{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T06:35:32Z","timestamp":1768631732153,"version":"3.49.0"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,10,29]],"date-time":"2018-10-29T00:00:00Z","timestamp":1540771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"European Science Foundation via its Research Network Program \u201cEvaluating Information Access Systems\u201d"},{"name":"European Commission via the FP7 project VISCERAL","award":["318068"],"award-info":[{"award-number":["318068"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>Evaluation in empirical computer science is essential to show progress and assess technologies developed. Several research domains such as information retrieval have long relied on systematic evaluation to measure progress: here, the Cranfield paradigm of creating shared test collections, defining search tasks, and collecting ground truth for these tasks has persisted up until now. In recent years, however, several new challenges have emerged that do not fit this paradigm very well: extremely large data sets, confidential data sets as found in the medical domain, and rapidly changing data sets as often encountered in industry. Crowdsourcing has also changed the way in which industry approaches problem-solving with companies now organizing challenges and handing out monetary awards to incentivize people to work on their challenges, particularly in the field of machine learning.<\/jats:p>\n          <jats:p>This article is based on discussions at a workshop on Evaluation-as-a-Service (EaaS). EaaS is the paradigm of not providing data sets to participants and have them work on the data locally, but keeping the data central and allowing access via Application Programming Interfaces (API), Virtual Machines (VM), or other possibilities to ship executables. The objectives of this article are to summarize and compare the current approaches and consolidate the experiences of these approaches to outline the next steps of EaaS, particularly toward sustainable research infrastructures.<\/jats:p>\n          <jats:p>The article summarizes several existing approaches to EaaS and analyzes their usage scenarios and also the advantages and disadvantages. The many factors influencing EaaS are summarized, and the environment in terms of motivations for the various stakeholders, from funding agencies to challenge organizers, researchers and participants, to industry interested in supplying real-world problems for which they require solutions.<\/jats:p>\n          <jats:p>EaaS solves many problems of the current research environment, where data sets are often not accessible to many researchers. Executables of published tools are equally often not available making the reproducibility of results impossible. EaaS, however, creates reusable\/citable data sets as well as available executables. Many challenges remain, but such a framework for research can also foster more collaboration between researchers, potentially increasing the speed of obtaining research results.<\/jats:p>","DOI":"10.1145\/3239570","type":"journal-article","created":{"date-parts":[[2018,10,29]],"date-time":"2018-10-29T12:02:18Z","timestamp":1540814538000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Evaluation-as-a-Service for the Computational Sciences"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0380-6088","authenticated-orcid":false,"given":"Frank","family":"Hopfgartner","sequence":"first","affiliation":[{"name":"University of Sheffield, United Kingdom"}]},{"given":"Allan","family":"Hanbury","sequence":"additional","affiliation":[{"name":"TU Wien, Complexity Science Hub Vienna, Vienna, Austria"}]},{"given":"Henning","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland"}]},{"given":"Ivan","family":"Eggel","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland"}]},{"given":"Krisztian","family":"Balog","sequence":"additional","affiliation":[{"name":"University of Stavanger, Stavanger, Norway"}]},{"given":"Torben","family":"Brodt","sequence":"additional","affiliation":[{"name":"plista GmbH, Berlin Germany"}]},{"given":"Gordon V.","family":"Cormack","sequence":"additional","affiliation":[{"name":"University of Waterloo, Waterloo, Canada"}]},{"given":"Jimmy","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Waterloo, Waterloo, Canada"}]},{"given":"Jayashree","family":"Kalpathy-Cramer","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School, Charlestown, MA USA"}]},{"given":"Noriko","family":"Kando","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Tokyo, Japan"}]},{"given":"Makoto P.","family":"Kato","sequence":"additional","affiliation":[{"name":"Kyoto University, Yoshida Honmachi, Sakyo, Kyoto, Japan"}]},{"given":"Anastasia","family":"Krithara","sequence":"additional","affiliation":[{"name":"National Center for Scientific Research \u201cDemokritos\u201d, Paraskevi, Athens, Greece"}]},{"given":"Tim","family":"Gollub","sequence":"additional","affiliation":[{"name":"Bauhaus-Universit\u00e4t Weimar, Weimar, Germany"}]},{"given":"Martin","family":"Potthast","sequence":"additional","affiliation":[{"name":"Leipzig University, Leipzig, Germany"}]},{"given":"Evelyne","family":"Viegas","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Simon","family":"Mercer","sequence":"additional","affiliation":[{"name":"Independent Consultant"}]}],"member":"320","published-online":{"date-parts":[[2018,10,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jet.2010.09.001"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646031"},{"key":"e_1_2_1_3_1","unstructured":"Michael Arrington. 2006. 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