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Context-based similarity measure on human behavior pattern analysis

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Abstract

Similarity measures for analyzing human behavior patterns are inseparable part of the intelligent environment, with the assistive functionality as its core value. The measure must represent the contexts properly which characterizes the users’ environment. Recent studies attempted to formulate similarity measures for the intelligent environment by incorporating relevant contexts. Yet, they are lacking the integration of multiple inter-related important contexts, which leads to model underestimation and possibly the wrong interpretation. This work proposes a context-based similarity measure for analyzing human behavior patterns. The proposed similarity measure extends and combines the commonly used contexts (i.e., activity, location, and time) into a holistic measure. To avoid the biased representation of activity context similarity, we add one more aspect, namely process context, which describes a wide range of interval relations among the activities of a user. The proposed approach is compared with state-of-the art similarity measures by evaluating both real and simulated data. The result shows that our approach yields the better result in terms of robustness toward noises. In addition, our approach also shows a better reliability compared to previous works in the case of anomaly detection.

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References

  • Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  • Albawendi S, Appiah K, Powell H, Lotf A (2015) Overview of behavioural understanding system with filtered vision sensor. In: 2015 International conference on interactive technologies and games (iTAG). IEEE, pp 90–95

  • Allen J (1983) Maintaining knowledge about temporal intervals. In: Readings in qualitative reasoning about physical systems. Elsevier, pp 361–372

  • Anderson DT, Ros M, Keller JM, Cuéllar MP, Popescu M, Delgado M, Vila A (2012) Similarity measure for anomaly detection and comparing human behaviors. Int J Intell Syst 27(8):733–756

    Article  Google Scholar 

  • Augusto JC, Callaghan V, Cook D, Kameas A, Satoh I (2013) Intelligent environments: a manifesto. Hum Centric Comput Inf Sci 3(1):12

    Article  Google Scholar 

  • Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2(4):263–277

    Article  Google Scholar 

  • Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distribution. Bull Calcutta Math Soc 35:99–109

  • Blanke U, Schiele B (2009) Daily routine recognition through activity spotting. In: Location and context awareness, pp 192–206

  • Blondel VD, Gajardo A, Heymans M, Senellart P, Van Dooren P (2004) A measure of similarity between graph vertices: applications to synonym extraction and web searching. SIAM Rev 46(4):647–666

    Article  MathSciNet  MATH  Google Scholar 

  • Boratto L, Carta S (2010) State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Soro A, Vargiu E, Armano G, Paddeu, G (eds) Information retrieval and mining in distributed environments. Springer, pp 1–20

  • Brdiczka O, Langet M, Maisonnasse J, Crowley JL (2009) Detecting human behavior models from multimodal observation in a smart home. IEEE Trans Autom Sci Eng 6(4):588–597

    Article  Google Scholar 

  • Brent RP (1971) An algorithm with guaranteed convergence for finding a zero of a function. Comput J 14(4):422–425

    Article  MathSciNet  MATH  Google Scholar 

  • Chen YC, Peng WC, Lee SY (2015) Mining temporal patterns in time interval-based data. IEEE Trans Knowl Data Eng 27(12):3318–3331

    Article  Google Scholar 

  • Dijkman R, Dumas M, García-Bañuelos L (2009) Graph matching algorithms for business process model similarity search. In: International conference on business process management. Springer, pp 48–63

  • Ducatel K, Bogdanowicz M, Scapolo F, Leijten J, Burgelman JC (2001) Scenarios for ambient intelligence in 2010. Office for official publications of the European Communities Luxembourg

  • Gallagher B (2006) Matching structure and semantics: a survey on graph-based pattern matching. AAAI FS 6:45–53

    Google Scholar 

  • Guy I, Jacovi M, Perer A, Ronen I, Uziel E (2010) Same places, same things, same people?: mining user similarity on social media. In: Proceedings of the 2010 ACM conference on Computer supported cooperative work. ACM, pp 41–50

  • Hickson M, Kargakis Y, Tzitzikas Y (2011) Similarity-based browsing over linked open data. arXiv preprint arXiv:1106.4176

  • Jiang L, Yang CC (2015) Determining user similarity in healthcare social media using content similarity and structural similarity. In: Conference on artificial intelligence in medicine in Europe. Springer, pp 216–226

  • Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA (2004) A survey method for characterizing daily life experience: the day reconstruction method. Science 306(5702):1776–1780

    Article  Google Scholar 

  • Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, p 34

  • Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  • Lv M, Chen L, Chen G (2013) Mining user similarity based on routine activities. Inf Sci 236:17–32

    Article  Google Scholar 

  • Mafrur R, Nugraha IGD, Choi D (2015) Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose. Hum Centric Comput Inf Sci 5(1):1

    Article  Google Scholar 

  • Passant A (2010) Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI spring symposium: linked data meets artificial intelligence, vol 77, p 123

  • Patel D, Hsu W, Lee ML (2008) Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 393–404

  • Rashidi P, Cook DJ (2010) Mining and monitoring patterns of daily routines for assisted living in real world settings. In: Proceedings of the 1st ACM international health informatics symposium. ACM, pp 336–345

  • Roddick JF, Hornsby K, de Vries D (2003) A unifying semantic distance model for determining the similarity of attribute values. In: Proceedings of the 26th Australasian computer science conference, vol 16. Australian Computer Society, Inc., pp 111–118

  • Smirnov A, Shilov N, Kashevnik A, Teslya N (2012) Smart logistic service for dynamic ridesharing. In: Internet of things, smart spaces, and next generation networking, pp 140–151

  • Sztyler T, Carmona J, Völker J, Stuckenschmidt H (2016) Self-tracking reloaded: applying process mining to personalized health care from labeled sensor data. In: Transactions on petri nets and other models of concurrency XI. Springer, pp 160–180

  • Troyer AK (2011) Activities of daily living (adl). In: Encyclopedia of clinical neuropsychology. Springer, pp 28–30

  • Van Dongen BF, de Medeiros AKA, Verbeek H, Weijters A, Van Der Aalst WM (2005) The prom framework: a new era in process mining tool support. In: International conference on application and theory of petri nets. Springer, pp 444–454

  • Weijters A, van Der Aalst WM, De Medeiros AA (2006) Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven Tech Rep WP 166:1–34

    Google Scholar 

  • Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern Syst 45(1):129–142

    Article  Google Scholar 

  • Ying JJC, Lu EHC, Lee WC, Weng TC, Tseng VS (2010) Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks. ACM, pp 19–26

  • Zager LA, Verghese GC (2008) Graph similarity scoring and matching. Appl Math Lett 21(1):86–94

    Article  MathSciNet  MATH  Google Scholar 

  • Zhong J, Zhu H, Li J, Yu Y (2002) Conceptual graph matching for semantic search. In: International conference on conceptual structures. Springer, pp 92–106

  • Zhu G, Iglesias CA (2017) Computing semantic similarity of concepts in knowledge graphs. IEEE Trans Knowl Data Eng 29(1):72–85

    Article  Google Scholar 

  • Zuo Z, Huang HH, Kawagoe K (2015) Evaluation of a similarity search method for human behavior (extended LDSD). In: The international multiconference of engineers and computer scientists, pp 96–101

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Acknowledgements

This research was supported by Hankuk University of Foreign Studies Research Fund, and also by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the ministry of Education (2015R1D1A1A01061402)

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Authors and Affiliations

  1. Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, 81 Oedae-ro, Mohyeon-eup, Cheoin-gu, Yongin-si, Gyeonggi-do, South Korea

    Aria Ghora Prabono, Seok-Lyong Lee & Bernardo Nugroho Yahya

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  1. Aria Ghora Prabono
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  2. Seok-Lyong Lee
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  3. Bernardo Nugroho Yahya
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Correspondence to Seok-Lyong Lee or Bernardo Nugroho Yahya.

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Prabono, A.G., Lee, SL. & Yahya, B.N. Context-based similarity measure on human behavior pattern analysis. Soft Comput 23, 5455–5467 (2019). https://doi.org/10.1007/s00500-018-3198-6

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