{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:05:53Z","timestamp":1740099953062,"version":"3.37.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030715922"},{"type":"electronic","value":"9783030715939"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-71593-9_20","type":"book-chapter","created":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T20:02:27Z","timestamp":1615665747000},"page":"249-260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1279-9318","authenticated-orcid":false,"given":"Alper","family":"Tufek","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":[[2021,3,14]]},"reference":[{"issue":"3","key":"20_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/1084805.1084812","volume":"34","author":"YL Simmhan","year":"2005","unstructured":"Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM SIGMOD Rec. 34(3), 31\u201336 (2005). https:\/\/doi.org\/10.1145\/1084805.1084812","journal-title":"ACM SIGMOD Rec."},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling provenance metadata. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 773\u2013776 (2013). https:\/\/doi.org\/10.1145\/2452376.2452478","DOI":"10.1145\/2452376.2452478"},{"key":"20_CR3","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), pp. 17\u201324 (2018). https:\/\/doi.org\/10.1109\/skg.2018.00009","DOI":"10.1109\/skg.2018.00009"},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Simmhan, Y.L., Plale, B., Gannon, D.: A framework for collecting provenance in data-centric scientific workflows. In: 2006 IEEE International Conference on Web Services (ICWS06), pp. 427\u2013436 (2006). https:\/\/doi.org\/10.1109\/icws.2006.5","DOI":"10.1109\/icws.2006.5"},{"key":"20_CR5","unstructured":"Indiana University, Pervasive Technology Institute. (n.d.). Karma. Pervasive Technology Institute website: https:\/\/pti.iu.edu\/impact\/open-source\/karma.html. 12 Apr 2020"},{"key":"20_CR6","unstructured":"Indiana University, Data To Insight Center (D2I). (n.d.). Komadu: Provenance collection and visualization tool based on W3C PROV standard, GitHub website: https:\/\/github.com\/Data-to-Insight-Center\/komadu. 12 Apr 2020"},{"key":"20_CR7","unstructured":"Droegemeier, K.K., et al.: Linked environments for atmospheric discovery (LEAD): architecture, technology roadmap and deployment strategy. In: 21st Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, January 2005"},{"issue":"7","key":"20_CR8","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. Concurrency Comput. Pract. Experience 20(7), 801\u2013823 (2008). https:\/\/doi.org\/10.1002\/cpe.1276","journal-title":"Concurrency Comput. Pract. Experience"},{"issue":"15","key":"20_CR9","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. Concurrency Comput. Pract. Experience 22(15), 2095\u20132123 (2010). https:\/\/doi.org\/10.1002\/cpe.1557","journal-title":"Concurrency Comput. Pract. Experience"},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Aktas, M.S., Fox, G.C., Pierce, M.: Information services for dynamically assembled semantic grids. In: 2005 First International Conference on Semantics, Knowledge and Grid, pp. 10\u201310 (2005). https:\/\/doi.org\/10.1109\/skg.2005.83","DOI":"10.1109\/skg.2005.83"},{"issue":"11","key":"20_CR11","doi-asserted-by":"publisher","first-page":"5090","DOI":"10.1109\/TGRS.2013.2266929","volume":"51","author":"S Jensen","year":"2013","unstructured":"Jensen, S., Plale, B., Aktas, M.S., Luo, Y., Chen, P., Conover, H.: Provenance capture and use in a satellite data processing pipeline. IEEE Trans. Geosci. Remote Sens. 51(11), 5090\u20135097 (2013). https:\/\/doi.org\/10.1109\/TGRS.2013.2266929","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"20_CR12","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.future.2010.07.005","volume":"27","author":"L Moreau","year":"2011","unstructured":"Moreau, L., et al.: The open provenance model core specification (v1.1). Future Gener. Comput. Syst. 27(6), 743\u2013756 (2011). https:\/\/doi.org\/10.1016\/j.future.2010.07.005","journal-title":"Future Gener. Comput. Syst."},{"issue":"4","key":"20_CR13","doi-asserted-by":"publisher","first-page":"944","DOI":"10.2166\/hydro.2012.134","volume":"14","author":"Y Shu","year":"2012","unstructured":"Shu, Y., Taylor, K., Hapuarachchi, P., Peters, C.: Modelling provenance in hydrologic science: a case study on streamflow forecasting. J. Hydroinformatics 14(4), 944\u2013959 (2012). https:\/\/doi.org\/10.2166\/hydro.2012.134","journal-title":"J. Hydroinformatics"},{"issue":"5","key":"20_CR14","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1175\/bams-d-15-00139.1","volume":"98","author":"L Bernardet","year":"2017","unstructured":"Bernardet, L., Carson, L., Tallapragada, V.: The design of a modern information technology infrastructure to facilitate research-to-operations transition for NCEP\u2019s modeling suites. Bull. Am. Meteor. Soc. 98(5), 899\u2013904 (2017). https:\/\/doi.org\/10.1175\/bams-d-15-00139.1","journal-title":"Bull. Am. Meteor. Soc."},{"key":"20_CR15","unstructured":"McCallumzy, A., Nigamy, K., Renniey, J., Seymorey, K.: Building domain-specific search engines with machine learning techniques. In: Proceedings of the AAAI Spring Symposium on Intelligent Agents in Cyberspace, pp. 28\u201339 (1999). http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.14.4717"},{"key":"20_CR16","unstructured":"Boyan, J., Freitag, D., Joachims, T.: A machine learning architecture for optimizing web search engines. In: AAAI Workshop on Internet Based Information Systems, pp. 1\u20138 (1996). http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.41.9172"},{"key":"20_CR17","doi-asserted-by":"publisher","unstructured":"Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 183\u2013194 (2008). https:\/\/doi.org\/10.1145\/1341531.1341557","DOI":"10.1145\/1341531.1341557"},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Neethu, M.S., Rajasree, R.: Sentiment analysis in Twitter using machine learning techniques. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1\u20135 (2013). https:\/\/doi.org\/10.1109\/ICCCNT.2013.6726818","DOI":"10.1109\/ICCCNT.2013.6726818"},{"key":"20_CR19","doi-asserted-by":"publisher","unstructured":"Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79\u201386 (2002). https:\/\/doi.org\/10.3115\/1118693.1118704","DOI":"10.3115\/1118693.1118704"},{"key":"20_CR20","unstructured":"Groth, P., Moreau, L. (Eds.). (n.d.). PROV-Overview: An Overview of the PROV Family of Documents. https:\/\/www.w3.org\/TR\/prov-overview. 12 Apr 2020"},{"issue":"3","key":"20_CR21","doi-asserted-by":"publisher","first-page":"e4793","DOI":"10.1002\/cpe.4793","volume":"31","author":"M Baeth","year":"2019","unstructured":"Baeth, M., Aktas, M.: Detecting misinformation in social networks using provenance data. Concurrency Comput. Pract. Experience 31(3), e4793 (2019)","journal-title":"Concurrency Comput. Pract. Experience"},{"issue":"21","key":"20_CR22","doi-asserted-by":"publisher","first-page":"e4690","DOI":"10.1002\/cpe.4690","volume":"30","author":"M Baeth","year":"2018","unstructured":"Baeth, M., Aktas, M.: An approach to custom privacy policy violation detection problems using big social provenance data. Concurrency Comput. Pract. Experience 30(21), e4690 (2018)","journal-title":"Concurrency Comput. Pract. Experience"},{"issue":"3","key":"20_CR23","doi-asserted-by":"publisher","first-page":"e4894","DOI":"10.1002\/cpe.4894","volume":"31","author":"M Riveni","year":"2019","unstructured":"Riveni, M., Nguyen, T., Aktas, M.S., Dustdar, S.: Application of provenance in social computing: a case study. Concurrency Comput. Pract. Experience 31(3), e4894 (2019)","journal-title":"Concurrency Comput. Pract. Experience"}],"container-title":["Lecture Notes in Computer Science","Euro-Par 2020: Parallel Processing Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71593-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T11:41:14Z","timestamp":1619178074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71593-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030715922","9783030715939"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71593-9_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Euro-Par","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Warsaw","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"europar2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2020.euro-par.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"158","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":"39","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":"25% - 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":"4","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":"2.5","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":"The conference was held virtually due to the coronavirus pandemic.","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)"}}]}}