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Phys. J. Plus"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>HEP-Frame is a new C++ package designed to efficiently perform analyses of datasets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high-performance servers and mini-clusters, and it was designed for natural science researchers with a user-friendly interface to access structured databases. HEP-Frame automatically evaluates the underlying computing resources and builds an adequate code skeleton when creating a data analysis application. At run-time, HEP-Frame analyses a sequence of datasets exploring the available parallelism in the code and hardware resources: it concurrently reads inputs from a user-defined data structure and processes them, following the user-specific sequence of requirements to select relevant data; it manages the efficient execution of that sequence; and it outputs results in user-defined objects (e.g., ROOT structures), stored together with the used input dataset. This paper shows how a domain expert software development can benefit from HEP-Frame, and how it significantly improved the performance of analyses of large datasets produced in proton-proton collisions at the LHC. Two case studies are discussed: the associated production of top quarks together with a Higgs boson (<jats:inline-formula><jats:alternatives><jats:tex-math>$$t\\bar{t}H$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>t<\/mml:mi><mml:mover><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><mml:mrow><mml:mo>\u00af<\/mml:mo><\/mml:mrow><\/mml:mover><mml:mi>H<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>) at the LHC, and a double- and single-top quark productions at the high-luminosity phase of the LHC (HL-LHC). Results show that the HEP-Frame awareness of the analysis code behaviour and structure, and the underlying hardware system, provides powerful and transparent parallelization mechanisms that largely improve the execution time of data analysis applications.<\/jats:p>","DOI":"10.1140\/epjp\/s13360-023-03861-1","type":"journal-article","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T08:02:54Z","timestamp":1679731374000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HEP-Frame: an efficient tool for big data applications at the LHC"],"prefix":"10.1140","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2110-914X","authenticated-orcid":false,"given":"Andr\u00e9","family":"Pereira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3471-2703","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Onofre","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6018-7346","authenticated-orcid":false,"given":"Alberto","family":"Proen\u00e7a","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"key":"3861_CR1","doi-asserted-by":"publisher","first-page":"107844","DOI":"10.1016\/j.cpc.2021.107844","volume":"263","author":"A Pereira","year":"2021","unstructured":"A. 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