In today's competitive world, information has emerged as one of the important production factors. As a result, the effort to extract information from data has attracted the attention of many people involved in the information industry and related fields. The large volume of data is constantly growing in all fields and the vast difference in data production process has increased the complexity of information management and extraction. Recently, several strategies and techniques have been used to collect, store, organize and efficiently manage existing data and achieve meaningful results, and data mining is one of the recent developments in the direction of data management technologies. The term data mining refers to the semi-automatic process of analyzing large databases and data warehouses in order to find useful and applicabale patterns. In this research, we are going to examine the operationalization process of data mining and do a practical analysis of this issue. In addition, we will research about the important applications and tools of this field.

The emergence of data mining science has made data become one of the most valuable assets of organizations and with the correct use of this trump card, software systems can produce results in a different and effective way. The process of extracting and discovering patterns and correlations from a large volume of raw data from one or more databases is called data mining. Data mining is an important and fundamental part in the analysis of distributed information of today's organizations. The data obtained from data mining can be used in business intelligence and advanced analysis. Increasing capacity, finding hidden patterns, trends and correlations in data sets is one of the main advantages of data mining tools. Due to the evolution of data storage technology and the growth of big data, the use of data mining techniques has increased dramatically in the last two decades. Using the best data mining tools helps businesses to make decisions and implement knowledge-based processes more efficiently by identifying hidden relationships and patterns in the data. Despite the technology constantly evolving to handle large-scale data, leaders still face challenges around scalability and automation. According to the importance of the topic in this article, we are going to examine the applications of data mining, requirements, process and important tools in this field. In the end, we will examine the technological perspective of data mining.

Ransomware attacks are taking advantage of the ongoing coronavirus pandemics and attacking the vulnerable systems in the health sector. Modeling ransomware attacks help to identify and simulate attacks against security environments, using likely adversary techniques. process mining (PM) is a field of study that focuses on analyzing process logs linked with the execution of the processes of a system to acquire insight into the variety of characteristics of how the functions behave. This paper presents a PM conformance-based approach to determining ransomware processes. First, frequent ransomware techniques were identified using state-of-the-art MITRE ATT&CK. Then, a model was developed to gather ransomware techniques using a process-based approach. The PM-based Prom tool is used to check the conformance of malware processes alongside the presented model to illustrate its efficiency. The model can identify chain processes associated with ransom-related behaviors. In this study, the presented model was evaluated using thirty common malwares in the healthcare industry. The approach demonstrates that this model could successfully classify ninety percent of malware instances as ransomware and non-ransomware. Finally, guidelines for future research are provided. We believe the proposed method will uncover behavioral models that will enable us to hunt ransomware threats.

Today, the aviation industry plays a significant role in relations between different countries of the world, demonstrating the economic and military power and extending essentials of a country. Improving the performance of the aviation industry by analyzing big data available using intelligent methods, increases the efficiency of this system. According to the previous studies, despite the importance of this industry and large amount of data, there has not been adequate attention using data-based methods in past studies in order to discover knowledge in this industry. The process mining is one of these intelligent methods that allows automatically obtaining information from event log data and analyzing system processes. This research focuses on the implementation of the process discovery method with control-flow and time pesrpective using Prom software to investigate intelligent prediction of occurrence time of key events in the field of aviation system. In this research, after extracting the initial flight data from Zagros Airlines and converting them to the event log, the flights event log is given to the Prom software and are extracted the transition system model and time prediction of key flight events. Some innovations of this research include using time prediction method of the process mining approach on the actual data, time prediction and analyse the time behavior of the key events of future flights dynamically based on the past behavior of each flight. By using the results of this study, waiting time which prevents from passengers’,congestion is reduced and because of high satisfaction of passengers with up-to-date flights information, demand for aviation system is increased. It also helps to create intelligent scheduling in the aviation system and improves the scheduling of airport facilities.

In highly configurable information systems such as SaaS information systems, business process variability management is an important issue. The variability model, which is often called the configurable process model (CPM), can be reused to configure a family of processes each serving a separate purpose or customer. If not already present, these business process variability models have to be “extracted” based on event logs residing in the databases of the target enterprise(s). Such extraction is costly to carry out manually. In this study, inspired by Software Product Line Engineering concepts, we propose a novel automated process-mining-based method by extending the “Alpha” algorithm for process discovery as a preliminary solution. The proposed method takes a set of event logs as input,and in three phases, outputs a CPM in terms of a model called “BPFM”. To evaluate the method, we used the Goal-Question-Metric approach in a case study on 10 cases. For this purpose, input event logs were artificially extracted from the cases’ existing BPFM models and were fed as input to the proposed method. Then, we observed if the output models of the method were similar to the preliminary existing ones. The results showed that the method was promising in identifying the CPMs,since the extracted models involved activities that were 97. 5% identical to what was expected. Moreover, a structural precision of 98% and a structural recall of 97. 3% were obtained. The set of configurations derivable from the output models was 100% similar to and provided 100% coverage over the expected configurations.

How to profoundly change the beliefs of the polytheists during the age of revelation from the denial of the Prophet (pbuh) to the certainty of his Message and the acceptance of his full guardianship is a question worthy of attention. The order of verses about prophecy based on the order of revelation is the reason to lead a transformative process and a special Quranic method in institutionalizing the belief of prophecy in the lives of Arabs. In the first stage, the Holy Quran in the first Makkī (Arabic: المکّیّ, suras revealed in Mecca) chapters has prepared the community from the motivational side to accept the truth of the Prophet’s (pbuh) prophecy and briefly stating the key issues surrounding it. The Holy Book of Quran at the time of the emergence of oppositions and doubts (the second stage) destroyed the arguments of the polytheists and explained the prophecy and proved its authenticity from a cognitive perspective in the second stage by presenting clear arguments. and the Holy Book of Quran in the third stage after Madanī (Arabic: المدنیّ, suras revealed in Medina) period has undertaken measures in order to fulfill the goals of the Massage by explaining the true position of the Prophet (pbuh) to deepen and operationalize the Prophetic belief through explaining practical duties for the Prophet (pbuh) and behavior based on the acceptance of Walaya (Arabic: ولایة, meaning “guardianship” or “governance”).