{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T02:02:43Z","timestamp":1760061763160,"version":"3.41.2"},"reference-count":45,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2015,7,13]],"date-time":"2015-07-13T00:00:00Z","timestamp":1436745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,7,13]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The outcome of the paper is measured as introducing a novel idea how SDM technique\u2019s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/imds-12-2014-0386","type":"journal-article","created":{"date-parts":[[2015,6,25]],"date-time":"2015-06-25T09:19:32Z","timestamp":1435223972000},"page":"1151-1178","source":"Crossref","is-referenced-by-count":11,"title":["Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques"],"prefix":"10.1108","volume":"115","author":[{"given":"Gebeyehu Belay","family":"Gebremeskel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chai","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengliang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongshi","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122221245035400_b2","unstructured":"Ahmad, L.\n \n et al. (2011), \u201cSmart homes for the elderly dementia sufferers: identification and prediction of abnormal behavior\u201d, \n J Ambient Intell Human Compute\n , Vol. 3 No. 3, pp. 205-218."},{"key":"key2020122221245035400_b1","doi-asserted-by":"crossref","unstructured":"Ahmed, E.\n and \n Nada, E.-G.\n (2013), \u201cTrajectory data mining: integrating semantics\u201d, \n Journal of Enterprise Information Management\n , Vol. 26 No. 5, pp. 516-535.","DOI":"10.1108\/JEIM-07-2013-0038"},{"key":"key2020122221245035400_b3","unstructured":"Ariel, K.\n \n et al. (2012), \u201cThe big data bootstrap\u201d, Proceedings of the 29th International Conference on Machine Learning, arxiv.1206-6415v1, pp. 1759 -1766."},{"key":"key2020122221245035400_b4","doi-asserted-by":"crossref","unstructured":"Bhatia, M.P.S.\n and \n Deepika, K.\n (2013), \u201cExperimental Study of data clustering using k-means and modified algorithms,\u201d \n International Journal of Data Mining & Knowledge Management Process\n , Vol. 3 No. 3, pp. 17-30.","DOI":"10.5121\/ijdkp.2013.3302"},{"key":"key2020122221245035400_b5","unstructured":"Brian, H.R.\n and \n C.J.H., M.\n (2007), \u201cSmart systems and environments\u201d, \n Kybernetes\n , Vol. 36 Nos 3\/4, pp. 456-464."},{"key":"key2020122221245035400_b6","unstructured":"CASAS\n (2010), \u201cSmart home publicly shared datasets\u201d, available at: http:\/\/ailab.wsu.edu\/casas\/datasets.html (accessed 6 November 2012)."},{"key":"key2020122221245035400_b7","unstructured":"Charu, C.A.\n (Ed.) (2013), \n Managing and Mining Sensor Data\n , Springer, New York, NY."},{"key":"key2020122221245035400_b8","unstructured":"Chen, L.\n \n et al. (2005), \u201cRobust and fast similarity search for moving object trajectories\u201d, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 491-502, doi:10:1146\/1066157-1066213."},{"key":"key2020122221245035400_b9","unstructured":"Debraj, D.\n \n et al. (2012), \n ActiSen: Activity-Aware Sensor Network in Smart Environments\n , Elsevier B.V., Pervasive and Mobile Computing, Vol. 8 No. 5, pp. 730-750."},{"key":"key2020122221245035400_b10","unstructured":"Dhaval, P.\n \n et al. (2009), \u201cAnalyzing abnormal events from spatio-temporal trajectories\u201d, IEEE International Conference on Data Mining Workshops, pp. 616-621, 978-0-7695-3902-7\/09, doi10.1109\/ICDMW.45."},{"key":"key2020122221245035400_b11","doi-asserted-by":"crossref","unstructured":"Diane, J.C.\n and \n Lawrence, B.H.\n (2011), \u201cSensor selection to support practical use of health-monitoring smart environments\u201d, \n WIREs Data Mining and Knowledge Discovery\n , Vol. 1 No. 4, pp. 339-351.","DOI":"10.1002\/widm.20"},{"key":"key2020122221245035400_b12","unstructured":"Dong, Z.\n \n et al. (2005), \u201cSemi-supervised adapted HMMs for unusual event detection\u201d, Proceedings of the 2005 IEEE Computer Society Conference on CVPR, No. 10636919\/ 05, pp. 611-618v1, doi:10:1109\/CVPR.2005.316."},{"key":"key2020122221245035400_b13","unstructured":"Elad, Y.-T.\n (2004), \u201cAn introduction to pattern classification\u201d, \n Machine Learning 2003, LNAI 3176\n , Springer-Verlag Berlin, Heidelberg, pp. 1-20, doi:10:1007\/978-3-540-26550-9-1."},{"key":"key2020122221245035400_b14","doi-asserted-by":"crossref","unstructured":"Erfaneh, A.\n , \n Mohammad, A.\n and \n Heidari, J.\n \n et al. (2012), \u201cThe role of smart home in smart real estate\u201d, \n Journal of European Real Estate Research\n , Vol. 5 No. 2 pp. 156-170.","DOI":"10.1108\/17539261211250726"},{"key":"key2020122221245035400_b16","doi-asserted-by":"crossref","unstructured":"Fan, J.\n \n et al. (2009), \u201cA dynamic hierarchical clustering method for trajectory-based unusual video event detection\u201d, \n IEEE Transactions on Image Processing\n , Vol. 18 No. 4, pp. 907-913.","DOI":"10.1109\/TIP.2008.2012070"},{"key":"key2020122221245035400_b15","doi-asserted-by":"crossref","unstructured":"Fan, J.\n \n et al. (2011), \u201cAnomalous video event detection using spatiotemporal context\u201d, \n Elsevier, Computer Vision and Image Understanding\n , Vol. 115 No. 3, pp. 323-333.","DOI":"10.1016\/j.cviu.2010.10.008"},{"key":"key2020122221245035400_b17","unstructured":"Fernando, R.-I.\n \n et al. (2005), \u201cA neural network agent-based approach to activity detection in ami environments\u201d, IEE International Workshop, Intelligent Environments, Colchester, 28-29 June, ISSN 0537-9989."},{"key":"key2020122221245035400_b18","unstructured":"Jaakko, S.\n \n et al. (2008), \u201cGaussian process person identifier based on simple floor sensors\u201d, EuroSSC 2008, LNCS 5279, Springer-Verlag Berlin Heidelberg, pp. 55-68, doi:10:1007\/978-3-540-88793-5-5."},{"key":"key2020122221245035400_b19","unstructured":"Jae-Gil, L.\n \n et al. (2007), \u201cTrajectory clustering: a partition-and-group framework \u201d, SIGMOD\u201907, ACM 978-1-59593-686-8\/07\/0006, doi: 10:1146\/1247801247546, pp. 593-604."},{"key":"key2020122221245035400_b20","doi-asserted-by":"crossref","unstructured":"Jan, O.B.\n (2001), \u201cA pattern approach to interaction design\u201d, \n AI & Soc\n , Vol. 15, Springer \u2013 Verlag, London, pp. 359-376, ISBN-10:0471498289, Ed1.","DOI":"10.1007\/BF01206115"},{"key":"key2020122221245035400_b22","doi-asserted-by":"crossref","unstructured":"Jianping, Z.\n \n et al. (2010), \u201cA new distance measure for hidden Markov models,\u201d \n Expert Systems with Applications\n , Vol. 37 No. 2, pp. 1550-1555.","DOI":"10.1016\/j.eswa.2009.06.063"},{"key":"key2020122221245035400_b23","unstructured":"Jo\u00eblle, C.\n \n et al. (2005), \u201cContext is key: communications of the ACM\u201d, Vol. 48 No. 3, pp. 49-53."},{"key":"key2020122221245035400_b24","doi-asserted-by":"crossref","unstructured":"Juan, C.A.\n \n et al. (2010), \u201cAmbient intelligence and smart environments: a state of the art\u201d, Springer Science + Business Media, Llc, Part 1, doi:10.1007\/978-0-387-93808-0_1, pp. 3-31.","DOI":"10.1007\/978-0-387-93808-0_1"},{"key":"key2020122221245035400_b25","unstructured":"Karthika, L.\n and \n Sumathi, A.C.\n (2012), \u201cAutomatic discovery and recognition of activities in smart environment\u201d, \n International Journal of Modern Engineering Research\n , Vol. 2 No. 1, pp. 353-358."},{"key":"key2020122221245035400_b26","doi-asserted-by":"crossref","unstructured":"Mu-Yen, C.\n and \n Edwin, D.L.\n (2013), \u201cSmart space technology innovations\u201d, \n Library Hi Tech\n , Vol. 31 No. 2, pp. 197-200.","DOI":"10.1108\/07378831311329004"},{"key":"key2020122221245035400_b27","unstructured":"Oren, B.\n and \n Michal, I.\n (2005), \u201cDetecting Irregularities in Images and Video\u201d, Proceedings of the Tenth IEEE International Conference on Computer Vision, No. 15505499\/ 05."},{"key":"key2020122221245035400_b30","unstructured":"Parisa, R.\n and \n Diane, J.C.\n (2010), \u201cMining Sensor Streams for discovering human activity patterns over time\u201d, IEEE International Conference on Data Mining, 1550-4786\/10, doi10.1109\/ICDM. 40."},{"key":"key2020122221245035400_b28","unstructured":"Parisa, R.\n and \n Diane, J.C.\n (2011), \u201cActivity knowledge transfer in smart environments\u201d, \n Elsevier, Pervasive and Mobile Computing\n , Vol. 7 No. 2011, pp. 331-343."},{"key":"key2020122221245035400_b29","doi-asserted-by":"crossref","unstructured":"Parisa, R.\n \n et al. (2011), \u201cDiscovering activities to recognize and track in a smart environment\u201d, \n IEEE Transactions on Knowledge and Data Engineering\n , Vol. 23 No. 4.","DOI":"10.1109\/TKDE.2010.148"},{"key":"key2020122221245035400_b31","unstructured":"Paul, C.\n and \n Richard, M.\n (2001), \u201cAn adaptive approach to indexing pervasive data\u201d, ACM 1-58113-412-6\/01\/05."},{"key":"key2020122221245035400_b32","doi-asserted-by":"crossref","unstructured":"Peng, L.\n and \n Elia, E.-D.\n \n et al. (2007), \u201cApplying data mining algorithms to inpatient dataset with missing values\u201d, \n Journal of Enterprise Information Management\n , Vol. 21 No. 1 pp. 81-92.","DOI":"10.1108\/17410390810842273"},{"key":"key2020122221245035400_b33","doi-asserted-by":"crossref","unstructured":"Piciarelli, C.\n and \n Foresti, G.L.\n (2006), \u201cOn-line trajectory clustering for anomalous events detection\u201d, \n Elsevier, Pattern Recognition Letters\n , Vol. 27 No. 15, pp. 1835-1842.","DOI":"10.1016\/j.patrec.2006.02.004"},{"key":"key2020122221245035400_b34","unstructured":"Rainer, S.\n \n et al. (2008), \u201cTracking identities and attention in smart environments \u2013 contributions and progress in the CHIL project\u201d, IEEE, Automatic Face and Gesture Recognition, 8th International Conference, pp. 1-8, 978-1-4244-2154-1\/08\/c, doi: 1109\/AFGR2008.4813322."},{"key":"key2020122221245035400_b35","unstructured":"Ramakrishnan, S.\n and \n Rakesh, A.\n (2013), \u201cMining sequential patterns: generalizations and performance improvements\u201d, Proceeding, the 5th International Conference on Extending Database Technology, pp. 3-17."},{"key":"key2020122221245035400_b36","doi-asserted-by":"crossref","unstructured":"Stephen, J.P.\n and \n Ernest, D.D.S.\n (1997), \u201cSmart sensors for industrial applications\u201d, \n Sensor Review\n , Vol. 17 No. 3, pp. 217-222.","DOI":"10.1108\/02602289710172337"},{"key":"key2020122221245035400_b37","unstructured":"Szewcyzk, S.\n \n et al. (2009), \u201cAnnotating smart environment sensor data for activity Learning Technology and HealthCare\u201d, Vol. 17 No. 3, pp. 161-169."},{"key":"key2020122221245035400_b38","unstructured":"T^am, H.\n and \n Bernt, S.\n (2006), \u201cUnsupervised discovery of structure in activity data using multiple eigenspaces\u201d, Springer-Verlag, Berlin Heidelberg, LNCS 3987, pp. 151-167."},{"key":"key2020122221245035400_b39","unstructured":"Tao, G.\n \n et al. (2009), \u201cepSICAR: an emerging patterns based approach to sequential, interleaved, and concurrent activity recognition\u201d, Proceedings of the IEEE International Conference on PCC, 978-1-4244-3304-9\/09\/$25.00, pp. 1-9, doi: 1109\/PERCOM2009.4912776."},{"key":"key2020122221245035400_b40","doi-asserted-by":"crossref","unstructured":"Tao, W.\n (2012), \u201cAn efficient algorithm for mining condensed sequential pattern basis\u201d, \n Kybernetes\n , Vol. 41 No. 9, pp. 1289-1296.","DOI":"10.1108\/03684921211275315"},{"key":"key2020122221245035400_b41","unstructured":"Thomas, B.M.\n \n et al. (2006), \u201cA survey of advances in vision-based human motion capture and analysis\u201d, \n Elsevier, Computer Vision and Image Understanding\n , Vol. 104 Nos 2-3, pp. 90-126."},{"key":"key2020122221245035400_b42","unstructured":"Wiliem, A.\n \n et al. (2008), \n Detecting Uncommon Trajectories in Digital Image Computing: Techniques and Applications (DICTA)\n , Canberra, pp. 1-3, doi: 10:1109\/DICTA2008.45, pp. 398-404."},{"key":"key2020122221245035400_b43","doi-asserted-by":"crossref","unstructured":"Yan, H.\n \n et al. (2008), \u201cA framework for mining sequential patterns from spatio-temporal event data sets\u201d, \n IEEE Transactions on Knowledge and Data Engineering\n , Vol. 20 No. 4, pp. 433-448.","DOI":"10.1109\/TKDE.2007.190712"},{"key":"key2020122221245035400_b44","doi-asserted-by":"crossref","unstructured":"Yiu, C.Y.\n and \n Yau, Y.\n (2006), \u201cA learning model of intelligent home\u201d, \n Facilities\n , Vol. 24 Nos 9\/10, pp. 365-375.","DOI":"10.1108\/02632770610677646"},{"key":"key2020122221245035400_b45","unstructured":"Zhonghong, W.\n (2008), \u201cSmart Spaces: creating new instructional space with smart classroom technology\u201d, \n New Library World\n , Vol. 109 Nos 3\/4, pp. 150-165."},{"key":"key2020122221245035400_frd1","doi-asserted-by":"crossref","unstructured":"Jianhua, M.\n and \n Laurence, T.Y.\n \n et al. (2005), \u201cTowards a smart world and ubiquitous intelligence: a walkthrough from smart things to smart hyperspaces and UbicKids\u201d, \n International Journal of Pervasive Computing and Communications\n , Vol. 1 No. 1, pp. 53-68.","DOI":"10.1108\/17427370580000113"}],"container-title":["Industrial Management & Data Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/IMDS-12-2014-0386","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-12-2014-0386\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-12-2014-0386\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:54:15Z","timestamp":1753394055000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/imds\/article\/115\/6\/1151-1178\/459716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,13]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2015,7,13]]}},"alternative-id":["10.1108\/IMDS-12-2014-0386"],"URL":"https:\/\/doi.org\/10.1108\/imds-12-2014-0386","relation":{},"ISSN":["0263-5577"],"issn-type":[{"type":"print","value":"0263-5577"}],"subject":[],"published":{"date-parts":[[2015,7,13]]}}}