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Text mining and text analytics are broad umbrella terms describing a range of technologies and methodologies for analyzing and processing semi-structured and unstructured text data. The unifying theme behind each of them is the need to “turn text into numbers” so powerful algorithms can be applied to large document databases. Converting text into a structured, numerical format and applying analytical algorithms require knowing how to both use and combine techniques for handling text, ranging from individual words to documents to entire document databases. More in details, we are applying such complex methodologies to issues like predicting maintenance. Predictive maintenance can prevent system failures of all machines and hence guarantee an uninterrupted operation. Up until now, predictive maintenance was only related to collecting and analyzing sensor and machine data. Maintenance protocols, however, can also contain valuable information in free text fields. Through text mining methods these data can be automatically evaluated and analyzed as well, improving the predictive power of analytical models.
The specifics of what the future world of work will look like are still unclear. Since the beginning of the last century, we have been facing a fundamental transformation of production methods. The fast-growing interconnectedness and rise in cooperation between man and machine is changing the way we produce things. As a result, new work preferences are emerging, and demand for products and services is also changing. In our laboratory, we are investigating what effects these developments will have on the organization of work and social evolution.
Scientific Coordinators: Prof. Alberto Petroni, Prof. Barbara Bigliardi.