{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T06:46:42Z","timestamp":1778568402777,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,21]],"date-time":"2017-10-21T00:00:00Z","timestamp":1508544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others.<\/jats:p>","DOI":"10.3390\/sym9100244","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T04:32:19Z","timestamp":1508733139000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring"],"prefix":"10.3390","volume":"9","author":[{"given":"Kun","family":"Lan","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa 999078, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Fong","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa 999078, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5909-9661","authenticated-orcid":false,"given":"Wei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athanasios","family":"Vasilakos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, SE-97187 Lulea, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-9615","authenticated-orcid":false,"given":"Richard","family":"Millham","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Durban University of Technology, Ritson Campus, Durban P.O. BOX 1334, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gil, D., Ferr\u00e1ndez, A., Mora-Mora, H., and Peral, J. (2016). Internet of things: A review of surveys based on context aware intelligent services. Sensors, 16.","DOI":"10.3390\/s16071069"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., and Corchado, E. (2017). Educational big data mining: How to enhance virtual learning environments. International Joint Conference Soco\u201916-Cisis\u201916-Iceute\u201916: San Sebasti\u00e1n, Spain, October 19th-21st, 2016 Proceedings, Springer International Publishing.","DOI":"10.1007\/978-3-319-47364-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ducange, P., Pecori, R., and Mezzina, P. (2017). A glimpse on big data analytics in the framework of marketing strategies. Soft Comput., 1\u201318.","DOI":"10.1007\/s00500-017-2536-4"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","article-title":"A survey on data stream clustering and classification","volume":"45","author":"Nguyen","year":"2015","journal-title":"Knowl. Inf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1083784.1083789","article-title":"Mining data streams: A review","volume":"34","author":"Gaber","year":"2005","journal-title":"ACM Sigmod Rec."},{"key":"ref_6","unstructured":"Sun, D., Zhang, G., Zheng, W., and Li, K. (2015). Key technologies for big data stream computing. Big Data, Algorithms, Analytics, and Applications, CRC."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The internet of things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lim, J., Yu, H., and Gil, J.-M. (2017). An efficient and energy-aware cloud consolidation algorithm for multimedia big data applications. Symmetry, 9.","DOI":"10.3390\/sym9090184"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kwon, D., Park, S., and Ryu, J.-T. (2017). A study on big data thinking of the internet of things-based smart-connected car in conjunction with controller area network bus and 4g-long term evolution. Symmetry, 9.","DOI":"10.3390\/sym9080152"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Byun, H., Park, J.H., and Jeong, Y.-S. (2016). Optional frame selection algorithm for adaptive symmetric service of augmented reality big data on smart devices. Symmetry, 8.","DOI":"10.3390\/sym8050037"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog Computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_12","unstructured":"Kennedy, J., Eberhart, R., and Shi, Y. (2001). Swarm Intelligence, Morgan Kaufmann publishers Inc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fong, S., Lan, K., and Wong, R. (2013). Classifying human voices by using hybrid sfx time-series preprocessing and ensemble feature selection. BioMed Res. Int., 2013.","DOI":"10.1155\/2013\/720834"},{"key":"ref_14","unstructured":"Hall, M.A. (July, January 29). Correlation-based feature selection of discrete and numeric class machine learning. Proceedings of the ICML Seventeenth International Conference on Machine Learning, Stanford, CA, USA."},{"key":"ref_15","unstructured":"Yang, Y., and Pedersen, J.O. (1997, January 8\u201312). A comparative study on feature selection in text categorization. Proceedings of the ICML Fourteenth International Conference on Machine Learning, Nashville, TN, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.eswa.2007.08.088","article-title":"Particle swarm optimization for parameter determination and feature selection of support vector machines","volume":"35","author":"Lin","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/5254.671091","article-title":"Feature subset selection using a genetic algorithm","volume":"13","author":"Yang","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Domingos, P., and Hulten, G. (2000, January 20\u201323). Mining high-speed data streams. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA.","DOI":"10.1145\/347090.347107"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/776985.776986","article-title":"Issues in data stream management","volume":"32","author":"Golab","year":"2003","journal-title":"ACM Sigmod Rec."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Muthitacharoen, A., Chen, B., and Mazieres, D. (2001, January 21\u201324). A low-bandwidth network file system. Proceedings of the eighteenth ACM SIGOPS Operating Systems Review, Banff, AB, Canada.","DOI":"10.1145\/502034.502052"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, L., Dong, X., Zhang, X., Guo, F., Wang, Y., and Gong, W. (2016). A logistic based mathematical model to optimize duplicate elimination ratio in content defined chunking based big data storage system. Symmetry, 8.","DOI":"10.3390\/sym8070069"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, Y., and Shasha, D. (2002, January 20\u201323). Statstream: Statistical monitoring of thousands of data streams in real time. Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China.","DOI":"10.1016\/B978-155860869-6\/50039-1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bifet, A., and Gavalda, R. (2007, January 26\u201328). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining, Minneapolis, MN, USA.","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.envsoft.2016.07.020","article-title":"Real-time environmental sensor data: An application to water quality using web services","volume":"84","author":"Wong","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jhydrol.2010.08.028","article-title":"Integrating hydrograph modeling with real-time flow monitoring to generate hydrograph-specific sampling schemes","volume":"393","author":"Gall","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.jare.2014.03.004","article-title":"An intelligent approach for variable size segmentation of non-stationary signals","volume":"6","author":"Azami","year":"2015","journal-title":"J. Adv. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0167-8655(01)00003-4","article-title":"Feature extraction using wavelet and fractal","volume":"22","author":"Tao","year":"2001","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhu, H., and Wang, Y. (2017). An orthogonal multi-swarm cooperative pso algorithm with a particle trajectory knowledge base. Symmetry, 9.","DOI":"10.3390\/sym9010015"},{"key":"ref_29","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"987","DOI":"10.2307\/1912773","article-title":"Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation","volume":"50","author":"Engle","year":"1982","journal-title":"Econometrica"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.physa.2004.06.130","article-title":"Time-dependent hurst exponent in financial time series","volume":"344","author":"Carbone","year":"2004","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_32","unstructured":"Appel, G. (2005). Technical Analysis: POWER Tools for Active Investors, FT Press."},{"key":"ref_33","unstructured":"MacQueen, J. (1965\u20137, January 27). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/10\/244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:05Z","timestamp":1760208485000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/10\/244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,21]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["sym9100244"],"URL":"https:\/\/doi.org\/10.3390\/sym9100244","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,21]]}}}