Massive volumes of diverse data are increasingly being collected, stored and analyzed for improved understanding, prediction and efficiency of complex systems and processes. Data mining of such rich resources has become increasingly important across many areas and disciplines. However, this is particularly true of communication systems and computer networks, where diverse data can be collected, managed, and analyzed within the systems and networks, and the output from data mining can be fed back again to improve performance and facilitate autonomy and self-management of such systems and networks. Here, data mining is concerned with the search for new knowledge in data, usually obtained in the form of rules specified in terms of data mining tasks such as classification, clustering, anomaly detection or prediction. Such functionality is becoming increasingly more important as we move towards 6G, with hugely improved connectivity and the ability to handle large amounts of heterogeneous data. In addition, the burgeoning Internet of Everything (I0E) is extending the Internet and corresponding data to people, devices, data stores, and networks with huge potential for data mining and computational intelligence. Recently Process Mining has emerged as a particularly important topic where computer systems generate rich process data which can be mined, while business processes from large organizations are diverse and complex, with massive opportunities for extracting and using new knowledge and improving understanding. Such smart process analytics allows the users and owners of such data and knowledge to achieve goals, via different mining and analytical techniques, thus facilitating better understanding of such processes and prediction of outcomes of interest. Such approaches can improve understanding of process dynamics and facilitate timely predictions and interventions
The architectures of mobile networks have seen an unprecedented techno-economic
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