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The International Workshop on Big Data Visual Exploration and Analytics will be held in conjunction with the  21th Intl. Conference on Extending Database Technology & 21th Intl. Conference on Database Theory (EDBT/ICDT 2018, March 26-29, Vienna, Austria). 

 

One the major challenges of the Big Data era is that it has realized the availability of a great amount and variety of massive datasets for analysis by non-corporate data analysts, such as research scientists, data journalists, policy makers, SMEs and individuals. A major characteristic of these datasets is that they are: accessible in a raw format that are not being loaded or indexed in a database (e.g., plain text files, json, rdf), dynamic, dirty and heterogeneous in nature. The level of difficulty in transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for numerous non-expert users.

 

 

In the Big Data era, several challenges arise in the field of data visualization and analytics. First, the modern exploration and visualization systems should offer scalable data management techniques in order to efficiently handle billion objects datasets, limiting the system response in a few milliseconds. Besides, nowadays systems must address the challenge of on-the-fly scalable visualizations over large and dynamic sets of volatile raw data, offering efficient interactive exploration techniques, as well as mechanisms for information abstraction, sampling and summarization for addressing problems related to information overplotting. 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 enable users to 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 and Mining, Information Visualization and Human-Computer Interaction and highlight novel works that bridge together these communities.

 

 

We solicit original contributions of research, work-in-progress, vision and demonstration papers addressing the following non-comprehensive list of topics. Selected papers will be invited to the Special Issue "Big Data Exploration, Visualization and Analytics" of the Big Data Research Journal, Elsevier.

 Visualization and exploration techniques for various Big Data types (stream, high-dimensional, graph)

 Human-centered database techniques

 Indexes and data structures for data visualization

 Raw data visual exploration and analytics

 Incremental and adaptive processing

 Interactive caching and prefetching

 Scalable visual operations (zooming, panning, linking, brushing)

 Big Data visual representation techniques (aggregation, sampling, multi-level, filtering)

 Setting-oriented visualization (display resolution, pixel-oriented, visualization over networks)

 User-oriented visualization (assistance, personalization, recommendation)

 Visual analytics (pattern matching, timeseries analytics, prediction analysis, outlier detection, OLAP)

 Visual and interactive data mining  Models of human-in-the-loop data analysis

 High performance/Parallel techniques  Visualization hardware and acceleration techniques

 Linked Data and ontologies visualization

 Systems and tools

 

Organizing Committee:  Nikos Bikakis | George Papastefanatos | Olga Papaemmanouil

 

Submission: December 25, 2017

Notification: January 22, 2018

Camera-ready: January 29, 2018