Varying Annotations in the Steps of the Visual Analysis (original) (raw)
Related papers
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018
While visual analytics (VA) supports the appraisal of large data amounts, annotations support the amendment of additional information to the VA system. Despite the fact that annotations have occasionally been used to facilitate the analysis, a thorough investigation of annotations themselves is challenging. Although they can represent a suitable way to transfer additional information into the visualization system, there is the need to characterize annotations in order to assure an appropriate use. With our paper we provide a characteristic for annotations, revealing and depicting key issues for the use of annotations. By supplementary fitting our characteristic into the knowledge generation model from Sacha et al. (2014), we provide a systematic view on annotations. We show the general applicability of our characteristic of annotations with a visual analytics approach on medical data in the field of ophthalmology. 2 CHARACTERIZING ANNOTATIONS The term "annotation" is frequently used in literature, yet it is challenging to find a definition or explanatory introduction. There is one definition by (Alm et al., 2015), who declare them as objects (e.g. text snippets, 264
Colvis - A Structured Annotation Acquisition System for Data Visualization
Inf., 2021
Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive “subject-verb-complement” set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of ...
TR-2009005: Visual Analytics: A Multi-Faceted Overview
Visual Analytics (VA) is an emerging field that provides automated analysis of large and complex data sets via interactive visualization systems in an effort to facilitate fruitful decision making. VA is a collaborative process between the human and the machine. In this paper, we present a multi-faceted overview of this human-computer collaboration. The system facet contains everything about the data, analytical tasks, visualization types and the relationships between them. The user facet contains the number and properties of the users. The collaboration facet covers the interactions between the system and the users within the context of VA.
Designing a Classification for User-authored Annotations in Data Visualization
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018
This article introduces a classification system for user-authored annotations in the domain of data visualization. The classification system was created with a bottom-up approach, starting from actual userauthored annotations. To devise relevant dimensions for this classification, we designed a data analysis web platform displaying four visualizations of a common dataset. Using this tool, 16 analysts recorded over 300 annotations that were used to design a classification system. That classification system was then iteratively evaluated and refined until a high inter-coder agreement was found. Use cases for such a classification includes assessing the expressiveness of visualizations on a common ground, based on the types of annotations that are produced with each visualization.
Visual Analytics: A Multifaceted Overview
2009
Visual Analytics (VA) is an emerging field that provides automated analysis of large and complex data sets via interactive visualization systems in an effort to facilitate fruitful decision making. VA is a collaborative process between the human and the machine. In this paper, we present a multi-faceted overview of this human-computer collaboration. The system facet contains everything about the data, analytical tasks, visualization types and the relationships between them. The user facet contains the number and properties of the users. The collaboration facet covers the interactions between the system and the users within the context of VA.
An integrated modular approach for Visual Analytic Systems in Electronic Health Records
International Journal of Advanced Computer Science and Applications, 2012
Latest visual analytic tools help physicians to visualize temporal data in regards to medical health records. Existing systems lack vast support in the generalized collaboration, a single user-centered and task based design for Electronic Health Records (EHR). Already existing frameworks are unable to mentor the interface gaps due to problems like complexity of data sets, increased temporal information density and no support to live databases. These are significant reasons for a single model to comply the end user requirements. We propose an integrated model termed as CARE 1.0 as a future Visual analytic process model for resolving these kinds of issues based on mix method studies. This will base on different disciplines of HCI, Statistics as well as Computer Sciences. This proposed model encompasses the cognitive behavioral requirements of its stake holder's i.e. physicians, database administrators and visualization designers. It helps in presenting a more generalized and detailed visualization for desired medical data sets.
Advanced visual analytics interfaces
Proceedings of the …, 2010
Advanced visual interfaces, like the ones found in information visualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visual representations and providing interactive tools to explore and navigate, it is possible to get an understanding of the data and possibly discover new knowledge. With the advent of modern data collection and analysis technologies, the direct visualization of data starts to show its limitations due to limited scalability in terms of volumes and to the complexity of required analytical reasoning. Many analytical problems we encounter today require approaches that go beyond pure analytics or pure visualization. Visual analytics provides an answer to this problems by advocating a tight integration between automatic computation and interactive visualization, proposing a more holistic approach. In this paper, we argue for Advanced Visual Analytics Interfaces (AVAIs), visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but where the synergy between automation and visualization is in fact advanced. We offer a detailed argumentation around the needs and challenges of AVAIs and provide several examples of this type of interfaces.
The State of the Art of Visual Analytics
2010
One of the critical issues challenging human decision makers in the information age is how to find out data relevant to their tasks and how to derive meaningful information from these data. As a new discipline for dealing with this issue, visual analytics has recently emerged. Visual analytics is defined as the science of analytical reasoning facilitated by interactive visual interfaces. It is a multidisciplinary subject that is related to data mining, information visualization, knowledge science, human factors, and so on. This paper reviews the state of the art of visual analytics and claims that the problems of visual analytics should be considered in the context of human-computer interaction and joint cognitive systems. Based on the review results, this paper proposes a conceptual framework for organizing research problems studied so far and identifying viable future research directions. The author hopes that this paper will provide well-organized information about visual analytics and be a good source for researchers who are interested in this new discipline.
2011 Workshop on Visual Analytics in Healthcare: understanding the physician perspective
ACM SIGHIT Record, 2012
This paper presents a review and summary of the 2011 Workshop on Visual Analytics in Healthcare (VAHC 2011). This was the second annual VAHC workshop and it was held in late October in conjunction with the IEEE VisWeek Conference. The primary goal of the VAHC workshop is to bring together researchers and clinicians to discuss the areas in healthcare that can benefit the most from advances in visualization and analytic systems. This review summarizes the event and provides information on how to access the electronic proceedings. In addition, we seek volunteers who are interested in helping to organize future VAHC workshops.
Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET '11, 2011
With the ever-increasing amount of data, the world has stepped into the era of ''Big Data''. Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.