CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories
Context-aware applications adapt their functionalities based on users contexts. Complementarily, a context history has information about previous contexts visited by a user. Context history enables applications to explore users past behavior. Researchers have studied different ways to analyze these...
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| Vydané v: | Pattern analysis and applications : PAA Ročník 23; číslo 2; s. 725 - 734 |
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| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.05.2020
Springer Nature B.V |
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| ISSN: | 1433-7541, 1433-755X |
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| Abstract | Context-aware applications adapt their functionalities based on users contexts. Complementarily, a context history has information about previous contexts visited by a user. Context history enables applications to explore users past behavior. Researchers have studied different ways to analyze these data. This article addresses a specific type of data analysis in contexts histories, which is the discovery and monitoring of sequential patterns. The article proposes a model, called CHSPAM, that allows the discovery of sequential patterns in contexts histories databases and keeps track of these patterns to monitor their evolution over time. There are two main contributions of this work. The first one is the use of a generic representation for stored context information on pattern recognition field, which enables the model to be used for different research domains. The second contribution is the fact that CHSPAM monitors discovered pattern evolution over time. We have build a functional prototype that allowed us to conduct experiments in two different applications. The first experiment used the model to perform pattern analysis and evaluate the prediction based on monitored sequential patterns. Prediction accuracy increased by up to 17% when compared to the use of common sequential patterns. On the second experiment, CHSPAM was used as a component of a learning object recommendation application. The application was able to recommend learning objects related to students interests based on monitored sequential patterns extracted from users session history. Usefulness for recommendations reached 84%. |
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| AbstractList | Context-aware applications adapt their functionalities based on users contexts. Complementarily, a context history has information about previous contexts visited by a user. Context history enables applications to explore users past behavior. Researchers have studied different ways to analyze these data. This article addresses a specific type of data analysis in contexts histories, which is the discovery and monitoring of sequential patterns. The article proposes a model, called CHSPAM, that allows the discovery of sequential patterns in contexts histories databases and keeps track of these patterns to monitor their evolution over time. There are two main contributions of this work. The first one is the use of a generic representation for stored context information on pattern recognition field, which enables the model to be used for different research domains. The second contribution is the fact that CHSPAM monitors discovered pattern evolution over time. We have build a functional prototype that allowed us to conduct experiments in two different applications. The first experiment used the model to perform pattern analysis and evaluate the prediction based on monitored sequential patterns. Prediction accuracy increased by up to 17% when compared to the use of common sequential patterns. On the second experiment, CHSPAM was used as a component of a learning object recommendation application. The application was able to recommend learning objects related to students interests based on monitored sequential patterns extracted from users session history. Usefulness for recommendations reached 84%. |
| Author | Dupont, Daniel Alves, Bruno Mota Barbosa, Jorge Luis Victória |
| Author_xml | – sequence: 1 givenname: Daniel surname: Dupont fullname: Dupont, Daniel organization: University of Vale do Rio dos Sinos (UNISINOS) – sequence: 2 givenname: Jorge Luis Victória surname: Barbosa fullname: Barbosa, Jorge Luis Victória organization: University of Vale do Rio dos Sinos (UNISINOS) – sequence: 3 givenname: Bruno Mota surname: Alves fullname: Alves, Bruno Mota email: motaalvesb@gmail.com organization: University of Vale do Rio dos Sinos (UNISINOS) |
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| Cites_doi | 10.1016/j.compeleceng.2014.04.006 10.1109/TKDE.2004.77 10.1016/j.eswa.2008.09.002 10.1109/MNET.2008.4579768 10.1007/s007790170019 10.1016/j.eswa.2015.09.016 10.1109/TII.2015.2475218 10.1007/s10586-014-0370-3 10.1038/scientificamerican0991-94 10.1109/TNNLS.2018.2797060 10.1016/j.compind.2016.04.006 10.1109/SURV.2013.042313.00197 10.3390/s130202682 10.1145/159544.159617 10.1145/1409635.1409638 10.1145/1864349.1864393 10.3390/s130505460 10.1145/72551.72554 10.1007/s00779-014-0796-x 10.1109/COMST.2014.2381246 10.1109/98.943998 10.1145/342009.335372 10.1016/j.ins.2014.06.026 10.1007/s40593-015-0047-y 10.1145/170036.170072 |
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| Keywords | Ubiquitous computing Context histories Pattern discovery Data mining |
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LeeSCPaikJOkJSongIKimUMEfficient mining of user behaviors by temporal mobile access patternsInt J Comput Sci Secur20077285291 BarbosaJLVMartinsCFrancoLKBarbosaDNFTrailtrade: a model for trail-aware commerce supportComput Ind201680435310.1016/j.compind.2016.04.006 ZhangHJiYLiJYeYA triple wing harmonium model for movie recommendationIEEE Trans Ind Inf201512231239 PereraCZaslavskyAChristenPGeorgakopoulosDContext aware computing for the internet of things: a surveyIEEE Commun Surv Tutor201416141445410.1109/SURV.2013.042313.00197 HongJSuhEHKimJKimSContext-aware system for proactive personalized service based on context historyExpert Syst Appl2009367448745710.1016/j.eswa.2008.09.002 ChikhaouiBWangSXiongTPigotHPattern-based causal relationships discovery from event sequences for modeling behavioral user profile in ubiquitous environmentsInf Sci201428520422210.1016/j.ins.2014.06.026 Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. 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| References_xml | – reference: PereraCZaslavskyAChristenPGeorgakopoulosDContext aware computing for the internet of things: a surveyIEEE Commun Surv Tutor201416141445410.1109/SURV.2013.042313.00197 – reference: DeyAKUnderstanding and using contextPersonal Ubiquitous Comput200154710.1007/s007790170019 – reference: ChodorowKMongoDB: the definitive guide2013SebastopolO’Reilly Media Inc. – reference: HudakPConception, evolution, and application of functional programming languagesACM Comput Surv19892135941110.1145/72551.72554 – reference: WeiserMSome computer science issues in ubiquitous computingCommun ACM199336758410.1145/159544.159617 – reference: PeiJHanJMortazavi-AslBWangJPintoHChenQDayalUHsuMCMining sequential patterns by pattern-growth: the prefixspan approachIEEE Trans Knowl Data Eng200416111424144010.1109/TKDE.2004.77 – reference: BarbosaJLVMartinsCFrancoLKBarbosaDNFTrailtrade: a model for trail-aware commerce supportComput Ind201680435310.1016/j.compind.2016.04.006 – reference: ChikhaouiBWangSXiongTPigotHPattern-based causal relationships discovery from event sequences for modeling behavioral user profile in ubiquitous environmentsInf Sci201428520422210.1016/j.ins.2014.06.026 – reference: YürürÖLiuCHShengZLeungVCMorenoWLeungKKContext-awareness for mobile sensing: a survey and future directionsIEEE Commun Surv Tutor2014181689310.1109/COMST.2014.2381246 – reference: MoorePHuBWanJCaballeSIntelligent context for personalised mobile learningArchitectures for distributed and complex M-learning systems2009New YorkApplying Intelligent Technologies236270 – reference: HongJSuhEHKimJKimSContext-aware system for proactive personalized service based on context historyExpert Syst Appl2009367448745710.1016/j.eswa.2008.09.002 – reference: RosaJHBarbosaJLKichMBritoLA multi-temporal context-aware system for competences managementInt J Artif Intell Educ20152545549210.1007/s40593-015-0047-y – reference: ZhangHWangSXuXChowTWSWuQMJTree2vector: learning a vectorial representation for tree-structured dataIEEE Trans Neural Netw Lean Syst20182953045318386784610.1109/TNNLS.2018.2797060 – reference: Hadley M, Sandoz P (2009) Jax-rs: JavaTM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{{\rm TM}}$$\end{document} api for restful web services. In: Java specification request (JSR), p 311 – reference: da RosaJHBarbosaJLRibeiroGDOracon: an adaptive model for context predictionExpert Syst Appl201645567010.1016/j.eswa.2015.09.016 – reference: YinJTianGFengZLiJHuman activity recognition based on multiple order temporal informationComput Electric Eng20144051538155110.1016/j.compeleceng.2014.04.006 – reference: WhiteTHadoop: the definitive guide2012SebastopolO’Reilly Media Inc. – reference: OrdóñezFJde ToledoPSanchisAActivity recognition using hybrid generative/discriminative models on home environments using binary sensorsSensors20131355460547710.3390/s130505460 – reference: ZhangHJiYLiJYeYA triple wing harmonium model for movie recommendationIEEE Trans Ind Inf201512231239 – reference: LeeSCPaikJOkJSongIKimUMEfficient mining of user behaviors by temporal mobile access patternsInt J Comput Sci Secur20077285291 – reference: Huynh T, Fritz M, Schiele B (2008) Discovery of activity patterns using topic models. In: Proceedings of the 10th international conference on ubiquitous computing, pp 10–19 – reference: FatimaIFahimMLeeYKLeeSA unified framework for activity recognition-based behavior analysis and action prediction in smart homesSensors20131322682269910.3390/s130202682 – reference: WoodADStankovicJAVironeGSelavoLHeZCaoQDoanTWuYFangLStoleruRContext-aware wireless sensor networks for assisted living and residential monitoringIEEE Netw200822263310.1109/MNET.2008.4579768 – reference: Nurmi P, Martin M, Flanagan JA (2005) Enabling proactiveness through context prediction. 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