{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:21:22Z","timestamp":1761402082059,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,6,28]],"date-time":"2020-06-28T00:00:00Z","timestamp":1593302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,28]],"date-time":"2020-06-28T00:00:00Z","timestamp":1593302400000},"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":["Prog Artif Intell"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s13748-020-00210-6","type":"journal-article","created":{"date-parts":[[2020,6,28]],"date-time":"2020-06-28T13:02:38Z","timestamp":1593349358000},"page":"239-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An analysis of technological frameworks for data streams"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8746-1077","authenticated-orcid":false,"given":"Fernando","family":"Puentes","sequence":"first","affiliation":[]},{"given":"Mar\u00eda Dolores","family":"P\u00e9rez-Godoy","sequence":"additional","affiliation":[]},{"given":"Pedro","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda Jos\u00e9","family":"Del Jesus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,28]]},"reference":[{"key":"210_CR1","volume-title":"Data Mining Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)","edition":"3"},{"key":"210_CR2","doi-asserted-by":"publisher","DOI":"10.1201\/EBK1439826119","volume-title":"Knowledge Discovery from Data Streams","author":"J Gama","year":"2010","unstructured":"Gama, J.: Knowledge Discovery from Data Streams. Chapman and Hall\/CRC, Boca Raton (2010)"},{"key":"210_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-47534-9","volume-title":"Data Streams: Models and Algorithms","author":"C Aggarwal","year":"2007","unstructured":"Aggarwal, C.: Data Streams: Models and Algorithms, vol. 31. Springer, New York (2007)"},{"key":"210_CR4","doi-asserted-by":"crossref","unstructured":"G\u00fcrcan, F., Berigel, M.: Real-time processing of big data streams: Lifecycle, tools, tasks, and challenges. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-6. IEEE (2018)","DOI":"10.1109\/ISMSIT.2018.8567061"},{"key":"210_CR5","unstructured":"Heudecker, N., Schulte, W. R.: Market guide for event stream processing. id:g00332885. Gartner (2018)"},{"key":"210_CR6","doi-asserted-by":"crossref","unstructured":"Chintapalli, S., Dagit, D., Evans, B., Farivar, R., Graves, T., Holderbaugh, M., Liu, Z., Nusbaum, K., Patil, K., Peng, B. J. et al.: Benchmarking streaming computation engines: storm, flink and spark streaming. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1789-1792. IEEE (2016)","DOI":"10.1109\/IPDPSW.2016.138"},{"issue":"1","key":"210_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12530-016-9168-2","volume":"9","author":"I Khamassi","year":"2018","unstructured":"Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., Gh\u00e9dira, K.: Discussion and review on evolving data streams and concept drift adapting. Evol. Syst. 9(1), 1\u201323 (2018)","journal-title":"Evol. Syst."},{"issue":"4","key":"210_CR8","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., \u017dliobait\u0117, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"210_CR9","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Wo\u017aniak, M.: Ensemble learning for data stream analysis: A survey. Inf. Fusion 37, 132\u2013156 (2017)","journal-title":"Inf. Fusion"},{"key":"210_CR10","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2017.01.078","volume":"239","author":"S Ram\u00edrez-Gallego","year":"2017","unstructured":"Ram\u00edrez-Gallego, S., Krawczyk, B., Garc\u00eda, S., Wo\u017aniak, M., Herrera, F.: A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239, 39\u201357 (2017)","journal-title":"Neurocomputing"},{"key":"210_CR11","unstructured":"Schulte, W. R., Heudecker, N.: Technology insight for event stream processing. id:g00334449. Gartner (2017)"},{"issue":"1","key":"210_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41044-016-0020-2","volume":"2","author":"D Garc\u00eda-Gil","year":"2017","unstructured":"Garc\u00eda-Gil, D., Ram\u00edrez-Gallego, S., Garc\u00eda, S., Herrera, F.: A comparison on scalability for batch big data processing on apache spark and apache flink. Big Data Anal. 2(1), 1 (2017)","journal-title":"Big Data Anal."},{"key":"210_CR13","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.procs.2016.05.322","volume":"80","author":"J Samosir","year":"2016","unstructured":"Samosir, J., Indrawan-Santiago, M., Haghighi, P.D.: An evaluation of data stream processing systems for data driven applications. Proc. Comput. Sci. 80, 439\u2013449 (2016)","journal-title":"Proc. Comput. Sci."},{"key":"210_CR14","unstructured":"Wang, Y.: Stream Processing Systems Benchmark: Streambench (2016)"},{"key":"210_CR15","doi-asserted-by":"crossref","unstructured":"Lu, R., Wu, G., Xie, B., Hu, J.: Stream bench: Towards benchmarking modern distributed stream computing frameworks. In: 2014 IEEE\/ACM 7th International Conference on Utility and Cloud Computing, pp. 69-78. IEEE (2014)","DOI":"10.1109\/UCC.2014.15"},{"key":"210_CR16","unstructured":"Shahverdi, E.: Comparative Evaluation for the Performance of Big Stream Processing Systems. PhD thesis, University of Tartu (2018)"},{"key":"210_CR17","unstructured":"Dasgupta, T.: Evaluation of Two Major Data Stream Processing Technologies. PhD thesis, University of Edinburgh (2016)"},{"key":"210_CR18","doi-asserted-by":"crossref","unstructured":"Karakaya, Z., Yazici, A., Alayyoub, M.: A Comparison of Stream Processing Frameworks. In: 2017 International Conference on Computer and Applications (ICCA), pp. 1-12. IEEE (2017)","DOI":"10.1109\/COMAPP.2017.8079733"},{"key":"210_CR19","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., Maskat, R.: The state of the art and taxonomy of big data analytics: view from new big data framework. In: Artificial Intelligence Review, pp. 1-49 (2019)","DOI":"10.1007\/s10462-019-09685-9"},{"key":"210_CR20","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.inffus.2017.09.005","volume":"41","author":"F Carcillo","year":"2018","unstructured":"Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., Bontempi, G.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Inf. Fusion 41, 182\u2013194 (2018)","journal-title":"Inf. Fusion"},{"key":"210_CR21","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.compeleceng.2017.03.009","volume":"65","author":"LR Nair","year":"2018","unstructured":"Nair, L.R., Shetty, S.D., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Comput. Electr. Eng. 65, 393\u2013399 (2018)","journal-title":"Comput. Electr. Eng."},{"key":"210_CR22","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jpdc.2016.06.004","volume":"108","author":"P Karunaratne","year":"2017","unstructured":"Karunaratne, P., Karunasekera, S., Harwood, A.: Distributed stream clustering using micro-clusters on apache storm. J. Parallel Distrib. Comput. 108, 74\u201384 (2017)","journal-title":"J. Parallel Distrib. Comput."},{"key":"210_CR23","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.ins.2017.11.064","volume":"432","author":"MR Karim","year":"2018","unstructured":"Karim, M.R., Cochez, M., Beyan, O.D., Ahmed, C.F., Decker, S.: Mining maximal frequent patterns in transactional databases and dynamic data streams: a spark-based approach. Inf. Sci. 432, 278\u2013300 (2018)","journal-title":"Inf. Sci."},{"key":"210_CR24","unstructured":"Apache Kafka Project: https:\/\/kafka.apache.org\/documentation\/ (2018). Accessed 10 Jan 2019"},{"key":"210_CR25","unstructured":"Apache Flume Project: https:\/\/flume.apache.org\/documentation.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR26","unstructured":"Apache Nifi Project: https:\/\/nifi.apache.org\/docs.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR27","unstructured":"Apache Spark Streaming Project: https:\/\/spark.apache.org\/docs\/latest\/streaming-programming-guide.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR28","unstructured":"Apache Spark Structured Streaming Project: https:\/\/spark.apache.org\/docs\/latest\/structured-streaming-programming-guide.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR29","unstructured":"Apache Storm Project: http:\/\/storm.apache.org\/releases\/1.2.2\/index.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR30","doi-asserted-by":"crossref","unstructured":"Apache Flink Project: https:\/\/ci.apache.org\/projects\/flink\/flink-docs-release-1.7\/ (2018). Accessed 10 Jan 2019","DOI":"10.1007\/978-3-319-63962-8_303-1"},{"key":"210_CR31","unstructured":"Apache Hadoop YARN Project: https:\/\/hadoop.apache.org\/docs\/current\/hadoop-yarn\/hadoop-yarn-site\/YARN.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR32","doi-asserted-by":"crossref","unstructured":"Apache Samza Project: http:\/\/samza.apache.org\/learn\/documentation\/1.0.0\/core-concepts\/core-concepts.html (2018). Accessed 10 Jan 2019","DOI":"10.1007\/978-3-319-63962-8_197-2"},{"key":"210_CR33","unstructured":"Apache Apex Project: https:\/\/apex.apache.org\/docs.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR34","unstructured":"Apache Beam Project: https:\/\/beam.apache.org\/documentation\/ (2018). Accessed 10 Jan 2019"},{"key":"210_CR35","unstructured":"MLLIB Project: https:\/\/spark.apache.org\/docs\/latest\/mllib-guide.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR36","unstructured":"StreamDM Project: http:\/\/huawei-noah.github.io\/streamDM (2018). Accessed 10 Jan 2019"},{"key":"210_CR37","unstructured":"Apache SAMOA Project: https:\/\/samoa.incubator.apache.org\/documentation\/Home.html (2018). Accessed 10 Jan 2019"},{"key":"210_CR38","unstructured":"Amidst Toolbox Project: http:\/\/www.amidsttoolbox.com\/documentation\/ (2018). Accessed 10 Jan 2019"},{"key":"210_CR39","unstructured":"Yahoo Streaming Benchmark. https:\/\/github.com\/yahoo\/streaming-benchmarks (2018). Accessed 10 Jan 2019"},{"key":"210_CR40","unstructured":"Yahoo Streaming Benchmark Source: https:\/\/github.com\/elkhan-shahverdi\/streaming-benchmarks (2018). Accessed 10 Jan 2019"},{"issue":"May","key":"210_CR41","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Moa: Massive online analysis. J. Mach. Learn. Res. 11(May), 1601\u20131604 (2010)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-020-00210-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13748-020-00210-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-020-00210-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T23:47:05Z","timestamp":1624837625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13748-020-00210-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,28]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["210"],"URL":"https:\/\/doi.org\/10.1007\/s13748-020-00210-6","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"type":"print","value":"2192-6352"},{"type":"electronic","value":"2192-6360"}],"subject":[],"published":{"date-parts":[[2020,6,28]]},"assertion":[{"value":"28 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}