Abstract
To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants’ context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants’ context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.
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References
Awad A, Fayek AR (2012) Adaptive learning of contractor default prediction model for surety bonding. J Constr Eng Manag 139(5):694–704
Ball D, Coelho PS, Vilares MJ (2006) Service personalization and loyalty. J Serv Mark 20(5):391–403
Bainbridge WS (2004) Berkshire encyclopedia of human-computer interaction, vol 2. Berkshire Publishing Group LLC, Great Barrington
Bashon Y, Neagu D, Ridley JR (2013) A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes. Soft Comput 17(9):1595–1615
Bregman D (2010) Smart home intelligence-the eHome that learns. Int J Smart Home 4(3):35–46
Byrne MM, Daw CN, Nelson HA, Urech TH, Pietz K, Peterson LA (2009) Method to develop health case peer groups for quality and financial comparison across hospitals. Health Serv Res 44(2):577–592
Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl 33(1):230–240
Chang PC, Liu CH, Lai RK (2008) A fuzzy case-based reasoning model for sales forecasting in print circuit board industries. Expert Syst Appl 34(3):2049–2058
Choy KL, Chow KH, Moon KL, Zeng X, Lau HCW, Chan FTS, Ho GTS (2009) A RFID-case-based sample management system for fashion product development. Eng Appl Artif Intell 22(5):882–896
Dabrowski M, Gromada J, Moustafa H, Forestier J (2013) A context-aware architecture for IPTV services personalization. J Internet Serv Inf Secur (JISIS) 3(1/2):49–70
Darmoul S, Pierreval H, Hajri-Gabouj S (2011) Using ontologies to capture and structure knowledge about disruptions in manufacturing systems: an immune driven approach. In: 2011 IEEE 16th conference on emerging technologies and factory automation (ETFA), pp 1–7
Das S (2013) Technology for SMART HOME. In: Proceedings of international conference on VLSI, communication, advanced devices, signals & systems and networking (VCASAN-2013). Springer India, pp 7–12
Fornells A, Golobardes E, Vernet D, Corral G (2006) Unsupervised case memory organization: Analysing computational time and soft computing capabilities. In: Advances in Case-Based Reasoning. Springer, Berlin, pp 241–255
Gaedke M, Grossniklaus M, Díaz O (eds) (2009) Web Engineering: 9th International Conference, ICWE 2009 San Sebastián, Spain, June 24–26 2009 Proceedings, vol 5648. Springer, Berlin
García-Crespo Á, López-Cuadrado JL, González-Carrasco I, Colomo-Palacios R, Ruiz-Mezcua B (2012) SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations. Knowl Based Syst 27:103–118
Göker AS, Myrhaug HI (2002) User context and personalisation. In: Workshop proceedings for the 6th European Conference on Case Based Reasoning
Hudson DL, Cohen ME, Deedwania PC (1995) Hybrid system for diagnosis and treatment of heart disease. In: Cohen ME, Hudson DL (eds) Comparative approaches to medical reasoning. World Scientific, Singapore, pp 289–310
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans Autom Control 42(10):1482–1484
Kadouche R, Abdulrazak B, Giroux S, Mokhtari M (2009) Disability centered approach in smart space management. Int J Smart Home 3(2):13–26
Lee CS, Wang MH, Hagras H (2010) A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Trans Fuzzy Syst 18(2):374–395
Lee H, Park SJ, Kim MJ, Jung JY, Lim HW, Kim JT (2013) The service pattern-oriented smart bedroom based on elderly spatial behavior patterns. Indoor Built Environ 22(1):299–308
Lin CT, Chiu H, Tseng YH (2006) Agility evaluation using fuzzy logic. Int J Prod Econ 101(2):353–368
Makonin S, Bartram L, Popowich F (2013) A smarter smart home: case studies of ambient intelligence. Pervasive Comput 12(1):1536–1568
Makrehchi M, Basir O, Kamel M (2003) Generation of fuzzy membership function using information theory measures and genetic algorithm. Fuzzy Sets and Systems-IFSA 2003. Springer, Berlin, pp 603–610
Martin S, Kelly G, Kernohan WG, McCreight B, Nugent C (2009) Smart home technologies for health and social care support (Review). Wiley, New York
Marsá-Maestre I, López-Carmona MA, Velasco JR, Navarro A (2008) Mobile agents for service personalization in smart environments. J Netw 3(4):30–41
Mooi E, Sarstedt M (2011) A concise guide to market research: the process, data, and methods using IBM SPSS statistics. Springer, Berlin
Navale RL, Nelson RM (2010) Use of evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil. Energy Build 42(11):2213–2218
Ogiela MR, Ogiela L (2012) Personal identification based on cognitive analysis of selected medical visualization. J Internet Serv Inf Secur (JISIS) 2(3/4):148–153
Olson CF (1995) Parallel algorithms for hierarchical clustering. Parallel Comput 21(8):1313–1325
Park GK, Benedictos JLR, Lee CS, Wang MH (2007) Ontology-based fuzzy-CBR Support System for ship’s collision avoidance. In: 2007 International conference on machine learning and cybernetics, vol 4. IEEE, New York, pp 1845–1850
Pedrycz W, Gudwin RR, Gomide FAC (1997) Nonlinear context adaptation in the calibration of fuzzy sets. Fuzzy Sets Syst 88(1):91–97
Qian G, Sural S, Gu Y, Pramanik S (2004) Similarity between Euclidean and cosine angle distance for nearest neighbor queries. In: Proceedings of the 2004 ACM symposium on Applied, computing, pp 1232–1237
Reichle R, Wagner M, Khan MU, Geihs K, Lorenzo J, Valla M, Fra C, Paspallis N, Papadopoulos GA (2008) A comprehensive context modeling framework for pervasive computing systems. In: Distributed applications and interoperable systems. Springer, Berlin
Sander HA, Ghosh D, Riper DV, Manson SM (2010) How do you measure distance in spatial models? An example using open-space valuation. Environ Plan B Plan Des 37(4):874
Sahoo AK, Zuo MJ, Tiwari MK (2012) A data clustering algorithm for stratified data partitioning in artificial neural network. Expert Syst Appl 39(8):7004–7014
Seo J, Choi S, Kim M, Han S (2013) The method of personalized recommendation with ensemble combination. J Wirel Mobile Netw Ubiquitous Comput Dependable Appl 4(3):108–121
Sheu JB (2007) A hybrid fuzzy-optimization approach to customer grouping-based logistics distribution operations. Appl Math Model 31(5):1048–1066
Sohn M, Jeong S, Lee HJ (2013) Self-evolved ontology-based service personalization framework for disabled users in smart home environment. In: 2013 Seventh international conference on innovative mobile and internet services in ubiquitous computing (IMIS). IEEE, New York, pp 238–244
Strang T, Linnhoff-Popien C (2004) A context modeling survey. In: Proceedings of first international workshop on advanced context modelling, reasoning and management at UbiComp 2004
Tzouveli P, Schmidt A, Schneider M, Symvonis A, Kollias S (2008). Adaptive reading assistance for the inclusion of students with dyslexia: the AGENT-DYSL approach. In: Eighth IEEE International Conference on Advanced Learning Technologies, 2008. ICALT’08. IEEE, NewYork, pp 167–171
Vijaya K, Nehemiah HK, Kannan A, Bhuvaneswari NG (2010) Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases. Int J Data Min Model Manag 2(3):388–402
Wang SL, Hsu SH (2004) A Web-based CBR knowledge management system for PC troubleshooting. Int J Adv Manuf Technol 23(7–8):532–540
Xie G, Xiong R, Church I (1998) Comparison of kinetics, neural network and fuzzy logic in modelling texture changes of dry peas in long time cooking. LWT Food Sci Technol 31(6):639–647
Yang Q, Wu J (2001) Enhancing the effectiveness of interactive case-based reasoning with clustering and decision forests. Appl Intell 14(1):49–64
Yue S, Wang P, Wang J, Hiang T (2013) Extension of the gap statistics index to fuzzy clustering. Soft Comput 17(10):1833–1846
Acknowledgments
This research was partially supported by the IT R&D program of MKE/KEIT [No. 10041788, Development of Smart Home Service based on Advanced Context-Awareness] and partially supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “IT Consilience Creative Program” (NIPA-2014-H0201-14-1002) supervised by the NIPA (National IT Industry Promotion Agency).
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Communicated by A. Castiglione.
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Sohn, M., Jeong, S. & Lee, H.J. Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment. Soft Comput 18, 1715–1728 (2014). https://doi.org/10.1007/s00500-014-1288-7
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DOI: https://doi.org/10.1007/s00500-014-1288-7