Visual process maps: a visualization tool for discovering habits in smart homes

  • Francesco Leotta
  • Massimo MecellaEmail author
  • Daniele Sora
Original Research


Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. The visual analysis by domain experts allows to identify stages of human habits that could be automatized or simplified by redesigning the environment. In this paper, we present a visual analysis pipeline for graphically visualizing human habits, starting from the sensor log of a smart space,. We apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed method is employed to automatically extract models to be reused for ambient intelligence. A user evaluation demonstrates the effectiveness of the approach, and compares it with respect to a relevant state-of-the-art visual tool, namely Situvis.


Visual process maps Habit mining Habit visualization 



Results in this paper have been obtained with an academic license of Disco freely provided by Fluxicon. The work of Daniele Sora has been partly supported by the Lazio regional project SAPERI & Co (FILAS-RU-2014-1113), the work of Francesco Leotta has been partly supported by the Lazio regional project Sapientia (FILAS-RU-2014-1186), all the authors have been also partly supported by Italian project Social Museum e Smart Tourism (CTN01-00034-23154), Italian project NEPTIS (PON03PE-00214-3) and Italian project RoMA–Resilence of Metropolitan Areas (SCN-00064).


  1. Augusto JC, Nugent CD (2004) The use of temporal reasoning and management of complex events in smart homes. In: Proceedings of the 16th European conference on artificial intelligence, ECAI’04. IOS Press, Amsterdam, pp 778–782.
  2. Augusto JC, Liu J, McCullagh P, Wang H, Yang JB (2008) Management of uncertainty and spatio-temporal aspects for monitoring and diagnosis in a smart home. Int J Comput Intell Syst 1(4):361–378. CrossRefGoogle Scholar
  3. Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P (2010) Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Architecture of computing systems (ARCS), 23rd international conference. Springer, Berlin, pp 1–10Google Scholar
  4. Aztiria A, Augusto JC, Basagoiti R, Izaguirre A, Cook DJ (2012) Discovering frequent user-environment interactions in intelligent environments. Pers Ubiquitous Comput 16(1):91–103. CrossRefGoogle Scholar
  5. Aztiria A, Izaguirre A, Basagoiti R, Augusto JC, Cook D (2010) Automatic modeling of frequent user behaviours in intelligent environments. In: Intelligent environments (IE), 2010 sixth international conference. IEEE, pp 7–12.
  6. Aztiria A, Izaguirre A, Basagoiti R, Augusto JC, Cook DJ (2009) Discovering frequent sets of actions in intelligent environments. In: Intelligent environments. iOS Press, Amsterdam, pp 153–160.
  7. Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervasive Mob Comput 6(2):161–180. CrossRefGoogle Scholar
  8. Chen H, Finin T, Joshi A (2003) An ontology for context-aware pervasive computing environments. Knowl Eng Rev 18(03):197–207. CrossRefGoogle Scholar
  9. Clear AK, Holland T, Dobson S, Quigley A, Shannon R, Nixon P (2010) Situvis: a sensor data analysis and abstraction tool for pervasive computing systems. Pervasive Mob Comput 6(5):575–589. CrossRefGoogle Scholar
  10. Cook DJ (2012) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 27(1):32–38CrossRefGoogle Scholar
  11. Dumas M, La Rosa M, Mendling J, Reijers HA (2018) Fundamentals of business process management, 2nd edn. Springer, Berlin HeidelbergGoogle Scholar
  12. García-Bañuelos L, Dumas M, La Rosa M, De Weerdt J, Ekanayake CC (2014) Controlled automated discovery of collections of business process models. Inf Syst 46:85–101. CrossRefGoogle Scholar
  13. Gini C (1921) Measurement of inequality of incomes. Econ J 31(121):124–126. CrossRefGoogle Scholar
  14. Gottfried B, Guesgen H, Hubner S (2006) Spatiotemporal reasoning for smart homes. In: Augusto J, Nugent C (eds) Designing smart homes, lecture notes in computer science, vol 4008. Springer, Berlin, pp 16–34Google Scholar
  15. Gu T, Pung HK, Zhang DQ (2005) A service-oriented middleware for building context-aware services. J Netw Comput Appl 28(1):1–18. CrossRefGoogle Scholar
  16. Günther CW, Van Der Aalst WM (2007) Fuzzy mining–adaptive process simplification based on multi-perspective metrics. In: Business process management. Springer, Berlin Heidelberg, pp 328–343.
  17. Hagras H (2018) Toward human-understandable, explainable AI. IEEE Comput 51(9):28–36. CrossRefGoogle Scholar
  18. Helaoui R, Riboni D, Stuckenschmidt H (2013) A probabilistic ontological framework for the recognition of multilevel human activities. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 345–354.
  19. Henricksen K, Indulska J (2006) Developing context-aware pervasive computing applications: models and approach. Pervasive Mob Comput 2(1):37–64. CrossRefGoogle Scholar
  20. Jorbina K, Rozumnyi A, Verenich I, Di Francescomarino C, Dumas M, Ghidini C, Maggi FM, La Rosa M, Raboczi S (2017) Nirdizati: a web-based tool for predictive process monitoring. In: Proceedings of the BPM demo track with 15th international conference on business process modeling (BPM 2017), Barcelona, Spain, September 13, 2017.
  21. Lee JG, Han J, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, pp 593–604.
  22. Leotta F, Mecella M, Mendling J (2015) Applying process mining to smart spaces: perspectives and research challenges. In: Advanced information systems workshops. Springer, Cham, pp 298–304.
  23. Loke SW (2004) Logic programming for context-aware pervasive computing: language support, characterizing situations, and integration with the web. In: Proceedings of the 2004 IEEE/WIC/ACM international conference on web intelligence, pp 44–50.
  24. Loke SW (2010) Incremental awareness and compositionality: a design philosophy for context-aware pervasive systems. Pervasive Mob Comput 6(2):239–253. CrossRefGoogle Scholar
  25. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990. CrossRefGoogle Scholar
  26. Ranganathan A, McGrath R, Campbell R, Mickunas M (2003) Use of ontologies in a pervasive computing environment. Knowl Eng Rev 18(03):209–220. CrossRefGoogle Scholar
  27. Rashidi P, Cook DJ (2013) Com: a method for mining and monitoring human activity patterns in home-based health monitoring systems. ACM Trans Intell Syst Technol (TIST) 4(4):64. Google Scholar
  28. Riboni D, Bettini C (2009) Context-aware activity recognition through a combination of ontological and statistical reasoning. In: Ubiquitous intelligence and computing, pp 39–53.
  29. Riboni D, Sztyler T, Civitarese G, Stuckenschmidt H (2016) Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, New York, pp 1–12.
  30. Russell SJ, Norvig P (2010) Artificial intelligence—a modern approach (3. internat. ed.). Pearson Education, Upper Saddle River, NJ, USAGoogle Scholar
  31. Tax N, Alasgarov E, Sidorova N, Haakma R (2016a) On generation of time-based label refinements. In: Proceedings of the 25th international workshop on concurrency, specification and programming, Rostock, Germany, September 28–30, 2016,, CEUR Workshop Proceedings, vol 1698, pp 25–36.
  32. Tax N, Sidorova N, Haakma R, van der Aalst WM (2016b) Event abstraction for process mining using supervised learning techniques. In: Proceedings of SAI intelligent systems conference. Springer, Cham, pp 251–269.
  33. Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: Advanced information systems engineering---29th international conference, CAiSE 2017, Essen, Germany, June 12–16, 2017, proceedings, pp 477–492.
  34. van der Aalst WM (2016) Process mining. Data science in action. Springer, Berlin HeidelbergCrossRefGoogle Scholar
  35. Ye J, Coyle L, Dobson S, Nixon P (2007) Ontology-based models in pervasive computing systems. Knowl Eng Rev 22(4):315–347. CrossRefGoogle Scholar
  36. Ye J, Dobson S, McKeever S (2012) Situation identification techniques in pervasive computing: a review. Pervasive Mob Comput 8(1):36–66. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio RubertiSapienza Università di RomaRomeItaly

Personalised recommendations