{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T05:10:25Z","timestamp":1745471425004,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031371165"},{"type":"electronic","value":"9783031371172"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-37117-2_2","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T23:04:56Z","timestamp":1687993496000},"page":"16-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Anomaly Detection Business Process for\u00a0Industrial Equipment Using Internet of\u00a0Things and\u00a0Unsupervised Machine Learning Algorithms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4987-7474","authenticated-orcid":false,"given":"Emrullah","family":"Gultekin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-5067","authenticated-orcid":false,"given":"Mehmet S.","family":"Aktas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Gultekin, E., Aktas, M.S.: A business workflow architecture for predictive maintenance using real-time anomaly prediction on streaming IoT data. In: 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan (2022). https:\/\/doi.org\/10.1109\/bigdata55660.2022.10020384","DOI":"10.1109\/bigdata55660.2022.10020384"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Mollao\u011flu, A., Baltao\u011flu, G., \u00c7akir, E., Akta\u015f, M.S.: Fraud detection on streaming customer behavior data with unsupervised learning methods. In: 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Kuala Lumpur, Malaysia, pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ICECCE52056.2021.9514152","DOI":"10.1109\/ICECCE52056.2021.9514152"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Kayacik, A.F., \u00d6zcan, B., Baltao\u011flu, G., \u00c7akir, E., Akta\u015f, M.S.: Real-time fraud prediction on streaming customer-behaviour data. In: 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Kuala Lumpur, Malaysia, pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ICECCE52056.2021.9514169","DOI":"10.1109\/ICECCE52056.2021.9514169"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Homayoun, S., Ahmadzadeh, M.: A review on data stream classification approaches. J. Adv. Comput. Sci. Technol. 5, 8 (2016). https:\/\/doi.org\/10.14419\/jacst.v5i1.5225","DOI":"10.14419\/jacst.v5i1.5225"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Gomes, H.M., Bifet, A., Read, J., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106, 1469\u20131495 (2017). https:\/\/doi.org\/10.1007\/s10994-017-5642-8","DOI":"10.1007\/s10994-017-5642-8"},{"issue":"5","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1109\/JIOT.2017.2712672","volume":"4","author":"A Akbar","year":"2017","unstructured":"Akbar, A., Khan, A., Carrez, F., Moessner, K.: Predictive analytics for complex IoT data streams. IEEE Internet Things J. 4(5), 1571\u20131582 (2017). https:\/\/doi.org\/10.1109\/JIOT.2017.2712672","journal-title":"IEEE Internet Things J."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation 893 modeling for aircraft engine run-to-failure simulation. In: Proceedings of the International Conference 894 Prognostics Health Management, pp. 1\u20139 (2008)","DOI":"10.1109\/PHM.2008.4711414"},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"95425","DOI":"10.1109\/ACCESS.2022.3203406","volume":"10","author":"O Asif","year":"2022","unstructured":"Asif, O., Haider, S.A., Naqvi, S.R., Zaki, J.F.W., Kwak, K.-S., Islam, S.M.R.: A deep learning model for remaining useful life prediction of aircraft turbofan engine on C-MAPSS dataset. IEEE Access 10, 95425\u201395440 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3203406","journal-title":"IEEE Access"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Muneer, A., Taib, S.M., Fati, S.M., Alhussian, H.: Deep-learning based prognosis approach for remaining useful life prediction of turbofan engine. Symmetry 13, 1861 (2021). https:\/\/doi.org\/10.3390\/sym13101861","DOI":"10.3390\/sym13101861"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Tufek, A., Gurbuz, A., Ekuklu, O.F., Aktas, M.S.: Provenance collection platform for the weather research and forecasting model. In: 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), Guangzhou, China, pp. 17\u201324 (2018). https:\/\/doi.org\/10.1109\/SKG.2018.00009","DOI":"10.1109\/SKG.2018.00009"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Riveni, M., Baeth, M.J., Aktas, M.S., Dustdar, S.: Provenance in social computing: A case study. In: 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, pp. 77\u201384 (2017). https:\/\/doi.org\/10.1109\/SKG.2017.00021","DOI":"10.1109\/SKG.2017.00021"},{"issue":"2017","key":"2_CR12","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.neucom.2017.02.045","volume":"240C","author":"L Guo","year":"2017","unstructured":"Guo, L., Li, N., Jia, F., Lei, Y., Lin, J.: A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240C(2017), 98\u2013109 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.02.045","journal-title":"Neurocomputing"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Tas, Y., Baeth, M.J., Aktas, M.S.: An approach to standalone provenance systems for big social provenance data. In: 2016 12th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, pp. 9\u201316 (2016). https:\/\/doi.org\/10.1109\/SKG.2016.010","DOI":"10.1109\/SKG.2016.010"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases - Volume 29 (VLDB \u201903), vol. 29. VLDB Endowment, pp. 81\u201392 (2003)","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"2_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4690","volume":"30","author":"MJ Baeth","year":"2018","unstructured":"Baeth, M.J., Aktas, M.S.: An approach to custom privacy policy violation detection problems using big social provenance data. Concurr. Computat. Pract. Exp. 30, e4690 (2018). https:\/\/doi.org\/10.1002\/cpe.4690","journal-title":"Concurr. Computat. Pract. Exp."},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1002\/cpe.1276","volume":"20","author":"MS Aktas","year":"2008","unstructured":"Aktas, M.S., Fox, G.C., Pierce, M., Oh, S.: XML metadata services. Concurr. Computat. Pract. Exp. 20, 801\u2013823 (2008). https:\/\/doi.org\/10.1002\/cpe.1276","journal-title":"Concurr. Computat. Pract. Exp."},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1002\/cpe.1557","volume":"22","author":"MS Aktas","year":"2010","unstructured":"Aktas, M.S., Pierce, M.: High-performance hybrid information service architecture. Concurr. Computat. Pract. Exp. 22, 2095\u20132123 (2010). https:\/\/doi.org\/10.1002\/cpe.1557","journal-title":"Concurr. Computat. Pract. Exp."},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Fox, G.C., et al.: Real time streaming data grid applications. Distributed Cooperative Laboratories: Networking, Instrumentation, and Measurements, pp. 253\u2013267. Springer, Boston (2006). https:\/\/doi.org\/10.1007\/0-387-30394-4_17","DOI":"10.1007\/0-387-30394-4_17"},{"issue":"3","key":"2_CR19","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.future.2006.05.009","volume":"23","author":"MS Aktas","year":"2007","unstructured":"Aktas, M.S., Fox, G.C., Pierce, M.: Fault tolerant high performance information services for dynamic collections of grid and web services. Future Gen. Comput. Syst. 23(3), 317\u2013337 (2007). https:\/\/doi.org\/10.1016\/j.future.2006.05.009","journal-title":"Future Gen. Comput. Syst."},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1002\/cpe.1312","volume":"20","author":"G Aydin","year":"2008","unstructured":"Aydin, G., Sayar, A., Gadgil, H., Aktas, M.S., Fox, G.C., Ko, S., Bulut, H., Pierce, M.E.: Building and applying geographical information system grids. Concurr. Computat. Pract. Exp. 20, 1653\u20131695 (2008). https:\/\/doi.org\/10.1002\/cpe.1312","journal-title":"Concurr. Computat. Pract. Exp."},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s00791-007-0083-8","volume":"12","author":"GC Fox","year":"2009","unstructured":"Fox, G.C., Aktas, M.S., Aydin, G., et al.: Algorithms and the grid. Comput. Visual Sci. 12, 115\u2013124 (2009). https:\/\/doi.org\/10.1007\/s00791-007-0083-8","journal-title":"Comput. Visual Sci."},{"key":"2_CR22","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1007\/s00024-006-0137-8","volume":"163","author":"M Aktas","year":"2006","unstructured":"Aktas, M., Aydin, G., Donnellan, A., et al.: iSERVO: Implementing the international solid earth research virtual observatory by integrating computational grid and geographical information web services. Pure Appl. Geophys. 163, 2281\u20132296 (2006). https:\/\/doi.org\/10.1007\/s00024-006-0137-8","journal-title":"Pure Appl. Geophys."},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Aydin, G., Aktas, M.S., Fox, G.C., Gadgil, H., Pierce, M., Saya, A.: SERVOGrid complexity computational environments (CCE) integrated performance analysis. In: The 6th IEEE\/ACM International Workshop on Grid Computing, 2005, Seattle, p. 6 (2005). https:\/\/doi.org\/10.1109\/GRID.2005.1542750","DOI":"10.1109\/GRID.2005.1542750"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Pierce, M.E., et al.: The QuakeSim Project: Web services for managing geophysical data and applications. In: Tiampo, K.F., Weatherley, D.K., Weinstein, S.A. (eds) Earthquakes: Simulations, Sources and Tsunamis. Pageoph Topical Volumes. Birkh\u00e4user Basel (2008). https:\/\/doi.org\/10.1007\/978-3-7643-8757-0_11","DOI":"10.1007\/978-3-7643-8757-0_11"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Uygun, Y., Oguz, R.F., Olmezogullari, E., Aktas, M.S.: On the large-scale graph data processing for user interface testing in big data science projects. In: 2020 IEEE International Conference on Big Data (Big Data), Atlanta, pp. 2049\u20132056 (2020). https:\/\/doi.org\/10.1109\/BigData50022.2020.9378153","DOI":"10.1109\/BigData50022.2020.9378153"},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Sahinoglu, M., Incki, K., Aktas, M.S.: Mobile application verification: A systematic mapping study. In: Computational Science and Its Applications - ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science, vol. 9159. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-21413-9_11","DOI":"10.1007\/978-3-319-21413-9_11"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Kapdan, M., Aktas, M., Yigit, M.: On the structural code clone detection problem: A survey and software metric based approach. In: Computational Science and Its Applications - ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol. 8583. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-09156-3_35","DOI":"10.1007\/978-3-319-09156-3_35"},{"issue":"9","key":"2_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6546","volume":"34","author":"E Olmezogullari","year":"2022","unstructured":"Olmezogullari, E., Aktas, M.S.: Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior. Concurr. Computat. Pract. Exp. 34(9), e6546 (2022). https:\/\/doi.org\/10.1002\/cpe.6546","journal-title":"Concurr. Computat. Pract. Exp."},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Olmezogullari, E., Aktas, M.S.: Representation of click-stream datasequences for learning user navigational behavior by using embeddings. In: 2020 IEEE International Conference on Big Data (Big Data), Atlanta, pp. 3173\u20133179 (2020). https:\/\/doi.org\/10.1109\/BigData50022.2020.9378437","DOI":"10.1109\/BigData50022.2020.9378437"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Nacar, M.A., et al.: VLab: collaborative grid services and portals to support computational material science. Concurr. Computat.: Pract. Exper. 19, 1717\u20131728 (2007). https:\/\/doi.org\/10.1002\/cpe.1199","DOI":"10.1002\/cpe.1199"},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Dundar, B., Astekin, M., Aktas, M.S.: A big data processing framework for self-healing Internet of Things applications. In: 2016 12th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, pp. 62\u201368 (2016). https:\/\/doi.org\/10.1109\/SKG.2016.017","DOI":"10.1109\/SKG.2016.017"},{"key":"2_CR32","doi-asserted-by":"publisher","unstructured":"Baeth, M.J., Aktas, M.S.: Detecting misinformation in social networks using provenance data. In: 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, pp. 85\u201389 (2017). https:\/\/doi.org\/10.1109\/SKG.2017.00022","DOI":"10.1109\/SKG.2017.00022"},{"key":"2_CR33","unstructured":"Aktas, M., et al.: Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. In: NASA Earth-Sun System Technology Conference, University of Maryland, Adelphi, Maryland (2005)"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37117-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T23:32:09Z","timestamp":1687995129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37117-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031371165","9783031371172"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37117-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}