Abstract Sequential rule mining is one of the most common data mining techniques. It intends to find desired rules in large sequence databases. It can decide the essential information that helps acquire knowledge from large search spaces and select curiously rules from sequence databases. The key challenge is to avoid wasting time, which is particularly difficult in large sequence databases. This paper studies the mining rules from two representations of sequential patterns to have compact databases without affecting the final result. In addition, execute a parallel approach by utilizing multi core processor architecture for mining non-redundant sequential rules. Also, perform pruning techniques to enhance the efficiency of the generated rules. The evaluation of the proposed algorithm was accomplished by comparing it with another non-redundant sequential rule algorithm called Non-Redundant with Dynamic Bit Vector (NRD-DBV). Both algorithms were performed on four real datasets with different characteristics. Our experiments show the performance of the

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[35] Zihayat M., Hut Z., An A., and Hut Y., “Distributed and Parallel High Utility Sequential Pattern Mining,” in Proceedings of IEEE International Conference on Big Data, Washington, pp. 853-862, 2016. Nesma Youssef has received her master’s degree from Sadat Academy for management science, department of an information system, Cairo, Egypt, 2014. The equivalency score was obtained with computers and information at the Supreme Council of Universities. Currently, she is working as an assistant teacher in the information system department at Sadat Academy for management science, Cairo, Egypt. She has 10 years of teaching experience. She is a research Scholar in the Information System department at the Faculty of Computers and Information, Menoufia University, Egypt. Hatem Abdulkadar has obtained his BS and M.SC., both in electrical engineering from the Alexandria University, faculty of Engineering, 1990 and 1995, respectively. He obtained his Ph.D. in electrical engineering also from faulty of engineering, Alexandria University, Egypt 2001. His area of interest is data security, web applications, artificial intelligence, and he is specialized neural networks. He is currently a professor in the information system department, faculty of computers and information, Menoufia University, Egypt. Amira Abdelwahab has received BSc degree in computer science and information systems from Faculty of Computers and Information, Helwan University, Egypt in 2000, and Ph.D. in information systems from Chiba University, Japan in 2012. In 2013, she was a postdoctoral fellow in Chiba University, Japan. from 2012 till 2018, she has been an assistant professor in information systems department, Faculty of Computers and Information, Menoufia University, Egypt. Since 2018, she has been an assistant professor in information systems department, college of Computer science and information technology, King Faisal University. Her research interests include Software Engineering, Decision Support System, database Systems, Data Mining, Machine Learning, Recommendation Systems, Web intelligence, and Big Data analytics.