{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:19:46Z","timestamp":1760242786323,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,7,13]],"date-time":"2016-07-13T00:00:00Z","timestamp":1468368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In the recent years the progress in technology and the increasing availability of fast connections have produced a migration of functionalities in Information Technologies services, from static servers to distributed technologies. This article describes the main tools available on the market to perform Analytics as a Service (AaaS) using a cloud platform. It is also described a use case of IBM Watson Analytics, a cloud system for data analytics, applied to the following research scope: detecting the presence or absence of Heart Failure disease using nothing more than the electrocardiographic signal, in particular through the analysis of Heart Rate Variability. The obtained results are comparable with those coming from the literature, in terms of accuracy and predictive power. Advantages and drawbacks of cloud versus static approaches are discussed in the last sections.<\/jats:p>","DOI":"10.3390\/fi8030032","type":"journal-article","created":{"date-parts":[[2016,7,13]],"date-time":"2016-07-13T09:48:45Z","timestamp":1468403325000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection"],"prefix":"10.3390","volume":"8","author":[{"given":"Gabriele","family":"Guidi","sequence":"first","affiliation":[{"name":"Department of Information Engineering Unversit\u00e0 degli Studi di Firenze, v. S. Marta, 3-50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Miniati","sequence":"additional","affiliation":[{"name":"Department of Information Engineering Unversit\u00e0 degli Studi di Firenze, v. S. Marta, 3-50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Mazzola","sequence":"additional","affiliation":[{"name":"Department of Information Engineering Unversit\u00e0 degli Studi di Firenze, v. S. Marta, 3-50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7291-4990","authenticated-orcid":false,"given":"Ernesto","family":"Iadanza","sequence":"additional","affiliation":[{"name":"Department of Information Engineering Unversit\u00e0 degli Studi di Firenze, v. S. Marta, 3-50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,13]]},"reference":[{"key":"ref_1","unstructured":"Mell, P., Grance, T., and Grance, T. The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology, Available online: http:\/\/nvlpubs.nist.gov\/nistpubs\/Legacy\/SP\/nistspecialpublication800-145.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, X., Gao, B., Fan, L., and An, W. (2012, January 24\u201329). A Cost-Effective Approach to Delivering Analytics as a Service. Proceedings of the 2012 IEEE 19th International Conference on Web Services, Honolulu, HI, USA.","DOI":"10.1109\/ICWS.2012.79"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Barga, R.S., Ekanayake, J., and Lu, W. (2012, January 1\u20135). Project Daytona: Data Analytics as a Cloud Service. Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, Arlington, VA, USA.","DOI":"10.1109\/ICDE.2012.136"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MC.2013.162","article-title":"Clouds for Scalable Big Data Analytics","volume":"46","author":"Talia","year":"2013","journal-title":"Computer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.dss.2012.05.048","article-title":"Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud","volume":"55","author":"Demirkan","year":"2013","journal-title":"Decis. Support. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Q., and Zeller, H. (2011, January 21\u201324). Experience in Continuous analytics as a Service (CaaaS). Proceedings of the 14th International Conference on Extending Database Technology, Uppsala, Sweden.","DOI":"10.1145\/1951365.1951426"},{"key":"ref_7","unstructured":"10 Enterprise Predictive Analytics Platforms Compared. Available online: http:\/\/www.kdnuggets.com\/2013\/08\/10-enterprise-predictive-analytics-platforms-compared.html."},{"key":"ref_8","unstructured":"Enterprise Predictive Analytics Comparisons 2014. Available online:http:\/\/www.butleranalytics.com\/enterprise-predictive-analytics-comparisons-2014\/."},{"key":"ref_9","unstructured":"SAS Analytics Home Page. Available online: Http:\/\/www.sas.com\/en_us\/home.html."},{"key":"ref_10","unstructured":"Gordon, L. (May, January 28). Using Classification and Regression Trees (CART) in SAS \u00ae Enterprise Miner TM For Applications in Public Health. SAS Global Forum\u2014Data Mining and Text Analytics, Proceedings of the SAS Global Forum 2013, SanFrancisco, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19","DOI":"10.7812\/TPP\/11-020","article-title":"Coffee, Caffeine, and Risk of Hospitalization for Arrhythmias","volume":"15","author":"Klatsky","year":"2011","journal-title":"Perm. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Abousalh-Neto, N.A., and Kazgan, S. (2012, January 14\u201319). Big data exploration through visual analytics. Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), Seattle, WA, USA.","DOI":"10.1109\/VAST.2012.6400514"},{"key":"ref_13","unstructured":"IBM Watson Analytics Home Page. Available online: Http:\/\/www.ibm.com\/analytics\/watson-analytics\/."},{"key":"ref_14","unstructured":"IBM Watson Analytics Community Page. Available online: https:\/\/community.watsonanalytics.com\/."},{"key":"ref_15","unstructured":"Watson Analytics Use Case for HR: Retaining Valuable Employees. Available online: https:\/\/www.ibm.com\/blogs\/watson-analytics\/watson-analytics-use-case-for-hr-retaining-valuable-employees\/."},{"key":"ref_16","unstructured":"Watson Analytics Use Case Independence Day Edition: Fireworks and the 4th of July. Available online: http:\/\/www.scoop.it\/t\/gaming-analytics\/p\/4046911509\/2015\/07\/02\/watson-analytics-use-case-independence-day-edition-fireworks-and-the-4th-of-july."},{"key":"ref_17","first-page":"40","article-title":"Using EHRs and Machine Learning for Heart Failure Survival Analysis","volume":"216","author":"Panahiazar","year":"2015","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_18","first-page":"3230","article-title":"Random Forest for Automatic Assessment of Heart Failure Severity in a Telemonitoring Scenario","volume":"2013","author":"Guidi","year":"2013","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1007\/978-3-319-00846-2_335","article-title":"Performance Assessment of a Clinical Decision Support System for analysis of Heart Failure","volume":"41","author":"Guidi","year":"2014","journal-title":"IFMBE Proc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5684","DOI":"10.1016\/j.eswa.2015.01.059","article-title":"Cardiovascular diseases identification using electrocardiogram health identifier based on multiple criteria decision making","volume":"42","author":"Chui","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.procs.2015.08.357","article-title":"M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease","volume":"63","author":"Boursalie","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","first-page":"2210","article-title":"Heart Failure analysis Dashboard for patient\u2019s remote monitoring combining multiple artificial intelligence technologies","volume":"2012","author":"Guidi","year":"2012","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11517-010-0728-5","article-title":"Discrimination power of long-term heart rate variability measures for chronic heart failure detection","volume":"49","author":"Melillo","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TITB.2010.2091647","article-title":"Discrimination power of short-term heart rate variability measures for CHF assessment","volume":"15","author":"Pecchia","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1093\/eurheartj\/ehs104","article-title":"ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart","volume":"33","author":"McMurray","year":"2012","journal-title":"Eur. Heart J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Inglis, S.C., Clark, R.A., McAlister, F.M., Ball, J., Lewinter, C., Cullington, D., Stewart, S., and Cleland, J. (2010). Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Lybrary, 8.","DOI":"10.1002\/14651858.CD007228.pub2"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Takeda, A., Sjc, T., Rs, T., Khan, F., Krum, H., and Underwood, M. (2012). Clinical service organisation for heart failure. Cochrane Database Syst Rev.","DOI":"10.1002\/14651858.CD002752.pub3"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guidi, G., Pollonini, L., Dacso, C.C., and Iadanza, E. (2015). A multi-layer monitoring system for clinical management of Congestive Heart Failure. BMC Med. Inform. Decis. Mak., 15.","DOI":"10.1186\/1472-6947-15-S3-S5"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/978-3-642-23508-5_176","article-title":"An rFId Smart container to perform drugs administration reducing adverse drug events","volume":"37","author":"Iadanza","year":"2011","journal-title":"IFMBE Proc."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/8\/3\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:26:03Z","timestamp":1760210763000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/8\/3\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,13]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["fi8030032"],"URL":"https:\/\/doi.org\/10.3390\/fi8030032","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2016,7,13]]}}}