{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:01:45Z","timestamp":1766268105108},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030414177"},{"type":"electronic","value":"9783030414184"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41418-4_19","type":"book-chapter","created":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T06:14:01Z","timestamp":1582092841000},"page":"279-295","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Provenance Based System for\u00a0Classification and Linear Regression in\u00a0Distributed Machine Learning"],"prefix":"10.1007","author":[{"given":"Muhammad Jahanzeb","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"19_CR1","unstructured":"Big data to turn \u2018mega\u2019 as capacity will hot 44 zettabytes by 2020, DataIQ News, Oct. 2014. \nhttps:\/\/tinyurl.com\/bigdata-hit-44-zettabytes-2020"},{"key":"19_CR2","unstructured":"Apache hadoop. \nhttps:\/\/hadoop.apache.org\/"},{"key":"19_CR3","unstructured":"Elo, A.: The rating of chessplayers past and present. Arco Pub (1978). \nhttps:\/\/books.google.com.au\/books?id=8pMnAQAAMAAJ"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","volume":"5","author":"A L\u2019Heureux","year":"2017","unstructured":"L\u2019Heureux, A., Grolinger, K., Elyamany, H.: Machine learning with big data: challenges and approaches. IEEE Access 5, 7776\u20137797 (2017). \nhttps:\/\/doi.org\/10.1109\/ACCESS.2017.2696365","journal-title":"IEEE Access"},{"key":"19_CR5","unstructured":"Wang, X., Zeng, K., Govindan, K., Mohapatra, P.: Chaining for securing data provenance in distributed information networks. In: MILCOM 2012 - 2012 IEEE Military Communications Conference, Orlando, FL, pp. 1\u20136 (2012)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Wang, R., Sun, D., Li, G., Atif, M., Nepal, S.: LogProv: logging events as provenance of big data analytics pipelines with trustworthiness. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, pp. 1402\u20131411 (2016)","DOI":"10.1109\/BigData.2016.7840748"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Bechhofer, S., Goble, C., Buchan, I.: Research objects: towards exchange and reuse of digital knowledge (2010).(August 2017)","DOI":"10.1038\/npre.2010.4626.1"},{"key":"19_CR8","first-page":"263","volume":"2017","author":"S Xu","year":"2018","unstructured":"Xu, S., Rogers, T., Fairweather, E., Glenn, A., Curran, J., Curcin, V.: Application of data provenance in healthcare analytics software: information visualisation of user activities. AMIA Joint Summits Transl. Sci. Proc. 2017, 263\u2013272 (2018)","journal-title":"AMIA Joint Summits Transl. Sci. Proc."},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Wang, R., Sun, D., Li, G., Wong, R., Chen, S.: Pipeline provenance for cloud-based big data analytics. Softw. Pract. Exper.,1\u201317 (2019). \nhttps:\/\/doi.org\/10.1002\/spe.2744","DOI":"10.1002\/spe.2744"},{"key":"19_CR10","unstructured":"ElasticSearch. \nhttps:\/\/www.elastic.co"},{"key":"19_CR11","unstructured":"Apache Pig. \nhttps:\/\/pig.apache.org"},{"key":"19_CR12","unstructured":"Kaggle Yelp Dataset. \nhttps:\/\/www.kaggle.com\/yelp-dataset\/yelp-dataset\/version\/9"},{"key":"19_CR13","unstructured":"StellarGraph. \nhttps:\/\/www.stellargraph.io\/"},{"key":"19_CR14","unstructured":"PySpark API. \nhttps:\/\/spark.apache.org\/docs\/2.2.1\/api\/python\/pyspark.html"},{"key":"19_CR15","unstructured":"ES-Hadoop. \nhttps:\/\/www.elastic.co\/guide\/en\/elasticsearch\/hadoop\/current\/index.html"},{"key":"19_CR16","first-page":"1","volume-title":"Lecture Notes in Computer Science","author":"Elisa Bertino","year":"2010","unstructured":"Bertino, E., Lim, H.-S.: Assuring data trustworthiness: concepts and research challenges. In: Proceedings of the 7th VLDB Conference on Secure Data Management service, SDM 2010, pp. 1\u201312 (2010)"},{"key":"19_CR17","unstructured":"Schelter, S., Boese, J.H., Kirschnick, J., Klein, T., Seufert, S.: Automatically tracking metadata and provenance of machine learning experiments. In: Machine Learning Systems workshop at NIPS (2017)"},{"key":"19_CR18","unstructured":"Yelper Recommendation System. \nhttp:\/\/tinyurl.com\/yxff5f4r"},{"key":"19_CR19","unstructured":"Yelp Site. \nhttps:\/\/www.yelp.com\/"},{"key":"19_CR20","unstructured":"Log Search. \nhttp:\/\/www.logsearch.io\/blog\/2015\/05\/performance-testing-elasticsearch.html"},{"key":"19_CR21","unstructured":"GraphSAGE: Inductive Representation. \nhttp:\/\/snap.stanford.edu\/graphsage\/"},{"key":"19_CR22","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. \narXiv:1706.02216\n\n [cs.SI] (2017)"},{"key":"19_CR23","unstructured":"Recommender System for Yelp Dataset - Northeastern University. \nwww.ccs.neu.edu\/home\/clara\/resources\/depaoliskaluza_CS6220.pdf"},{"key":"19_CR24","unstructured":"http:\/\/openprovenance.org\/"},{"key":"19_CR25","doi-asserted-by":"publisher","unstructured":"Xing, E.P., et al.: Petuum: a new platform for distributed machine learning on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), pp. 1335\u20131344. ACM, New York (2015). \nhttps:\/\/doi.org\/10.1145\/2783258.2783323","DOI":"10.1145\/2783258.2783323"},{"key":"19_CR26","unstructured":"https:\/\/taverna.incubator.apache.org\/"},{"key":"19_CR27","unstructured":"Dremio. \nhttps:\/\/www.dremio.com"},{"key":"19_CR28","unstructured":"https:\/\/getmanta.com\/"},{"key":"19_CR29","unstructured":"graphLab. \nhttps:\/\/turi.com\/"},{"key":"19_CR30","unstructured":"Tensorflow Fold. \nhttps:\/\/github.com\/tensorflow\/fold"},{"key":"19_CR31","unstructured":"MxNet. \nhttps:\/\/mxnet.apache.org\/"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Bykov, S., Geller, A., Kliot, G., Larus, J.R., Pandya, R., Andthelin, J.: Orleans: cloud computing for everyone. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, p. 16. ACM (2011)","DOI":"10.1145\/2038916.2038932"},{"key":"19_CR33","unstructured":"Akka. \nhttps:\/\/akka.io\/"}],"container-title":["Lecture Notes in Computer Science","Structured Object-Oriented Formal Language and Method"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41418-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T06:15:45Z","timestamp":1582092945000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41418-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414177","9783030414184"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41418-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"20 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOFL+MSVL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Structured Object-Oriented Formal Language and Method","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sofl2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/csse.szu.edu.cn\/icfem2019\/soflmsvl.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}