{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:54:37Z","timestamp":1767084877494,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,10]]},"DOI":"10.1109\/bigdata.2015.7363897","type":"proceedings-article","created":{"date-parts":[[2015,12,28]],"date-time":"2015-12-28T16:36:21Z","timestamp":1451320581000},"page":"1368-1377","source":"Crossref","is-referenced-by-count":14,"title":["Business understanding, challenges and issues of Big Data Analytics for the servitization of a capital equipment manufacturer"],"prefix":"10.1109","author":[{"given":"Mikel","family":"Nino","sequence":"first","affiliation":[]},{"given":"Jose Miguel","family":"Blanco","sequence":"additional","affiliation":[]},{"given":"Arantza","family":"Illarramendi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"34","article-title":"Servitization of capital good manufacturers: an empirical research in Italian machinery sector","author":"adrodegari","year":"2013","journal-title":"Proceedings SOLE 18th International Logistics Symposium"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004320"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2349359"},{"key":"ref13","article-title":"Big Data: Principles and best practices of scalable real-time data systems","author":"marz","year":"2015","journal-title":"Manning Publications"},{"key":"ref14","first-page":"1","article-title":"A survey on platforms for big data analytics","volume":"2","author":"singh","year":"2014","journal-title":"Journal Of Big Data"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/07408170600899532"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2008.4618372"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004327"},{"article-title":"Announcing SparkR: R on Spark","year":"2015","author":"venkataraman","key":"ref18"},{"year":"2013","key":"ref19","article-title":"Project Management Institute, A Guide to the Project Management Body of Knowledge-PMBOK® Guide"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2014.02.001"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1115\/1.2194554"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/jsc.1982"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0263-2373(88)90033-3"},{"key":"ref8","article-title":"Industrial Internet: Pushing the Boundaries of Minds and Machines","author":"evans","year":"2012","journal-title":"General Electric"},{"key":"ref7","article-title":"Design Principles for Industrie 4.0 Scenarios: A Literature Review","author":"hermann","year":"2015","journal-title":"Technische Universität Dortmund"},{"key":"ref2","article-title":"Data Science for Business: What you need to know about data mining and data-analytic thinking","author":"provost","year":"2013","journal-title":"O'Reilly Media"},{"key":"ref1","article-title":"Big Data: The next frontier for innovation, competition and productivity","author":"manyika","year":"2011","journal-title":"McKinsey Global Institute Tech Rep"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004332"},{"key":"ref20","article-title":"CRISP-DM 1.0: Step-by-step data mining guide","author":"chapman","year":"0","journal-title":"SPSS 2000"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004330"},{"key":"ref21","article-title":"CRISP-DM, still the top methodology for analytics, data mining, or data science projects","author":"piatetsky","year":"2014","journal-title":"Kdnuggets com"},{"year":"2013","key":"ref24","article-title":"NIST, NIST Big Data Public Working Group-Uses Cases and Requirements"},{"journal-title":"Dic 2014","article-title":"Udacity","year":"0","key":"ref23"}],"event":{"name":"2015 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2015,10,29]]},"location":"Santa Clara, CA, USA","end":{"date-parts":[[2015,11,1]]}},"container-title":["2015 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7347101\/7363706\/07363897.pdf?arnumber=7363897","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,3,24]],"date-time":"2017-03-24T20:20:58Z","timestamp":1490386858000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7363897\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2015.7363897","relation":{},"subject":[],"published":{"date-parts":[[2015,10]]}}}