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Final submissions due: 1 December 2018
Publication date: July/August 2019

Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making.

A recent advance in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps such as unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process.

Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research.

For this special issue, we are soliciting papers that describe innovative research, design, system/tools, and viewpoints regarding the collection, analysis, and summarization of provenance information to support the design and evaluation of novel techniques for sense making across different application domains:

  • Use cases of provenance and logging information, such as:
    1. Supporting sense making;
    2. Understanding user sense making activities and/or evaluation of sense making tools;
    3. Supporting collaborative sense making;
    4. Providing sense making transparency and reproducibility
  • Research related to the challenges in capturing the required provenance information, such as:
    1. The complex provenance information required for different use cases;
    2. Automatic capture of high-level provenance such as human thinking and reasoning;
    3. Software architecture for provenance capture for both new and existing systems.
  • Research related to the analysis and visualization of provenance data, such as:
    1. Visualization and summarization of provenance information;
    2. Machine learning and Nature Language Processing techniques that can help analysis of provenance data.
  • The ethical and privacy implications of the collection, storage, and analysis of provenance data and their impact on the design of the techniques and software.

Submission guidelines
Nondepartment articles submitted to IEEE CG&A should not exceed 8,000 words, including the main text, abstract, keywords, bibliography, biographies, and table text, where a page is approximately 800 words. Articles should include no more than 10 figures or images. Each 1/4 page figure, image, and table counts for approx. 200 words. Note that all tables, images, and illustrations must be appropriately scaled and legible; larger elements should be accounted for accordingly with respect to word count. Please limit the number of references to the most relevant and ensure to delineate your work from relevant past articles in CG&A. Furthermore, avoid an excessive number of references to published work that might only be marginally relevant. Visit the CG&A style, length and supplemental guidelines at www.computer.org/web/peer-review/magazines.

Please submit your paper using the manuscript submission service at https://mc.manuscriptcentral.com/cs-ieee. When uploading your paper, select the appropriate special issue title under the category “Manuscript Type.” Also, include complete contact information for all authors. If you have any questions about submitting your article, contact the peer review coordinator at cga-ma@computer.org.

Guest Editors
Kai Xu (k.xu@mdx.ac.uk), Middlesex University
Melanie Tory (mtory@tableau.com), Tableau ResearchT.J. Jankun-Kelly (tjk@acm.org), Mississippi State University
Jean-Daniel Fekete (Jean-Daniel.Fekete@inria.fr), INRIA Unité de Recherche Saclay