{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:20:27Z","timestamp":1763727627345,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T00:00:00Z","timestamp":1574985600000},"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>The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical \u201cbatch\u201d approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM     3    S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM     3    S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform.<\/jats:p>","DOI":"10.3390\/fi11120249","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T10:58:21Z","timestamp":1575025101000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Real-Time Stream Processing in Social Networks with RAM3S"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8074-1129","authenticated-orcid":false,"given":"Ilaria","family":"Bartolini","sequence":"first","affiliation":[{"name":"DISI, University of Bologna, 40100 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2655-0759","authenticated-orcid":false,"given":"Marco","family":"Patella","sequence":"additional","affiliation":[{"name":"DISI, University of Bologna, 40100 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Carrington, P.J., Scott, J., and Wasserman, S. (2005). Models and Methods in Social Network Analysis, Cambridge University Press. Structural Analysis in the Social Sciences.","DOI":"10.1017\/CBO9780511811395"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ijinfomgt.2017.08.003","article-title":"Social Network Analysis: Characteristics of Online Social Networks After a Disaster","volume":"3","author":"Kim","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tang, M., Pongpaichet, S., and Jain, R. (2016, January 15\u201319). Research Challenges in Developing Multimedia Systems for Managing Emergency Situations. Proceedings of the 24th ACM International Conference on Multimedia (ACM MM 2016), Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2976761"},{"key":"ref_4","unstructured":"Liu, B., Messina, E., Fersini, E., and Pozzi, F.A. (2016). Sentiment Analysis in Social Networks, Morgan Kaufmann."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Modoni, G., and Tosi, D. (2016, January 22\u201324). Correlation of Weather and Moods of the Italy Residents through an Analysis of Their Tweets. Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Vienna, Austria.","DOI":"10.1109\/W-FiCloud.2016.53"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1:1","DOI":"10.2196\/publichealth.5059","article-title":"The Measles Vaccination Narrative in Twitter: A Quantitative Analysis","volume":"2","author":"Radzikowski","year":"2016","journal-title":"JMIR Public Health Surveill."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/ACCESS.2014.2332453","article-title":"Toward Scalable Systems for big data Analytics: A Technology Tutorial","volume":"2","author":"Hu","year":"2014","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8:1","DOI":"10.1186\/s40537-014-0008-6","article-title":"A Survey on Platforms for big data Analytics","volume":"2","author":"Singh","year":"2015","journal-title":"J. Big Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00530-017-0566-5","article-title":"A General Framework for Real-time Analysis of Massive Multimedia Streams","volume":"24","author":"Bartolini","year":"2018","journal-title":"Multimed. Syst."},{"key":"ref_10","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., and Stoica, I. (2010, January 22). Spark: Cluster Computing with Working Sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud \u201910), Boston, MA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s00778-014-0357-y","article-title":"The Stratosphere Platform for big data Analytics","volume":"23","author":"Alexandrov","year":"2014","journal-title":"VLDB J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1145\/214451.214456","article-title":"Distributed Snapshots: Determining Global States of Distributed Systems","volume":"3","author":"Chandy","year":"1985","journal-title":"ACM Trans. Comput. Syst."},{"key":"ref_13","unstructured":"Greene, L. (2019, November 29). Face Scans Match Few Suspects. Available online: https:\/\/web.archive.org\/web\/20141130123749\/http:\/\/www.sptimes.com\/News\/021601\/TampaBay\/Face_scans_match_few_.shtml."},{"key":"ref_14","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, USA."},{"key":"ref_15","unstructured":"Turk, M., and Pentland, A.P. (1991, January 3\u20136). Face Recognition Using Eigenfaces. Proceedings of the 1991 Conference on Computer Vision and Pattern Recognition (CVPR 1991), Lahaina, HI, USA."},{"key":"ref_16","unstructured":"Chintapalli, S., Dagit, D., Evans, B., Farivar, R., Graves, T., Holderbaugh, M., Liu, Z., Nusbaum, K., Patil, K., and Jerry Peng, B. (2019, November 29). Benchmarking Streaming Computation Engines at Yahoo! 2015. Available online: http:\/\/yahooeng.tumblr.com\/post\/135321837876\/benchmarking-streaming-computation-engines-at."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wolf, L., Hassner, T., and Maoz, I. (2011, January 20\u201325). Face Recognition in Unconstrained Videos with Matched Background Similarity. Proceedings of the 2011 Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TCSVT.2012.2203741","article-title":"Automatic License Plate Recognition (ALPR): A State-of-the-Art Review","volume":"23","author":"Du","year":"2013","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11042-011-0948-1","article-title":"SHIATSU: Tagging and Retrieving Videos without Worries","volume":"63","author":"Bartolini","year":"2013","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","unstructured":"Kasturi, R., Strayer, S.H., Gargi, U., and Antani, S. (1996, January 22\u201323). An Evaluation of Color Histogram Based Methods in Video Indexing. Proceedings of the First International Workshop on Image Database and Multi-Media Search (IDB-MMS \u201996), Amsterdam, The Netherlands."},{"key":"ref_21","unstructured":"Jacobs, A., Miene, A., Ioannidis, G.T., and Herzog, O. (2004, January 15\u201316). Automatic Shot Boundary Detection Combining Color, Edge, and Motion Features of Adjacent Frames. Proceedings of the TRECVID Workshop (TRECVID 2004), Gaithersburg, MD, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, A., and Grauman, K. (2014, January 23\u201328). Fine-Grained Visual Comparisons with Local Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.32"},{"key":"ref_23","unstructured":"Bartolini, I., Patella, M., and Stromei, G. (2011, January 18\u201321). The Windsurf Library for the Efficient Retrieval of Multimedia Hierarchical Data. Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2011), Seville, Spain."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.14778\/3137765.3137770","article-title":"Samza: Stateful Scalable Stream Processing at LinkedIn","volume":"10","author":"Noghabi","year":"2017","journal-title":"Proc. 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