Show Me: Automatic Presentation for Visual Analysis

Mackinlay, Jock D.; Hanrahan, Patrick; Stolte, Chris

doi:10.1109/tvcg.2007.70594

Cited by 437 publications

(383 citation statements)

References 7 publications

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“…Examination of the data on across each visualization type did show that the Structure + N umericF eatures outperformed the simpler Structure approach for datasets which resulted in visualizations which fall under the category of Tracking Trends in particular when line graphs were generated. This finding highlights the potential for more advanced case representation, possibly combining several other dimensions for comparison as highlighted by [10] such as whether the data is increasing or decreasing, the uniformity of the increase or decrease etc.…”

Section: Discussionmentioning

confidence: 79%

“…With work into creating sophisticated CBR representations planned we can also compare the performance of the CBR method with other classification approaches such as naive bayes, decision trees or neural networks. We also plan to compare our technique to a rule based algorithm [10] to determine its accuracy with its static counterpart.…”

Section: Discussionmentioning

confidence: 99%

“…More recent work aims to discover patterns in user behaviour in preparation of a dataset in order to predict visualization requirements [9]. Finally, closest to our work is work by Mackinlay from 2007 [10] where a rule based approach is taken to visualization recommendation. In that work the characteristics of a dataset including the structure and content determine the type of recommendation presented.…”

Section: Introductionmentioning

confidence: 99%

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Visualization for the Masses: Learning from the Experts

Freyne

Smyth

2010

Case-Based Reasoning. Research and Development

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Abstract. Increasingly, in our everyday lives, we rely on our ability to access and understand complex information. Just as the search engine played a key role in helping people access relevant information, there is evidence that the next generation of information tools will provide users with a greater ability to analyse and make sense of large amounts of raw data. Visualization technologies are set to play an important role in this regard. However, the current generation of visualization tools are simply too complex for the typical user. In this paper we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, ManyEyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations to assist novice users in the selection of appropriate visualization types.

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Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visualization for the Masses: Learning from the Experts

Freyne

Smyth

2010

Case-Based Reasoning. Research and Development

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“…Much work has focused on the automatic generation of visual representations, but not via NL (Feiner, 1985;Roth et al, 1994;Mackinlay et al, 2007). Likewise, much work is devoted to multimodal interaction with visual representations (e.g.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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“…This includes Mackinlay's APT system for automatically generating visualizations based on properties of the data [16], a concept which has recently been incorporated into the commercial software Tableau [17]. Such applications suggest the immediate practicality of creating consistent and precise theories of visual representation.…”

Section: Visual Mappings and Languagesmentioning

confidence: 99%

Embedding Information Visualization within Visual Representation

Ziemkiewicz

Kosara

2009

Advances in Information and Intelligent Systems

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Despite its often technical nature, visualization is in many ways a form of visual representation. Just how visualization relates to illustration, information graphics, digital art, visual languages, etc., is nonetheless poorly understood. We propose a theory that embeds information visualization within other visual traditions in terms of criteria that are not purely technical: dependence on data, mapping, interactivity, and notationality. In addition to providing the means for a classification, these criteria also foster a different understanding of information visualization. We further adapt our criteria to differentiate within visualization, using mapping, readability and information loss, and notationality as the criteria. Both sets of criteria are demonstrated in a number of case studies. We believe that our novel taxonomies of visualization methods serve as a step towards a more comprehensive theoretical context to understanding the essential purposes, properties, and functions of information visualization.

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Show Me: Automatic Presentation for Visual Analysis

Cited by 437 publications

References 7 publications

Visualization for the Masses: Learning from the Experts

Visualization for the Masses: Learning from the Experts

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Embedding Information Visualization within Visual Representation

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