- From: Nikos Bikakis <bikakis@athenarc.gr>
- Date: Tue, 19 Jan 2021 15:06:25 +0200
- To: undisclosed-recipients: ;
**Deadline Extension** Due to requests the submission deadline has been extended to **January 24, 2021** **Special Issue** Extended versions of the best papers will be invited for submission to a Special Issue of the IEEE Computer Graphics and Applications (CG&A) [pending final decision]. **Special Theme** Machine Learning and Visualization: BigVis 2021 will devote a session to machine learning approaches in the context of Big data visualization and analytics. ------------------------------------------------------------- Call for Papers BigVis 2021: 4th International Workshop on Big Data Visual Exploration and Analytics https://bigvis.imsi.athenarc.gr/bigvis2021 March 23, 2021, Nicosia, Cyprus Held in conjunction with the 24th Intl. Conference on Extending Database Technology & 24th Intl. Conference on Database Theory (EDBT/ICDT 2021) Information Visualization is nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for a great number of users with little or no support and expertise on the data processing part. Thus, the area of data visualization, visual exploration and analysis has gained great attention recently, calling for joint action from different research areas from the HCI, Computer graphics and Data management and mining communities. In this respect, several traditional problems from these communities such as efficient data storage, querying & indexing for enabling visual analytics, new ways for visual presentation of massive data, efficient interaction and personalization techniques that can fit to different user needs are revisited. The modern exploration and visualization systems should nowadays offer scalable techniques to efficiently handle billion objects datasets, limiting the visual response in a few milliseconds along with mechanisms for information abstraction, sampling and summarization for addressing problems related to visual information over-plotting. Further, they must encourage user comprehension offering customization capabilities to different user-defined exploration scenarios and preferences according to the analysis needs. Overall, the challenge is to offer self-service visual analytics, i.e. enable data scientists and business analysts to visually gain value and insights out of the data as rapidly as possible, minimizing the role of IT-expert in the loop. The BigVis workshop aims at addressing the above challenges and issues by providing a forum for researchers and practitioners to discuss, exchange, and disseminate their work. BigVis attempts to attract attention from the research areas of Data Management & Mining, Information Visualization and Human-Computer Interaction and highlight novel works that bridge together these communities. Workshop Topics ------------------------------------ In the context of visual exploration and analytics, topics of interest include, but are not limited to: - Visualization, exploration & analytics techniques for various data types; e.g., stream, spatial, graph - Human -in -the -loop processing - Human -centered databases - Data modeling, storage, indexing, caching, prefetching & query processing for interactive applications - Interactive & human -centered machine learning - Interactive data mining - User -oriented visualization; e.g., recommendation, assistance, personalization - Visualization & knowledge; e.g., storytelling - Progressive analytics - In -situ visual exploration & analytics - Novel interface & interaction paradigms - Visual representation techniques; e.g., aggregation, sampling, multi -level, filtering - Scalable visual operations; e.g., zooming, panning, linking, brushing - Scientific visualization; e.g., volume visualization - Analytics in the fields of scholarly data, digital libraries, multimedia, scientific data, social data, etc. - Immersive visualization - Interactive computer graphics - Setting -oriented visualization; e.g., display resolution/size, smart phones, visualization over networks - High performance, distributed & parallel techniques - Visualization hardware & acceleration techniques - Linked Data & ontologies visualization - Benchmarks for data visualization & analytics - Case & user studies - Systems & tools Special Theme ------------------------------------ ***Machine Learning and Visualization*** BigVis 2021 will devote a session to machine learning approaches in the context of Big data visualization and analytics. Submissions ------------------------------------ Regular/Short Research papers [up to 8/4 pages] Work-in-progress papers [up to 4 pages] Vision papers [up to 4 pages] System papers and Demos [up to 4 pages] For the first time, BigVis will give a Best Paper Award. Best paper will be accompanied with a monetary prize, sponsored by the Visual Facts project. Special Issue ------------------------------------ Extended versions of the best papers will be invited for submission to a Special Issue of the IEEE Computer Graphics and Applications (CG&A) [pending final decision]. Important Dates ------------------------------------ Submission: January 24, 2021 ***extended*** Notification: January 29, 2021 Camera-ready: February 8, 2021 Workshop: March 23, 2021 Organizing Committee ------------------------------------ Nikos Bikakis, ATHENA Research Center, Greece Panos K. Chrysanthis, University of Pittsburgh, USA George Papastefanatos, ATHENA Research Center, Greece Tobias Schreck, Graz University of Technology, Austria Program Committee ------------------------------------ James Abello, Rutgers University, USA Gennady Andrienko, Fraunhofer, Germany Natalia Andrienko, Fraunhofer, Germany Michael Behrisch, Utrecht University, Netherlands Jacob Biehl, University of Pittsburgh, USA Rick Cole, Tableau Alfredo Cuzzocrea, University of Calabria, Italy Ahmed Eldawy, University of California, Riverside, USA Jean-Daniel Fekete, INRIA, France Steffen Frey, University of Stuttgart, Germany Issei Fujishiro, Keio University, Japan Giorgos Giannopoulos, ATHENA Research Center, Greece Parke Godfrey, University of York, Canada Silu Huang, Microsoft Christophe Hurter, Ecole Nationale de l’Aviation Civile, France Halldor Janetzko, Lucerne University of Applied Sciences & Arts, Switzerland Stefan Jänicke, University of Southern Denmark, Denmark Vana Kalogeraki, Athens University of Economics & Business, Greece Eser Kandogan, IBM Anastasios Kementsietsidis, Google James Klosowski, AT&T Research Stavros Maroulis, National Technical University of Athens, Greece Suvodeep Mazumdar, The University of Sheffield, United Kingdom Silvia Miksch, Vienna University of Technology, Austria Davide Mottin, Aarhus University, Denmark Martin Nöllenburg, Vienna University of Technology, Austria Behrooz Omidvar-Tehrani, NAVER LABS Europe, France Jaakko Peltonen, Aalto University & University of Tampere, Finland Laura Po, Unimore, Italy Giuseppe Polese, University of Salerno, Italy Alexander Rind, St. Pölten University of Applied Sciences, Austria Rahman Sajjadur, Megagon Labs Hans-Jörg Schulz, Aarhus University, Denmark Bettina Speckmann, Eindhoven University of Technology, Netherlands Kostas Stefanidis, University of Tampere, Finland Christian Tominski, University of Rostock, Germany Yannis Tzitzikas, University of Crete & FORTH-ICS, Greece Katerina Vrotsou, Linköping University, Sweden Chaoli Wang, University of Notre Dame, USA Junpeng Wang, Visa Research Chen Wei, Zhejiang University, China Yingcai Wu, Zhejiang University, China Jiazhi Xia, Central South University, China Panpan Xu, Bosch Research Hongfeng Yu, University of Nebraska-Lincoln, USA -- Nikos Bikakis ATHENA Research Center Athens | Greece www.nbikakis.com -- Nikos Bikakis ATHENA Research Center Athens | Greece www.nbikakis.com
Received on Tuesday, 19 January 2021 13:06:49 UTC