{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:16:53Z","timestamp":1780607813068,"version":"3.54.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["390621612"],"award-info":[{"award-number":["390621612"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007210","name":"RWTH Aachen University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007210","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Data visualization plays an important role in the analysis of data and the identification of insights and characteristics within the dataset. Current visualization tools suffer from limitations with respect to the number of dimensions that can be displayed simultaneously. This often results in the inability to highlight hidden patterns or trends, affecting the analysts\u2019 decision-making capabilities. In this scenario, it becomes imperative to propose systems that recommend useful insights to guide users to understand and explore data more efficiently. Addressing this problem, we propose a rule-based visualization recommendation(VizRec) system which caters for the research goal of providing an intelligent assistant that can guide users directly to relevant insights in the data. Our proposed VizRec system automates aspects of visualization design and recommends visualizations incorporating both the data characteristics and the diverse tasks representing users\u2019 goals and intents using a knowledge-based rule engine. To ensure the correctness of knowledge-based rules and the dynamic nature of the rule engine, a formalization theory is proposed. We implemented our proposed model into a working tool for the exploration of complex production data generated from the manufacturing processes for the German excellence cluster \u201cInternet of Production\u201d. Our tool was able to generate recommendations capable of visualizing data insights regardless of the domain it is used in. The existing systems face several shortcomings ranging from the number of dimensions they can handle to the limited number of supported visualizations. The recommended visualizations derived from our proposed system were able to mitigate such challenges by adopting a generic rule-based approach incorporating visualizations handling multidimensional data. We evaluated our system in multiple fields, including the engineering domain, where production systems datasets are used to achieve user-intended tasks and gain valuable insights.<\/jats:p>","DOI":"10.1007\/s42979-022-01419-z","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T17:05:50Z","timestamp":1666803950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Monitoring Large Scale Production Processes Using a Rule-Based Visualization Recommendation System"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1930-201X","authenticated-orcid":false,"given":"Arnab","family":"Chakrabarti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhad","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Jarke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Quix","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"1419_CR1","doi-asserted-by":"crossref","unstructured":"Shneiderman, B.: Extreme visualization: squeezing a billion records into a million pixels. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 3\u201312 (2008)","DOI":"10.1145\/1376616.1376618"},{"issue":"4","key":"1419_CR2","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1145\/3092931.3092937","volume":"45","author":"M Vartak","year":"2017","unstructured":"Vartak M, Huang S, Siddiqui T, Madden S, Parameswaran A. Towards visualization recommendation systems. Acm Sigmod Record. 2017;45(4):34\u20139.","journal-title":"Acm Sigmod Record."},{"key":"1419_CR3","doi-asserted-by":"publisher","unstructured":"Vartak, M., Rahman, S., Madden, S., Parameswaran, A., Polyzotis, N.: Seedb. Proceedings of the VLDB Endowment 8(13), 2182\u20132193 (2015). https:\/\/doi.org\/10.14778\/2831360.2831371","DOI":"10.14778\/2831360.2831371"},{"key":"1419_CR4","doi-asserted-by":"publisher","unstructured":"Luo, Y., Qin, X., Tang, N., Li, G.: Deepeye: Towards automatic data visualization. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 101\u2013112. IEEE, ??? (16\/04\/2018 - 19\/04\/2018). https:\/\/doi.org\/10.1109\/ICDE.2018.00019","DOI":"10.1109\/ICDE.2018.00019"},{"key":"1419_CR5","doi-asserted-by":"publisher","unstructured":"Krause, J., Dasgupta, A., Fekete, J.-D., Bertini, E.: Seekaview: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces. In: 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), pp. 11\u201319. IEEE, ??? (23\/10\/2016 - 28\/10\/2016). https:\/\/doi.org\/10.1109\/LDAV.2016.7874305","DOI":"10.1109\/LDAV.2016.7874305"},{"issue":"12","key":"1419_CR6","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/TVCG.2011.229","volume":"17","author":"E Bertini","year":"2011","unstructured":"Bertini E, Tatu A, Keim D. Quality metrics in high-dimensional data visualization: An overview and systematization. IEEE Trans Vis Comput Graphics. 2011;17(12):2203\u201312.","journal-title":"IEEE Trans Vis Comput Graphics"},{"key":"1419_CR7","doi-asserted-by":"crossref","unstructured":"Hu, K., Bakker, M.A., Li, S., Kraska, T., Hidalgo, C.: Vizml: A machine learning approach to visualization recommendation. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1\u201312 (2019)","DOI":"10.1145\/3290605.3300358"},{"key":"1419_CR8","doi-asserted-by":"publisher","unstructured":"Chakrabarti., A., Ahmad., F., Quix., C.: Towards a Rule-based Visualization Recommendation System. In: Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,, pp. 57\u201368. SciTePress, ??? (2021). https:\/\/doi.org\/10.5220\/0010677100003064. INSTICC","DOI":"10.5220\/0010677100003064"},{"key":"1419_CR9","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla J, Ortega F, Hernando A, Guti\u00e9rrez A. Recommender systems survey Knowledge-based systems. 2013;46:109\u201332.","journal-title":"Recommender systems survey. Knowledge-based systems"},{"key":"1419_CR10","doi-asserted-by":"publisher","unstructured":"Kaur, P., Owonibi, M.: A review on visualization recommendation strategies. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 266\u2013273. SCITEPRESS - Science and Technology Publications, ??? (27\/02\/2017 - 01\/03\/2017). https:\/\/doi.org\/10.5220\/0006175002660273","DOI":"10.5220\/0006175002660273"},{"issue":"6","key":"1419_CR11","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TVCG.2007.70594","volume":"13","author":"J Mackinlay","year":"2007","unstructured":"Mackinlay J, Hanrahan P, Stolte C. Show me: automatic presentation for visual analysis. IEEE Trans Visualization Comput Graphics. 2007;13(6):1137\u201344. https:\/\/doi.org\/10.1109\/TVCG.2007.70594.","journal-title":"IEEE Trans Visualization Comput Graphics"},{"issue":"1","key":"1419_CR12","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1109\/TVCG.2016.2599030","volume":"23","author":"A Satyanarayan","year":"2017","unstructured":"Satyanarayan A, Moritz D, Wongsuphasawat K, Heer J. Vega-lite: A grammar of interactive graphics. IEEE Trans Visualization Comput Graphics. 2017;23(1):341\u201350. https:\/\/doi.org\/10.1109\/TVCG.2016.2599030.","journal-title":"IEEE Trans Visualization Comput Graphics"},{"key":"1419_CR13","unstructured":"Klumpar, D., Anderson, K., Simoudis, A.: Rave: Rapid visualization environment (1994)"},{"key":"1419_CR14","doi-asserted-by":"publisher","unstructured":"Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: Proceedings of the 14th International Conference on Intelligent User Interfaces. IUI \u201909, pp. 315\u2013324. ACM, New York, NY, USA (2009). https:\/\/doi.org\/10.1145\/1502650.1502695.","DOI":"10.1145\/1502650.1502695"},{"key":"1419_CR15","doi-asserted-by":"publisher","unstructured":"Key, A., Howe, B., Perry, D., Aragon, C.: Vizdeck: Self-organizing dashboards for visual analytics. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. SIGMOD \u201912, pp. 681\u2013684. ACM, New York, NY, USA (2012). https:\/\/doi.org\/10.1145\/2213836.2213931.","DOI":"10.1145\/2213836.2213931"},{"issue":"4","key":"1419_CR16","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1145\/3092931.3092937","volume":"45","author":"M Vartak","year":"2017","unstructured":"Vartak M, Huang S, Siddiqui T, Madden S, Parameswaran A. Towards visualization recommendation systems. ACM SIGMOD Record. 2017;45(4):34\u20139. https:\/\/doi.org\/10.1145\/3092931.3092937.","journal-title":"ACM SIGMOD Record"},{"key":"1419_CR17","unstructured":"Plotly: Plotly Community Feed. Https:\/\/plot.ly\/feed. https:\/\/plot.ly\/feed Accessed 2019-08-27"},{"issue":"3","key":"1419_CR18","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.visinf.2020.07.002","volume":"4","author":"S Zhu","year":"2020","unstructured":"Zhu S, Sun G, Jiang Q, Zha M, Liang R. A survey on automatic infographics and visualization recommendations. Visual Informatics. 2020;4(3):24\u201340.","journal-title":"Visual Informatics"},{"issue":"2012","key":"1419_CR19","first-page":"1","volume":"2007","author":"J Gantz","year":"2012","unstructured":"Gantz J, Reinsel D. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future. 2012;2007(2012):1\u201316.","journal-title":"IDC iView: IDC Analyze the future"},{"key":"1419_CR20","doi-asserted-by":"crossref","unstructured":"Pennekamp, J., Glebke, R., Henze, M., Meisen, T., Quix, C., Hai, R., Gleim, L., Niemietz, P., Rudack, M., Knape, S., et\u00a0al. Towards an infrastructure enabling the internet of production. In: 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pp. 31\u201337 (2019). IEEE","DOI":"10.1109\/ICPHYS.2019.8780276"},{"key":"1419_CR21","doi-asserted-by":"crossref","unstructured":"Liebenberg, M., Jarke, M.: Information systems engineering with digital shadows: Concept and case studies. In: International Conference on Advanced Information Systems Engineering, pp. 70\u201384 (2020). Springer","DOI":"10.1007\/978-3-030-49435-3_5"},{"issue":"2","key":"1419_CR22","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1145\/2076450.2076473","volume":"55","author":"SS Vempala","year":"2012","unstructured":"Vempala SS. Modeling high-dimensional data: Technical perspective. Commun ACM. 2012;55(2):112\u2013112.","journal-title":"Commun ACM"},{"key":"1419_CR23","unstructured":"React: Https:\/\/reactjs.org\/. https:\/\/reactjs.org\/ Accessed 2020-09-04"},{"key":"1419_CR24","unstructured":"Data-Driven Documents. Https:\/\/d3js.org\/. https:\/\/d3js.org\/ Accessed 2020-09-04"},{"key":"1419_CR25","unstructured":"Material-UI: Https:\/\/material-ui.com\/. https:\/\/material-ui.com\/ Accessed 2020-09-04"},{"key":"1419_CR26","unstructured":"Flask: Https:\/\/flask.palletsprojects.com\/en\/1.1.x\/. https:\/\/flask.palletsprojects.com\/en\/1.1.x\/ Accessed 2020-09-04"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01419-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T22:21:25Z","timestamp":1673130085000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01419-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["1419"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01419-z","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"2 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy\u2014EXC-2023 Internet of Production\u2014390621612.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval:"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate:"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication:"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials:"}},{"value":"Not applicable.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability:"}},{"value":"Not applicable.","order":9,"name":"Ethics","group":{"name":"EthicsHeading","label":"Authors\u2019 contributions:"}}],"article-number":"32"}}