{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:30:07Z","timestamp":1743075007727,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030339036"},{"type":"electronic","value":"9783030339043"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-33904-3_73","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T22:40:05Z","timestamp":1572043205000},"page":"769-779","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video Popularity Forecasting to Improve Cache Miss Rate in Content Delivery Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1017-703X","authenticated-orcid":false,"given":"Jairo","family":"Rojas-Delgado","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7392-1570","authenticated-orcid":false,"given":"Rafael","family":"Trujillo-Ras\u00faa","sequence":"additional","affiliation":[]},{"given":"Rafael","family":"Bello","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0146-4953","authenticated-orcid":false,"given":"Gerdys E. Jim\u00e9nez","family":"Moya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"issue":"7","key":"73_CR1","doi-asserted-by":"publisher","first-page":"B25","DOI":"10.1364\/AO.57.000B25","volume":"57","author":"HM Ahmed","year":"2018","unstructured":"Ahmed, H.M., Youssef, B.A., Elkorany, A.S., Saleeb, A.A., El-Samie, F.A.: Hybrid gray wolf optimizer\u2013artificial neural network classification approach for magnetic resonance brain images. Appl. Opt. 57(7), B25\u2013B31 (2018)","journal-title":"Appl. Opt."},{"key":"73_CR2","doi-asserted-by":"crossref","unstructured":"Alghamdi, F., Mahfoudh, S., Barnawi, A.: A novel fog computing based architecture to improve the performance in content delivery networks. Wirel. Commun. Mob. Comput. 2019 (2019)","DOI":"10.1155\/2019\/7864094"},{"issue":"4","key":"73_CR3","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1109\/TNET.2015.2461599","volume":"24","author":"D Applegate","year":"2016","unstructured":"Applegate, D., Archer, A., Gopalakrishnan, V., Lee, S., Ramakrishnan, K.: Optimal content placement for a large-scale VoD system. IEEE\/ACM Trans. Netw. 24(4), 2114\u20132127 (2016)","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"73_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","volume-title":"Neural Networks: Tricks of the Trade","author":"Y Bengio","year":"2012","unstructured":"Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437\u2013478. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_26"},{"issue":"1","key":"73_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/EVCO_r_00180","volume":"25","author":"Mohammad Reza Bonyadi","year":"2017","unstructured":"Bonyadi, M.R., Michalewicz, Z.: Particle swarm optimization for single objective continuous space problems: a review (2017)","journal-title":"Evolutionary Computation"},{"key":"73_CR6","doi-asserted-by":"crossref","unstructured":"Borst, S., Gupta, V., Walid, A.: Distributed caching algorithms for content distribution networks. In: 2010 Proceedings IEEE INFOCOM, pp. 1\u20139. Citeseer (2010)","DOI":"10.1109\/INFCOM.2010.5461964"},{"issue":"6","key":"73_CR7","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1109\/TPDS.2016.2614805","volume":"28","author":"N Carlsson","year":"2017","unstructured":"Carlsson, N., Eager, D.: Ephemeral content popularity at the edge and implications for on-demand caching. IEEE Trans. Parallel Distrib. Syst. 28(6), 1621\u20131634 (2017)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"73_CR8","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-981-10-6916-1_26","volume-title":"Smart Trends in Systems, Security and Sustainability","author":"S Chatterjee","year":"2018","unstructured":"Chatterjee, S., Dey, N., Ashour, A.S., Drugarin, C.V.A.: Electrical energy output prediction using cuckoo search based artificial neural network. In: Yang, X.-S., Nagar, A.K., Joshi, A. (eds.) Smart Trends in Systems, Security and Sustainability. LNNS, vol. 18, pp. 277\u2013285. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-6916-1_26"},{"key":"73_CR9","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1007\/978-3-319-75928-9_91","volume-title":"Advances in Internet, Data & Web Technologies","author":"W Chen","year":"2018","unstructured":"Chen, W., Wang, X.A., Zhang, W., Xu, C.: Phishing detection research based on PSO-BP neural network. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds.) EIDWT 2018. LNDECT, vol. 17, pp. 990\u2013998. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75928-9_91"},{"key":"73_CR10","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1109\/TWC.2019.2906202","volume":"18","author":"G Feng","year":"2019","unstructured":"Feng, G., Qin, S., Yum, T.S.P., Cao, G., et al.: Multi-agent reinforcement learning for efficient content caching in mobile D2D networks. IEEE Trans. Wirel. Commun. 18, 1610\u20131622 (2019)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"73_CR11","unstructured":"Friedlander, E., Aggarwal, V.: Generalization of LRU cache replacement policy with applications to video streaming. arXiv preprint arXiv:1806.10853 (2018)"},{"key":"73_CR12","doi-asserted-by":"crossref","unstructured":"Goian, H.S., Al-Jarrah, O.Y., Muhaidat, S., Al-Hammadi, Y., Yoo, P., Dianati, M.: Popularity-based video caching techniques for cache-enabled networks: a survey. IEEE Access (2019)","DOI":"10.1109\/ACCESS.2019.2898734"},{"key":"73_CR13","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"73_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12243-018-0698-4","volume":"74","author":"NB Hassine","year":"2019","unstructured":"Hassine, N.B., Minet, P., Marinca, D., Barth, D.: Popularity prediction-based caching in CDN. Ann. Telecommun. 74, 1\u201314 (2019)","journal-title":"Ann. Telecommun."},{"key":"73_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01132-1","volume-title":"Progress in Artificial Intelligence and Pattern Recognition","author":"YH Heredia","year":"2018","unstructured":"Heredia, Y.H., N\u00fa\u00f1ez, V.M., Shulcloper, J.R.: IWAIPR 2018. LNCS, vol. 11047. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01132-1"},{"key":"73_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"73_CR17","unstructured":"Izvorski, A., Deven, R., Dharmapurikar, M.: Method and apparatus for controlling source transmission rate for video streaming based on queuing delay. US Patent 9,860,605, 2 January 2018"},{"issue":"2","key":"73_CR18","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/TPAMI.2017.2670560","volume":"40","author":"YG Jiang","year":"2018","unstructured":"Jiang, Y.G., Wu, Z., Wang, J., Xue, X., Chang, S.F.: Exploiting feature and class relationships in video categorization with regularized deep neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 352\u2013364 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"73_CR19","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/2.268884","volume":"27","author":"R Karedla","year":"1994","unstructured":"Karedla, R., Love, J.S., Wherry, B.G.: Caching strategies to improve disk system performance. Computer 27(3), 38\u201346 (1994)","journal-title":"Computer"},{"key":"73_CR20","doi-asserted-by":"crossref","unstructured":"Koch, C., Pfannm\u00fcller, J., Rizk, A., Hausheer, D., Steinmetz, R.: Category-aware hierarchical caching for video-on-demand content on YouTube. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 89\u2013100. ACM (2018)","DOI":"10.1145\/3204949.3204963"},{"key":"73_CR21","doi-asserted-by":"crossref","unstructured":"Koch, C., Werner, S., Rizk, A., Steinmetz, R.: Mira: proactive music video caching using convnet-based classification and multivariate popularity prediction. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 109\u2013115. IEEE (2018)","DOI":"10.1109\/MASCOTS.2018.00019"},{"key":"73_CR22","doi-asserted-by":"crossref","unstructured":"Lee, M.C., Ji, M., Molisch, A.F., Sastry, N.: Performance of caching-based D2D video distribution with measured popularity distributions. arXiv preprint arXiv:1806.05380 (2018)","DOI":"10.1109\/GLOBECOM38437.2019.9013504"},{"issue":"1","key":"73_CR23","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11036-016-0694-8","volume":"22","author":"Z Liu","year":"2017","unstructured":"Liu, Z., Dong, M., Gu, B., Zhang, C., Ji, Y., Tanaka, Y.: Fast-start video delivery in future internet architectures with intra-domain caching. Mob. Netw. Appl. 22(1), 98\u2013112 (2017)","journal-title":"Mob. Netw. Appl."},{"key":"73_CR24","unstructured":"Livni, R., Shalev-Shwartz, S., Shamir, O.: On the computational efficiency of training neural networks. In: Advances in Neural Information Processing Systems, pp. 855\u2013863 (2014)"},{"issue":"4","key":"73_CR25","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MC.2004.1297303","volume":"37","author":"N Megiddo","year":"2004","unstructured":"Megiddo, N., Modha, D.S.: Outperforming LRU with an adaptive replacement cache algorithm. Computer 37(4), 58\u201365 (2004)","journal-title":"Computer"},{"key":"73_CR26","doi-asserted-by":"crossref","unstructured":"Mobahi, H., Fisher III, J.W.: A theoretical analysis of optimization by Gaussian continuation. In: AAAI, pp. 1205\u20131211 (2015)","DOI":"10.1609\/aaai.v29i1.9356"},{"issue":"4","key":"73_CR27","first-page":"7","volume":"175","author":"K Potdar","year":"2017","unstructured":"Potdar, K., Pardawala, T., Pai, C.: A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175(4), 7\u20139 (2017)","journal-title":"Int. J. Comput. Appl."},{"issue":"11","key":"73_CR28","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1109\/TMM.2017.2695439","volume":"19","author":"T Trzci\u0144ski","year":"2017","unstructured":"Trzci\u0144ski, T., Rokita, P.: Predicting popularity of online videos using support vector regression. IEEE Trans. Multimed. 19(11), 2561\u20132570 (2017)","journal-title":"IEEE Trans. Multimed."},{"key":"73_CR29","unstructured":"Cisco Visual: Cisco visual networking index: forecast and trends, 2017\u20132022. Technical report, Cisco Visual (2017)"},{"key":"73_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Proactive video push for optimizing bandwidth consumption in hybrid CDN-P2P VoD systems. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 2555\u20132563. IEEE (2018)","DOI":"10.1109\/INFOCOM.2018.8485962"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33904-3_73","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:13:40Z","timestamp":1710252820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33904-3_73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030339036","9783030339043"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33904-3_73","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Havana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuba","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ciarp.uci.cu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"128","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":"70","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":"0","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":"55% - 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":"3","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":"3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}