A Critical Survey of Data Grid Replication Strategies Based on Data Mining Techniques

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Current research activities are leveraging the Grid to create generic- and domain-specific solutions and services for data management and knowledge discovery. Knowledge and Data Management in Grids is the third volume of the CoreGRID series; it gathers contributions by researchers and scientists working on storage, data, and knowledge management in Grid and Peer-to-Peer systems. This volume presents the latest Grid solutions and research results in key areas such as distributed storage management, Grid databases, Semantic Grid and Grid-aware data mining. Written for a professional audience of researchers and practitioners in industry, it is suitable for graduate-level students in computer science.

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

Replication is a widely used method for achieving high availability in database systems. The data replication approach can be used for solutions such as sharing data among remote offices, sharing data among dispersed users, make server data more accessible, distribute solution updates, back up data, provide internet or intranet replication. The main aim of this research is to provide a better technique for solving the drawbacks that currently exist in the literary works of data replica environment. Here, we intend to propose data replication system based on data mining techniques. That is in proposed method FDDRA (data mining based dynamic replication algorithm), we will use a data mining with combination of replication algorithm and data grid policy for data replication in the data mining environment through monitoring all data process. The data replication will be done by identifying the frequently used data patterns in the large database of a node. This will be done by Apriorialgorithm. The system availability and replica will be measured and identifying the location in which the replicated data will be stored. The result shows the efficiency of our algorithm in terms latency time and shortest path of nodes.

Usability evaluation is one of the essential aspects of software production. This evaluation should be done during the entire life cycle of a software application, from pre-production to production and post-production. However, the collection and evaluation of usability data can be a very challenging, time-consuming, and expensive task to be conducted manually, particularly for certain types of products and working conditions. These challenges may include the need to recruit participants fully engage and motivate them during evaluation, and factor in environmental conditions. Other challenges may include collecting data in real-world environments, especially when the users are geographically dispersed, minimizing evaluator and participant bias, and analyzing complex data sets, particularly when the volume of data is large. This research explores an alternative approach to automate the whole process of usability evaluation by utilizing data mining and machine learning techniques on data recorded in log files. The objective of this dissertation is to extract the value of usability-related metrics from user interactions and estimate usability measures in a quantitative manner. The use of log files for usability evaluation offers several advantages over traditional methods of evaluation, such as collecting data objectively without the need for subjective interpretation of the evaluator in some cases, creating a comprehensive view of user interactions with the possibility of identifying user behavioral patterns and trends that appear in large data sets, and collecting data from real-world scenarios instead of data from simulated scenarios. Other advantages include reducing evaluation costs, enabling remote data collection from anywhere in the world, which leads to the identification of location-dependent usability problems, and facilitating continuous monitoring over time, which leads to the identification of time-dependent usability issues. In this dissertation, Chapter II provides a comprehensive categorization, comparison, and summary of the pertinent usability evaluation techniques that utilize log files as input data in both academic and industrial research. Each method is examined carefully, and its respective strengths and weaknesses are highlighted to provide a systematic understanding of the advantages and limitations of each technique. Chapter III assesses the originality of the research questions and proposed solutions. Given the ongoing development and refinement of log file analysis tools and techniques by proficient teams in advanced corporations, and the substantial research already conducted in web usage mining, this dissertation compares its solution with prior works in these two domains. The similarities and disparities between them are evaluated to determine the uniqueness and value of

Controlled clinical trials are studies done by scientists on a concrete domain with the objective of execute a prospective analysis that attempts to compare the effect and value of one or more interventions in humans under specific conditions. Our case study is focused on applying artificial intelligence techniques to support scientists working on bio-nutritional trials, which rely on the study of the effect of consuming food with biomarkers on human health. This work is developed under the HENUFOOD2project. In order to simplify the domain, two trial stages are considered: the trial experimental execution and the trial data analysis. It is after the trial has been executed and all the information collected, when we try to discover non obvious knowledge among the trial data and support the scientists on their everyday analytical tasks. On the one hand, this analysis includes generic statistical manipulation and data discovery empowered by data mining techniques. On the other hand, a scientist support engine has been developed in order to allow non data manipulation and analysis experts to access the information and ask for new analysis processes. The approach, thanks to a user friendly interface enabled with query answering techniques, is able to interact with the user in a conversational mode and run analytical or data discovery processes, do a semantic search on previous indexed works or, finally, infer new rules to feed the expert system ontology. The expert system is not fully covered on this paper, as it will focus on the explanation of the usage of data mining and query answering techniques on that bio-nutritional focused expert system.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Data Mining Applications in Engineering and Medicine targets to help data miners who wish to apply different data mining techniques. Data mining generally covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, etc. In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management. Since Data Mining has been widely used in a medical field, this book contains different chapters reffering to some aspects and importance of its use in the mentioned field: Incorporating Domain Knowledge into Medical Image Mining, Data Mining Techniques in Pharmacovigilance, Electronic Documentation of Clinical Pharmacy Interventions in Hospitals etc. We hope that this book will inspire readers to pursue education and research in this emerging field.

With the growing technology and amount of data, the use of cloud and centralized servers is increasing. When the data of the individuals is stored by the third party it leads to the insecurity of misuse of their information in the individuals. Data Mining is carried on this data stored over cloud to get useful information, analysis and decision making purposes. But along with the process of mining, privacy preservation is the key concern. While performing data mining over large set of data there are chances of privacy violation, loss of information, loss of privacy of individuals. While concentrating only on preserving privacy can increase inefficiency and complexity of data mining techniques. Techniques used for achieving privacy lead to a significant information loss. The methods concerned about information loss generally lack behind in achieving a good level of privacy. Thus it is important to find the efficient data mining technique which can preserve privacy without causing high loss of information. This paper analyses various privacy preserving data mining techniques for their pros and cons and then gives a proposed concept to achieve privacy with minimum information loss.

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

The application of Data Mining (DM) techniques for DQ, often called Data Quality Mining (DQM), offers a wide range of possibilities for DQ assessment. The goal of this work is to propose a mechanism for data currency assessment using statistics and DM techniques. The proposed approach consists on estimating the validity period for the entities using a training set and then evaluating the probability of currency of the last known data value for each entity. The proposed scheme helps in two ways to lead to an always up-to-date database: it can warn if a certain data value is becoming obsolete, and it can inform the data manager about the best frequency for updating data.