{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T13:54:46Z","timestamp":1752674086652,"version":"3.40.3"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031104497"},{"type":"electronic","value":"9783031104503"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10450-3_9","type":"book-chapter","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T06:02:47Z","timestamp":1657778567000},"page":"113-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Effectively and Efficiently Supporting Visual Big Data Analytics over Big Sequential Data: An Innovative Data Science Approach"],"prefix":"10.1007","author":[{"given":"Alfredo","family":"Cuzzocrea","sequence":"first","affiliation":[]},{"given":"Majid Abbasi","family":"Sisara","sequence":"additional","affiliation":[]},{"given":"Carson K.","family":"Leung","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"9_CR1","first-page":"133","volume":"2","author":"IM Anderson-Gr\u00e9goire","year":"2021","unstructured":"Anderson-Gr\u00e9goire, I.M., et al.: A big data science solution for analytics on moving objects. AINA 2, 133\u2013145 (2021)","journal-title":"AINA"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Diallo, A.H., et al.: Proportional visualization of genotypes and phenotypes with rainbow boxes: methods and application to sickle cell disease. IV 2019, Part I, pp. 1\u20136 (2019)","DOI":"10.1109\/IV.2019.00010"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Hamdi, S., et al.: Intra and inter relationships between biomedical signals: a VAR model analysis. IV 2019, Part I, pp. 411\u2013416 (2019)","DOI":"10.1109\/IV.2019.00076"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Pellecchia, M.T., et al.: Identifying correlations among biomedical data through information retrieval techniques. IV 2019, Part I, pp. 269\u2013274 (2019)","DOI":"10.1109\/IV.2019.00052"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Shang, S., et al.: Spatial data science of COVID-19 data. IEEE HPCC- SmartCity-DSS 2020, pp. 1370\u20131375 (2020)","DOI":"10.1109\/HPCC-SmartCity-DSS50907.2020.00177"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Choy, C.M., et al.: Natural sciences meet social sciences: census data analytics for detecting home language shifts. IMCOM 2021, pp. 1\u20138 (2021)","DOI":"10.1109\/IMCOM51814.2021.9377412"},{"key":"9_CR7","first-page":"207","volume":"79","author":"AK Chanda","year":"2017","unstructured":"Chanda, A.K., et al.: A new framework for mining weighted periodic patterns in time series databases. ESWA 79, 207\u2013224 (2017)","journal-title":"ESWA"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Jonker, D., et al.: Industry-driven visual analytics for understanding financial timeseries models. IV 2019, Part I, pp. 210\u2013215 (2019)","DOI":"10.1109\/IV.2019.00043"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Luong, N.N.T., et al.: A visual interactive analytics interface for complex event processing and machine learning processing of financial market data. IV 2020, pp. 189\u2013194 (2020)","DOI":"10.1109\/IV51561.2020.00039"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Morris, K.J., et al.: Token-based adaptive time-series prediction by ensembling linear and non-linear estimators: a machine learning approach for predictive analytics on big stock data. IEEE ICMLA 2018, pp. 1486\u20131491 (2018)","DOI":"10.1109\/ICMLA.2018.00242"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Prokofieva, M.: Visualization of financial data in teaching financial accounting, IV 2020, pp. 674\u2013678 (2020)","DOI":"10.1109\/IV51561.2020.00109"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Barkwell, K.E., et al.: Big data visualisation and visual analytics for music data mining. IV 2018, pp. 235\u2013240 (2018)","DOI":"10.1109\/iV.2018.00048"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lee, W., et al.: Reducing noises for recall-oriented patent retrieval. IEEE BDCloud 2014, pp. 579\u2013586 (2014)","DOI":"10.1109\/BDCloud.2014.14"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: Information technology-based patent retrieval model. Springer Handbook of Science and Technology Indicators, pp. 859\u2013874 (2019)","DOI":"10.1007\/978-3-030-02511-3_34"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Huang, M.L., et al.: Designing infographics\/visual icons of social network by referencing to the design concept of ancient oracle bone characters. IV 2020, pp. 694\u2013699 (2020)","DOI":"10.1109\/IV51561.2020.00120"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, F., et al.: Finding popular friends in social networks. CGC 2012, pp. 501\u2013508 (2012)","DOI":"10.1109\/CGC.2012.99"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Singh, S.P., Leung, C.K.: A theoretical approach for discovery of friends from directed social graphs. IEEE\/ACM ASONAM 2020, pp. 697\u2013701 (2020)","DOI":"10.1109\/ASONAM49781.2020.9381341"},{"key":"9_CR18","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/978-3-030-22354-0_21","volume-title":"Complex, Intelligent, and Software Intensive Systems","author":"A-RA Audu","year":"2020","unstructured":"Audu, A.-R.A., Cuzzocrea, A., Leung, C.K., MacLeod, K.A., Ohin, N.I., Pulgar-Vidal, N.C.: An intelligent predictive analytics system for transportation analytics on open data towards the development of a smart city. In: Barolli, L., Hussain, F.K., Ikeda, M. (eds.) CISIS 2019. AISC, vol. 993, pp. 224\u2013236. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-22354-0_21"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1016\/j.procs.2020.09.202","volume":"176","author":"PPF Balbin","year":"2020","unstructured":"Balbin, P.P.F., et al.: Predictive analytics on open big data for supporting smart transportation services. Procedia Comput. Sci. 176, 3009\u20133018 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: Effective classification of ground transportation modes for urban data mining in smart cities. DaWaK 2018, pp. 83\u201397 (2018)","DOI":"10.1007\/978-3-319-98539-8_7"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: Urban analytics of big transportation data for supporting smart cities. DaWaK 2019, pp. 24\u201333 (2019)","DOI":"10.1007\/978-3-030-27520-4_3"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Shawket, I.M., El khateeb, S.: Redefining urban public space's characters after COVID-19: empirical study on Egyptian residential spaces. IV 2020, pp. 614\u2013619 (2020)","DOI":"10.1109\/IV51561.2020.00107"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Cox, T.S., et al.: An accurate model for hurricane trajectory prediction. IEEE COMPSAC 2018, vol. 2, pp. 534\u2013539 (2018)","DOI":"10.1109\/COMPSAC.2018.10290"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: Explainable machine learning and mining of influential patterns from sparse web. IEEE\/WIC\/ACM WI-IAT 2020, pp. 829\u2013836 (2020)","DOI":"10.1109\/WIIAT50758.2020.00128"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Singh, S.P., et al.: Analytics of similar-sounding names from the web with phonetic based clustering. IEEE\/WIC\/ACM WI-IAT 2020, pp. 580\u2013585 (2020)","DOI":"10.1109\/WIIAT50758.2020.00087"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Dierckens, K.E., et al.: A data science and engineering solution for fast k-means clustering of big data. IEEE TrustCom-BigDataSE-ICESS 2017, pp. 925\u2013932 (2017)","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.332"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Leung, C.K., Jiang, F.: A data science solution for mining interesting patterns from uncertain big data. IEEE BDCloud 2014, pp. 235\u2013242 (2014)","DOI":"10.1109\/BDCloud.2014.136"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Lago, P., et al.: Visualising the structure of 18th century operas: a multidisciplinary data science approach. IV 2020, pp. 530\u2013536 (2020)","DOI":"10.1109\/IV51561.2020.00091"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Alam, M.T., et al.: Mining frequent patterns from hypergraph databases. PAKDD 2021, Part II, pp. 3\u201315 (2021)","DOI":"10.1007\/978-3-030-75765-6_1"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Fariha, A., et al.: Mining frequent patterns from human interactions in meetings using directed acyclic graphs. PAKDD 2013, Part I, pp. 38\u201349 (2013)","DOI":"10.1007\/978-3-642-37453-1_4"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Leung, C.K.: Big data analysis and mining. Encyclop. Inf. Sci. Technol. 4e, 338\u2013348 (2018)","DOI":"10.4018\/978-1-5225-2255-3.ch030"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Leung, C.K.: Uncertain frequent pattern mining. Frequent Pattern Mining, pp. 417\u2013453 (2014)","DOI":"10.1007\/978-3-319-07821-2_14"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Roy, K.K., et al.: Mining sequential patterns in uncertain databases using hierarchical index structure. PAKDD 2021, Part II, pp. 29\u201341 (2021)","DOI":"10.1007\/978-3-030-75765-6_3"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"von Richthofen, A., et al.: Urban mining: visualizing the availability of construction materials for re-use in future cities. IV 2017, pp. 306\u2013311 (2017)","DOI":"10.1109\/iV.2017.34"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Casalino, G., et al.: Incremental and adaptive fuzzy clustering for virtual learning environments data analysis. IV 2020, pp. 382\u2013387 (2020)","DOI":"10.1109\/IV.2019.00071"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Huang, M.L., et al.: Stroke data analysis through a HVN visual mining platform. IV 2019, Part II, pp. 1\u20136 (2019)","DOI":"10.1109\/IV-2.2019.00010"},{"issue":"4","key":"9_CR37","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.3390\/a8041175","volume":"8","author":"F Jiang","year":"2015","unstructured":"Jiang, F., Leung, C.K.: A data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments. Algorithms 8(4), 1175\u20131194 (2015)","journal-title":"Algorithms"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"W. Lee, et al. (eds.): Big Data Analyses, Services, and Smart Data (2021)","DOI":"10.1007\/978-981-15-8731-3"},{"key":"9_CR39","doi-asserted-by":"crossref","unstructured":"Leung, C.K., Jiang, F.: Big data analytics of social networks for the discovery of \u201cfollowing\u201d patterns. DaWaK 2015, pp. 123\u2013135 (2015)","DOI":"10.1007\/978-3-319-22729-0_10"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Afonso, A.P., et al.: RoseTrajVis: visual analytics of trajectories with rose diagrams. IV 2020, pp. 378\u2013384 (2020)","DOI":"10.1109\/IV51561.2020.00067"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Kaupp, L., et al.: An Industry 4.0-ready visual analytics model for context-aware diagnosis in smart manufacturing. IV 2020, pp. 350\u2013359 (2020)","DOI":"10.1109\/IV51561.2020.00064"},{"issue":"2","key":"9_CR42","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/1809400.1809407","volume":"11","author":"CK Leung","year":"2009","unstructured":"Leung, C.K., Carmichael, C.L.: FpVAT: A visual analytic tool for supporting frequent pattern mining. ACM SIGKDD Explor. 11(2), 39\u201348 (2009)","journal-title":"ACM SIGKDD Explor."},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Ma\u00e7\u00e3s, C., et al.: VaBank: visual analytics for banking transactions. IV 2020, pp. 336\u2013343 (2020)","DOI":"10.1109\/IV51561.2020.00062"},{"key":"9_CR44","doi-asserted-by":"crossref","unstructured":"Ahn, S., et al.: A fuzzy logic based machine learning tool for supporting big data business analytics in complex artificial intelligence environments. FUZZ-IEEE 2019, pp. 1259\u20131264 (2019)","DOI":"10.1109\/FUZZ-IEEE.2019.8858791"},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: Big data visualization and visual analytics of COVID- 19 data. IV 2020, pp. 415\u2013420 (2020)","DOI":"10.1109\/IV51561.2020.00073"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Jentner, W., Keim, D.A.: Visualization and visual analytic techniques for patterns. High-Utility Pattern Mining, pp. 303\u2013337 (2019)","DOI":"10.1007\/978-3-030-04921-8_12"},{"key":"9_CR47","unstructured":"Munzner, T., et al.: Visual mining of power sets with large alphabets. Tech. rep. TR-2005\u201325, UBC (2005). https:\/\/www.cs.ubc.ca\/tr\/2005\/tr-2005-25"},{"key":"9_CR48","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: FIsViz: a frequent itemset visualizer. PAKDD 2008, pp. 644\u2013652 (2008)","DOI":"10.1007\/978-3-540-68125-0_60"},{"key":"9_CR49","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: PyramidViz: visual analytics and big data visualization of frequent patterns. IEEE DASC-PICom-DataCom- CyberSciTech 2016, pp. 913\u2013916 (2016)","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.158"},{"key":"9_CR50","doi-asserted-by":"crossref","unstructured":"Leung, C.K., et al.: FpMapViz: a space-filling visualization for frequent patterns. IEEE ICDM 2011 Workshops, pp. 804\u2013811 (2011)","DOI":"10.1109\/ICDMW.2011.86"},{"issue":"1","key":"9_CR51","first-page":"532","volume":"24","author":"BCM Cappers","year":"2018","unstructured":"Cappers, B.C.M., van Wijk, J.J.: Exploring multivariate event sequences using rules, aggregations, and selections. IEEE TVCG 24(1), 532\u2013541 (2018)","journal-title":"IEEE TVCG"},{"key":"9_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, J., et al.: MatrixWave: visual comparison of event sequence data. ACM CHI 2015, pp. 259\u2013268 (2015)","DOI":"10.1145\/2702123.2702419"},{"issue":"1","key":"9_CR53","first-page":"45","volume":"24","author":"Y Chen","year":"2018","unstructured":"Chen, Y., et al.: Sequence synopsis: optimize visual summary of temporal event data. IEEE TVCG 24(1), 45\u201355 (2018)","journal-title":"IEEE TVCG"},{"issue":"12","key":"9_CR54","first-page":"1653","volume":"20","author":"CD Stolper","year":"2014","unstructured":"Stolper, C.D., et al.: Progressive visual analytics: user-driven visual exploration of in-progress analytics. IEEE TVCG 20(12), 1653\u20131662 (2014)","journal-title":"IEEE TVCG"},{"key":"9_CR55","unstructured":"Jentner, W., et al.: Feature alignment for the analysis of verbatim text transcripts. EuroVis 2017 Workshop on EuroVA, pp. 13\u2013 18 (2017)"},{"issue":"3\/4","key":"9_CR56","first-page":"301","volume":"4","author":"A Cuzzocrea","year":"2009","unstructured":"Cuzzocrea, A., et al.: Fragmenting very large XML data warehouses via K-means clustering algorithm. Int. J. Bus. Intell. Data Min. 4(3\/4), 301\u2013328 (2009)","journal-title":"Int. J. Bus. Intell. Data Min."},{"issue":"3","key":"9_CR57","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s10844-013-0268-1","volume":"44","author":"M Ceci","year":"2013","unstructured":"Ceci, M., Cuzzocrea, A., Malerba, D.: Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering. J. Intell. Inf. Syst. 44(3), 309\u2013333 (2013). https:\/\/doi.org\/10.1007\/s10844-013-0268-1","journal-title":"J. Intell. Inf. Syst."},{"key":"9_CR58","doi-asserted-by":"crossref","unstructured":"Bellatreche, L., et al.: F&A: a methodology for effectively and efficiently designing parallel relational data warehouses on heterogenous database clusters. DaWak 2010, pp. 89\u2013104 (2010)","DOI":"10.1007\/978-3-642-15105-7_8"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10450-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T03:25:49Z","timestamp":1727580349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10450-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031104497","9783031104503"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10450-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"279","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"285 Workshop submission accepted out of 815 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}