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EnglishEspresso quality attracts the interest of many stakeholders: from consumers to local business activities, from coffee-machine vendors to international coffee industries. So far, it has been mostly addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a datadriven analysis exploiting time-series feature engineering.We analyze a real-world dataset of espresso brewing by professional coffee-making machines. The novelty of the proposed work is provided by the focus on the brewing time series, from which we propose to engineer features able to improve previous data-driven metrics determining the quality of the espresso. Thanks to the exploitation of the proposed features, better quality-evaluation predictions are achieved with respect to previous data-driven approaches that relied solely on metrics describing each brewing as a whole (e.g., average flow, total amount of water). Yet, the engineered features are simple to compute and add a very limited workload to the coffee-machine sensor-data collection device, hence being suitable for large-scale IoT installations on-board of professional coffee machines, such as those typically installed in consumer-oriented business activities, shops, and workplaces. To the best of the authors' knowledge, this is the first attempt to perform a data-driven analysis of real-world espresso-brewing time series. Presented results yield to three-fold improvements in classification accuracy of high-quality espresso coffees with respect to current data-driven approaches (from 30% to 100%), exploiting simple threshold-based quality evaluations, defined in the newly proposed feature space.
Evaluating espresso coffee quality by means of time-series feature engineering / Apiletti, D.; Pastor, E.; Callà, R.; Baralis, E.. - ELETTRONICO. - 2578(2020). ((Intervento presentato al convegno Workshops of the 23rd International Conference on Extending Database Technology/23rd International Conference on Database Theory, EDBT-ICDT-WS 2020 tenutosi a dnk nel 2020.
| Titolo: | Evaluating espresso coffee quality by means of time-series feature engineering |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Serie: | |
| Abstract: | Espresso quality attracts the interest of many stakeholders: from consumers to local business act...ivities, from coffee-machine vendors to international coffee industries. So far, it has been mostly addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a datadriven analysis exploiting time-series feature engineering.We analyze a real-world dataset of espresso brewing by professional coffee-making machines. The novelty of the proposed work is provided by the focus on the brewing time series, from which we propose to engineer features able to improve previous data-driven metrics determining the quality of the espresso. Thanks to the exploitation of the proposed features, better quality-evaluation predictions are achieved with respect to previous data-driven approaches that relied solely on metrics describing each brewing as a whole (e.g., average flow, total amount of water). Yet, the engineered features are simple to compute and add a very limited workload to the coffee-machine sensor-data collection device, hence being suitable for large-scale IoT installations on-board of professional coffee machines, such as those typically installed in consumer-oriented business activities, shops, and workplaces. To the best of the authors' knowledge, this is the first attempt to perform a data-driven analysis of real-world espresso-brewing time series. Presented results yield to three-fold improvements in classification accuracy of high-quality espresso coffees with respect to current data-driven approaches (from 30% to 100%), exploiting simple threshold-based quality evaluations, defined in the newly proposed feature space. |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2845112
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