During complex analysis tasks, it can be valuable to maintain a history of the data and reasoning involved - referred to as ‘provenance’ information. Provenance information can be a resource for "reﬂection-in-action" during analysis, supporting collaboration between analysts, and reporting to decision makers. It can also act as a resource after the event, supporting the interpretation of claims, audit, accountability and training.
There has been considerable work on capturing and visualizing of ‘data provenance’, which focuses on data collection and computation, and ‘analytic provenance’, which captures the interactive data exploration and reasoning process. However, there is limited work of utilizing such provenance information to support sensemaking, in terms of improving efficacy and avoiding pitfalls such as issues of data quality and human bias.
This workshop aims to bring together researchers involved in visual analytics and various aspects of sensemaking to consider emerging positions, questions, and ﬁndings related to the capture, processing, representation and use of provenance information to support complex sensemaking tasks. The emphasis is on discussion and collaboration, with a goal of collaboratively producing a paper describing the state-of-the-art of provenance for sensemaking following the workshop.
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Abstract: The unprecedented volume of data acquired by sensors, published in social media, and shared on the Web, opens up new opportunities and has already led to many advances in different domains. But it also creates challenges when it comes to analyzing and extracting knowledge from these data. In this talk, I will discuss the importance of maintaining detailed provenance (also referred to as lineage and pedigree) for digital data as well as for the computational processes used in the data lifecycle. Provenance provides important documentation that is key to preserve data, to determine their quality, reproduce and validate results. It can also be used to streamline the data exploration process. We give an overview of techniques for capturing, managing, and re-using provenance information, and describe emerging applications and novel uses of provenance in collaborative data analysis, teaching science, and publishing reproducible results.
Bio: Juliana Freire is a Professor at the Department of Computer Science and Engineering at New York University. She holds an appointment at the Courant Institute for Mathematical Science, is a faculty member at the NYU Center for Urban Science and at the NYU Center of Data Science, where she is also the Director of Graduate Studies. Before, she was an Associate Professor at the School of Computing, University of Utah; an Assistant Professor at OGI/OHSU; and a member of technical staff at the Database Systems Research Department at Bell Laboratories (Lucent Technologies). An important theme is Professor Freire's work is the development of data management technology to address new problems introduced by emerging applications that manipulate different kinds of data, including urban, scientific and Web data. Her recent research has focused on big-data analysis and visualization, large-scale information integration, provenance management, and computational reproducibiity. Professor Freire is an active member of the database and Web research communities, having co-authored over 140 technical papers, several open-source systems, and holding 10 U.S. patents. She is a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. She has chaired or co-chaired several workshops and conferences, and she has participated as a program committee member in over 70 events. Her research has been funded by grants from the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, the University of Utah, New York University, Microsoft Research, Yahoo! and IBM.
The most part of the workshop will be discussion. Three topics have been selected based on the subissions.
During the break out discussion sessions, three groups will be formed, one for each topic. There will be a chance to change group, if you are interested in more than one topics.
There is one organiser looking after each topic (with their nanme at the end). You can contact the organiser if you have any question or suggestion for that topic. You can find the contact details at the organiser's webpage (the link is under the portrait).
Phong H. Nguyen, Kai Xu, Rick Walker, and B.L. William Wong
Olga Buchel and Tatiana Lukoianova
Ali Sarvghad and Melanie Tory
Ashley J. Wheat, Simon Attfield, and Bob Fields
Dominik Sacha, Hansi Senaratne , Bum Chul Kwon, and Daniel A. Keim
Colin C. Venters, Jim Austin, Charlie E. Dibsdale, Vania Dimitrova, Karim Djemame, Martyn, Fletcher, Sarah Fores, Stephen Hobson, Lydia Lau, John McAvoy, Alison Marshall, Paul, Townend, Nick Taylor, Valentina Viduto, David E. Webster and Jie Xu
We solicit extended abstracts (statements of position or current work). Accepted authors will be asked to be present these as posters at the workshop. The abstracts will be used to stimulate discussion topics. This will form the basis of parallel breakout groups intended to explore and develop the thinking. We aim to collaboratively produce a paper covering these topics following the workshop.
Middlesex University, UK
Middlesex University, UK
Mississippi State University, USA