History Viewer: Displaying User Interaction History in Visual Analytics Applications

Nowadays, we have access to data of unprecedented volume, high dimensionality, and complexity. To extract novel insights from such complex and dynamic data, we need effective and efficient strategies. One such strategy is to combine data analysis and visualization techniques, which are the essence of visual analytics applications. After the knowledge discovery process, a major challenge is to filter the essential information that has led to a discovery and to communicate the findings to other people, explaining the decisions they may have made based on the data. We propose to record and use the trace left by the exploratory data analysis, in the form of user interaction history, to aid this process. With the trace, users can choose the desired interaction steps and create a narrative, sharing the acquired knowledge with readers. To achieve our goal, we have developed the BONNIE ( Building Online Narratives from Noteworthy Interaction Events ) framework. BONNIE comprises a log model to register the interaction events, auxiliary code to help developers instrument their own code, and an environment to view users’ own interaction history and build narratives. This article presents our proposal for communicating discoveries in visual analytics applications, the BONNIE framework, and the studies we conducted to evaluate our solution. After two user studies (the first one focused on history visualization and the second one focused on narrative creation), our solution has showed to be promising, with mostly positive feedback and results from a Technology Acceptance Model ( TAM ) questionnaire.

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LIVE: A Work-Centered Approach to Support Visual Analytics of Multi-Dimensional Engineering Design Data With Interactive Visualization and Data-Mining

Volume 5: 37th Design Automation Conference, Parts A and B ◽

10.1115/detc2011-48333 ◽

2011 ◽

Cited By ~ 1

Author(s):

Xin Yan ◽

Mu Qiao ◽

Timothy W. Simpson ◽

Jia Li ◽

Xiaolong Luke Zhang

Keyword(s):

Data Analysis ◽

Engineering Design ◽

Visual Analytics ◽

Interactive Visualization ◽

User Interaction ◽

Data Sets ◽

Preliminary Evaluation ◽

Complex Data ◽

Design Data ◽

Depth Analysis

During the process of trade space exploration, information overload has become a notable problem. To find the best design, designers need more efficient tools to analyze the data, explore possible hidden patterns, and identify preferable solutions. When dealing with large-scale, multi-dimensional, continuous data sets (e.g., design alternatives and potential solutions), designers can be easily overwhelmed by the volume and complexity of the data. Traditional information visualization tools have some limits to support the analysis and knowledge exploration of such data, largely because they usually emphasize the visual presentation of and user interaction with data sets, and lack the capacity to identify hidden data patterns that are critical to in-depth analysis. There is a need for the integration of user-centered visualization designs and data-oriented data analysis algorithms in support of complex data analysis. In this paper, we present a work-centered approach to support visual analytics of multi-dimensional engineering design data by combining visualization, user interaction, and computational algorithms. We describe a system, Learning-based Interactive Visualization for Engineering design (LIVE), that allows designer to interactively examine large design input data and performance output data analysis simultaneously through visualization. We expect that our approach can help designers analyze complex design data more efficiently and effectively. We report our preliminary evaluation on the use of our system in analyzing a design problem related to aircraft wing sizing.

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Semiotic Assessment of Visualization Tools

10.20948/graphicon-2021-3027-288-295 ◽

2021 ◽

Author(s):

Alena Zakharova ◽

Aleksey Shklyar ◽

Evgeniya Vekhter

Keyword(s):

Russian Federation ◽

Visual Analytics ◽

Visual Representation ◽

User Interaction ◽

Data Representation ◽

Complete Analysis ◽

Wide Range ◽

Continuous User ◽

Visualization Tools ◽

Selection Of

Semiotic Assessment of Visualization Tools Alena Zakharova 1, Aleksey Shklyar 2 and Evgeniya Vekhter 2 1 Institute of Control Sciences of Russian Academy of Sciences, 65, Profsoyuznaya st., Moscow, 117997, Russian Federation 2 Tomsk Polytechnic University, 30, Lenin Ave., Tomsk, 634050, Russian Federation Abstract A visual analytics means is a set of tools used to obtain a visual representation of the data explored by the user, its interpretation and complete analysis. It is quite possible that such a set will be a developed specialized environment for continuous user interaction with a data stream, which has the capacity to control and replace the way of visual data representation. A wide range of existing visualization tools, as well as unlimited borrowing of technological solutions originally intended for a narrow application area, and the transfer of the capabilities they create to other directions make it urgent to form a sound approach to the comparison and selection of means of data visual representation of a given complexity [1]. The purpose of this selection is to save the end user’s resources, based on the absence or minimization of resource losses arising at the stage of selection and testing of visual analytics tools. The paper shows the possibility of using a semiotic visualization model for purposeful design or selection of visualization tools.

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Survey on the Analysis of User Interactions and Visualization Provenance

10.31219/osf.io/jux76 ◽

2020 ◽

Author(s):

Kai Xu ◽

Alvitta Ottley ◽

Conny Walchshofer ◽

Marc Streit ◽

Remco Chang ◽

...

Keyword(s):

Future Development ◽

Visual Analytics ◽

User Interaction ◽

Evidence Based ◽

User Interactions ◽

Related Research ◽

Current State ◽

Provenance Data ◽

Comprehensive Survey ◽

Evidence Based Guidelines

There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.

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Improving Sequential Recommendation Consistency with Self-Supervised Imitation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/457 ◽

2021 ◽

Author(s):

Xu Yuan ◽

Hongshen Chen ◽

Yonghao Song ◽

Xiaofang Zhao ◽

Zhuoye Ding

Keyword(s):

State Of The Art ◽

User Interaction ◽

Global Perspective ◽

Sparse Learning ◽

Temporal Consistency ◽

Chronological Order ◽

Learning Framework ◽

Real World Datasets ◽

Interaction History ◽

Global And Local

Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential recommender is prone to make inconsistent predictions. In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation. Precisely, we extract the consistency knowledge by utilizing three self-supervised pre-training tasks, where temporal consistency and persona consistency capture user-interaction dynamics in terms of the chronological order and persona sensitivities, respectively. Furthermore, to provide the model with a global perspective, global session consistency is introduced by maximizing the mutual information among global and local interaction sequences. Finally, to comprehensively take advantage of all three independent aspects of consistency-enhanced knowledge, we establish an integrated imitation learning framework. The consistency knowledge is effectively internalized and transferred to the student model by imitating the conventional prediction logit as well as the consistency-enhanced item representations. In addition, the flexible self-supervised imitation framework can also benefit other student recommenders. Experiments on four real-world datasets show that SSI effectively outperforms the state-of-the-art sequential recommendation methods.

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An Analytical Evaluation of a User Interaction History Visualization System Using CDN and PoN

Proceedings of the 15th Brazilian Symposium on Human Factors in Computer Systems - IHC '16 ◽

10.1145/3033701.3033729 ◽

2016 ◽

Author(s):

Vinícius C.V.B. Segura ◽

Juliana J. Ferreira ◽

Renato F. de G. Cerqueira ◽

Simone D.J. Barbosa

Keyword(s):

User Interaction ◽

Analytical Evaluation ◽

Visualization System ◽

Interaction History ◽

History Visualization

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Classification of means and methods of the Web semantic retrieval

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2017.01.030 ◽

2017 ◽

pp. 030-050

Author(s):

J.V. Rogushina ◽

Keyword(s):

Search Engines ◽

Domain Knowledge ◽

Information Needs ◽

Web Search ◽

User Interaction ◽

Query Languages ◽

Semantic Search ◽

Semantic Retrieval ◽

The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

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