female, more educated, younger, and middle income customers. Among the 11 perceptual variables studied, only six variables, namely: information, performance, self-interest, service quality, satisfaction, and experience are found to be significantly and positively associated with e-banking usage.

This study identifies factors which may be focussed on by bankers during the formulation of their operations and marketing strategies to provide the best e-banking experience to their customers, enabling bankers to augment bank profitability through the strategic use of technologies.

Past studies have seldom examined the combined influence of demographics and other factors on e-banking services usage in the context of developing countries. Most of the earlier studies have considered single service or examined the adoption as only a binary variable.

The purpose of this paper is analysis of Green and Lean Six Sigma, based on the success factors in its use through a well thought-out literature review, is being planned; a framework has been integrated in a productive manner with the Green and Lean and Six Sigma methods so as to incorporate and actualize the execution.

The methodology consists of comparative investigation of Green, Lean management and Six Sigma using open written work, essential analysis at data and master experience of the researchers. To achieve this goal, a significant review of the existing literature of the subject area has been done to prepare a framework in view of the critical success factors. A study was arranged and flowed survey from various businesses utilizing John’s Macintosh Project (JMP) statistical software.

The paper establishes the distinguishing proof of five success factors with their situational importance and shows that the integrated Green and Lean Six Sigma can drive the organizations to optimize their resources and cost of services or productions.

A Green and Lean Six Sigma organization would take profits by the use of the proposed framework in an alternate extent of organizations should be dynamic. The organizations should assess their shortcomings and qualities, set needs and perceive objectives for fruitful implementation.

Suggestions are being made regarding thoughts and methods that would constitute a Green and Lean Six Sigma organization. The suggested framework compare the method for improvements that may occur in organizations while implementation of the Green and Lean management or Six Sigma.