Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.

Unsupervised Segmentation of Smart Home Logs for Human Habit Discovery / Esposito, Lucia; Leotta, Francesco; Mecella, Massimo; Veneruso, Silvestro. - (2022), pp. 1-8. (Intervento presentato al convegno The International Conference on Intelligent Environments tenutosi a Biarritz; France) [10.1109/IE54923.2022.9826776].

Unsupervised Segmentation of Smart Home Logs for Human Habit Discovery

Esposito, Lucia
;
Leotta, Francesco
;
Mecella, Massimo
;
Veneruso, Silvestro
2022

Abstract

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.
2022
The International Conference on Intelligent Environments
ambient intelligence; habit mining; unsupervised log segmentation; process mining
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Unsupervised Segmentation of Smart Home Logs for Human Habit Discovery / Esposito, Lucia; Leotta, Francesco; Mecella, Massimo; Veneruso, Silvestro. - (2022), pp. 1-8. (Intervento presentato al convegno The International Conference on Intelligent Environments tenutosi a Biarritz; France) [10.1109/IE54923.2022.9826776].
File allegati a questo prodotto
File Dimensione Formato  
Esposito_Unsupervised_2022.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.36 MB
Formato Adobe PDF
1.36 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1651159
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 1
social impact