{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:49:58Z","timestamp":1742996998445,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811941085"},{"type":"electronic","value":"9789811941092"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-4109-2_46","type":"book-chapter","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T14:38:26Z","timestamp":1659364706000},"page":"479-484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Web Data Mining Algorithm in Cloud Service Architecture Based on New Popular Learning Algorithm and Adaptive Adjustment Mechanism"],"prefix":"10.1007","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tie","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"issue":"1","key":"46_CR1","first-page":"169","volume":"10","author":"L Maaten","year":"2009","unstructured":"Maaten, L., Postma, E., Herik, J.: Dimensionality reduction: a comparative review. Rev. Lit. Arts Am. 10(1), 169\u2013182 (2009)","journal-title":"Rev. Lit. Arts Am."},{"key":"46_CR2","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.procs.2019.08.084","volume":"155","author":"L Rabhi","year":"2019","unstructured":"Rabhi, L., Falih, N., Afraites, L., et al.: Big data approach and its applications in various fields: review. Procedia Comput. Sci. 155, 599\u2013605 (2019)","journal-title":"Procedia Comput. Sci."},{"issue":"11","key":"46_CR3","doi-asserted-by":"publisher","first-page":"8173","DOI":"10.1007\/s00500-019-04384-7","volume":"24","author":"W Wang","year":"2019","unstructured":"Wang, W., Lu, C.: Visualization analysis of big data research based on Citespace. Soft Comput. 24(11), 8173\u20138186 (2019). https:\/\/doi.org\/10.1007\/s00500-019-04384-7","journal-title":"Soft Comput."},{"issue":"1","key":"46_CR4","first-page":"1","volume":"24","author":"V Tournay","year":"2019","unstructured":"Tournay, V., Jacomy, M., Necula, A., et al.: A new web-based big data analytics for dynamic public opinion mapping in digital networks on contested biotechnology fields. Omics J. Integr. Biol. 24(1), 1\u201313 (2019)","journal-title":"Omics J. Integr. Biol."},{"key":"46_CR5","unstructured":"Yha, B., Wba, C., Irf, A., et al.: Big data and IoT-based applications in smart environments: a systematic review-ScienceDirect. Comput. Sci. Rev. 39, 1\u201323"},{"issue":"2","key":"46_CR6","doi-asserted-by":"publisher","first-page":"e488","DOI":"10.7717\/peerj-cs.488","volume":"7","author":"AM Rahmani","year":"2021","unstructured":"Rahmani, A.M., Azhir, E., Ali, S., et al.: Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Comput. Sci. 7(2), e488(2021)","journal-title":"PeerJ Comput. Sci."},{"key":"46_CR7","unstructured":"Khalil, A. et al.: Flight web searches analytics through big data. Int. J. Comput. Appl. Technol. 21\u201336 (2021)"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Heidari, M., et al.: Finding relevant information via a discrete fourier expansion. In: 2021 International Conference on Machine Learning, pp. 4181\u20134191. PMLR (2021)","DOI":"10.1109\/ISIT45174.2021.9517924"},{"issue":"9","key":"46_CR9","doi-asserted-by":"publisher","first-page":"e28765","DOI":"10.2196\/28765","volume":"23","author":"KK Dysthe","year":"2021","unstructured":"Dysthe, K.K., et al.: Finding relevant psychoeducation content for adolescents experiencing symptoms of depression: content analysis of user-generated online texts. J. Med. Internet Res. 23(9), e28765 (2021)","journal-title":"J. Med. Internet Res."},{"key":"46_CR10","doi-asserted-by":"publisher","unstructured":"Piotrkowicz, A., Johnson, O., Hall, G.: Finding relevant free-text radiology reports at scale with IBM Watson content analytics: a feasibility study in the UK NHS. J. Biomed. Semant. 10, 21 (2019). https:\/\/doi.org\/10.1186\/s13326-019-0213-5","DOI":"10.1186\/s13326-019-0213-5"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Cohen-Osher, M., et al.: Finding information framework: a tool to teach life-long learning skills. PRiMER Peer-Rev. Rep. Med. Educ. Res. 5, 1\u201319 (2021)","DOI":"10.22454\/PRiMER.2021.934572"},{"issue":"3","key":"46_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s10115-017-1100-y","volume":"55","author":"D Diefenbach","year":"2017","unstructured":"Diefenbach, D., Lopez, V., Singh, K., Maret, P.: Core techniques of question answering systems over knowledge bases: a survey. Knowl. Inf. Syst. 55(3), 529\u2013569 (2017). https:\/\/doi.org\/10.1007\/s10115-017-1100-y","journal-title":"Knowl. Inf. Syst."},{"issue":"3","key":"46_CR13","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1002\/asi.24234","volume":"71","author":"J Liu","year":"2020","unstructured":"Liu, J., Liu, C., Belkin, N.J.: Personalization in text information retrieval: a survey. J. Assoc. Inf. Sci. Technol. 71(3), 349\u2013369 (2020)","journal-title":"J. Assoc. Inf. Sci. Technol."},{"issue":"3","key":"46_CR14","doi-asserted-by":"publisher","first-page":"263","DOI":"10.2501\/JAR-2018-035","volume":"58","author":"J Kietzmann","year":"2018","unstructured":"Kietzmann, J., Paschen, J., Treen, E.: Artificial intelligence in advertising: how marketers can leverage artificial intelligence along the consumer journey. J. Advert. Res. 58(3), 263\u2013267 (2018)","journal-title":"J. Advert. Res."},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Lee, G., Yun, U., et al.: Erasable itemset mining over incremental databases with weight conditions. Eng. Appl. Artif. Intell. 89\u201399 (2016)","DOI":"10.1016\/j.engappai.2016.03.003"}],"container-title":["Communications in Computer and Information Science","Exploration of Novel Intelligent Optimization Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-4109-2_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T14:48:05Z","timestamp":1659365285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-4109-2_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811941085","9789811941092"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-4109-2_46","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISICA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligence Computation and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Giangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isica2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gdstinfo.scau.edu.cn\/isica2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","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":"48","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":"48% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}