{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T21:41:34Z","timestamp":1757540494861,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"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":["Comput Softw Big Sci"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s41781-020-00040-0","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T07:02:22Z","timestamp":1592204542000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics"],"prefix":"10.1007","volume":"4","author":[{"given":"M.","family":"Migliorini","sequence":"first","affiliation":[]},{"given":"R.","family":"Castellotti","sequence":"additional","affiliation":[]},{"given":"L.","family":"Canali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4281-4582","authenticated-orcid":false,"given":"M.","family":"Zanetti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"40_CR1","first-page":"10","volume-title":"Spark: Cluster computing with working sets. In: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing. HotCloud\u201910","author":"M Zaharia","year":"2010","unstructured":"Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: Cluster computing with working sets. In: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing. HotCloud\u201910. USENIX Association, Berkeley, p 10"},{"key":"40_CR2","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1038\/ncomms5308","volume":"5","author":"P Baldi","year":"2014","unstructured":"Baldi P, Sadowski P, Whiteson D (2014) Searching for exotic particles in high-energy physics with deep learning. Nat Commun 5:4308","journal-title":"Nat Commun"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Dai J, Wang Y, Qiu X, Ding D, Zhang Y, Wang Y, Jia X, Zhang C, Wan Y, Li Z, Wang J, Huang S, Wu Z, Wang Y, Yang Y, She B, Shi D, Lu Q, Huang K, Song G (2018) BigDL: a distributed deep learning framework for big data. arXiv e-prints, arXiv:1804.05839","DOI":"10.1145\/3357223.3362707"},{"key":"40_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s41781-019-0028-1","volume":"3","author":"TQ Nguyen","year":"2019","unstructured":"Nguyen TQ, Weitekamp I, Anderson Daniel D, Castello R, Cerri O, Pierini M, Spiropulu M, Vlimant J-R (2019) Topology classification with deep learning to improve real-time event selection at the LHC. Comput Softw Big Sci 3:12","journal-title":"Comput Softw Big Sci"},{"issue":"1","key":"40_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/S0168-9002(97)00048-X","volume":"389","author":"R Brun","year":"1997","unstructured":"Brun R, Rademakers F (1997) Root - an object oriented data analysis framework. Nucl Instrum Methods Phys Res Sect A 389(1):81\u201386 (New Computing Techniques in Physics Research V)","journal-title":"Nucl Instrum Methods Phys Res Sect A"},{"key":"40_CR6","unstructured":"Bird I, Buncic P, Carminati F, Cattaneo M, Clarke P, Fisk I, Girone M, Harvey J, Kersevan B, Mato P, Mount R, Panzer-Steindel B (2014) Update of the computing models of the WLCG and the LHC experiments. Tech Rep CERN-LHCC-2014-014. LCG-TDR-002"},{"key":"40_CR7","unstructured":"Hoecker A, Speckmayer P, Stelzer J, Therhaag J, von Toerne E, Voss H, Backes M, Carli T, Cohen O, Christov A, Dannheim D, Danielowski K, Henrot-Versille S, Jachowski M, Kraszewski K, Krasznahorkay JA, Kruk M, Mahalalel Y, Ospanov R, Prudent X, Robert A, Schouten D, Tegenfeldt F, Voigt A, Voss K, Wolter M, Zemla A (2007) TMVA - toolkit for multivariate data analysis. arXiv e-prints, p. physics\/0703039"},{"key":"40_CR8","first-page":"12","volume":"331","author":"AJ Peters","year":"2011","unstructured":"Peters AJ, Janyst L (2011) Exabyte scale storage at cern. J Phys 331:12","journal-title":"J Phys"},{"key":"40_CR9","unstructured":"Khristenko V, Pivarski J (2017) diana-hep\/spark-root: Release 0.1.14."},{"key":"40_CR10","unstructured":"CERN-DB (2013) Hadoop-XRootD connector. https:\/\/github.com\/cerndb\/hadoop-xrootd"},{"key":"40_CR11","unstructured":"Apache Hadoop project. https:\/\/hadoop.apache.org\/"},{"key":"40_CR12","unstructured":"Apache Parquet. https:\/\/parquet.apache.org\/"},{"key":"40_CR13","unstructured":"Google, Protocol buffers. http:\/\/code.google.com\/apis\/protocolbuffers\/"},{"key":"40_CR14","unstructured":"Scikit-learn. https:\/\/scikit-learn.org\/"},{"key":"40_CR15","unstructured":"Keras tuner. https:\/\/keras-team.github.io\/keras-tuner\/"},{"key":"40_CR16","unstructured":"Kubernetes. https:\/\/kubernetes.io\/"},{"key":"40_CR17","unstructured":"Chollet F et\u00a0al. (2015) Keras. https:\/\/keras.io"},{"key":"40_CR18","unstructured":"CERN openlab. https:\/\/openlab.cern\/"},{"key":"40_CR19","unstructured":"Analytics Zoo. https:\/\/analytics-zoo.github.io\/"},{"key":"40_CR20","unstructured":"TensorFlow. https:\/\/www.tensorflow.org\/"},{"key":"40_CR21","unstructured":"TF-Spawner. https:\/\/github.com\/cerndb\/tf-spawner"},{"key":"40_CR22","unstructured":"Goyal P, Doll\u00e1r P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, Tulloch A, Jia Y, He K (2017) Accurate, large minibatch SGD: training ImageNet in 1 hour. arXiv e-prints arXiv:1706.02677"},{"key":"40_CR23","unstructured":"High Luminosity LHC Project. https:\/\/hilumilhc.web.cern.ch"}],"container-title":["Computing and Software for Big Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-020-00040-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41781-020-00040-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-020-00040-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T23:31:36Z","timestamp":1623713496000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41781-020-00040-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["40"],"URL":"https:\/\/doi.org\/10.1007\/s41781-020-00040-0","relation":{},"ISSN":["2510-2036","2510-2044"],"issn-type":[{"type":"print","value":"2510-2036"},{"type":"electronic","value":"2510-2044"}],"subject":[],"published":{"date-parts":[[2020,6,15]]},"assertion":[{"value":"3 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"8"}}