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This study analyzes how the Chilean economy has evolved, and, in particular, the importance of the copper mining sector in this process. Input-output modeling is used to measure sensitivities and structural changes in production. The information used comes from the Organisation for Economic Co-operation and Development (oecd) and covers an 11-year horizon (from 2005 to 2015). The study reveals the importance that copper has on the development of the Chilean economy. It arises in the relationship changes in the supply and demand of inputs made by this activity. Additionally, the results show a weakening in the sensitivities regarding the relations of the manufacturing industry. This effect is known in the input-output literature as "hollowing-out". A decay of the complexity of the industry's linkages implies a reduction in the sensitivity of direct, indirect, and total links with the rest of the productive system. It also means increasing dependence on the rest of the world on input providers. [ABSTRACT FROM AUTHOR]

Process mining aims to discovering, monitoring and improving process, starting from events logged during process execution on information systems. Nevertheless, available process mining techniques for process diagnosis offer insufficient information about event log completeness or infrequent process behavior, affecting process comprehension. A new technique, named Variants Miner is presented, starting from limitations of process mining techniques regarding process comprehension. This technique aims to construct different variants of process models through process decomposition on sub-processes. An application of Variants Miner and other techniques in a real environment is presented. The obtained results show that the proposal decreases the cognitive load of the model and improve the fitness, generalization and precision, considering missing information and noise, improving process comprehension on process diagnosis stage. [ABSTRACT FROM AUTHOR]

Nowadays Process Mining and Simulation are recognized like innovative alternatives for business process analysis and improvement. Traditional techniques of process analysis need generally long processing times and are supported on analysts expertise, with the corresponding risk of human error; however, process mining techniques are able to extract knowledge from event logs commonly available in today's information systems through less expensive methods in time and resources. Whereas traditional simulation approaches rely on hand-made models, we advocate the use of process mining techniques for creating more reliable simulation models based on real event data. The present paper shows the results obtained with process mining and simulation on Petri Nets and ARENA in the same process, in order to compare the results obtained by each of them and determine if there are significant differences between them. [ABSTRACT FROM AUTHOR]