Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userVisualization by Example
106
0
0.0
(
0
)
Added by
Chenglong Wang
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
While visualizations play a crucial role in gaining insights from data, generating useful visualizations from a complex dataset is far from an easy task. Besides understanding the functionality provided by existing visualization libraries, generating the desired visualization also requires reshaping and aggregating the underlying data as well as composing different visual elements to achieve the intended visual narrative. This paper aims to simplify visualization tasks by automatically synthesizing the required program from simple visual sketches provided by the user. Specifically, given an input data set and a visual sketch that demonstrates how to visualize a very small subset of this data, our technique automatically generates a program that can be used to visualize the entire data set. Automating visualization poses several challenges. First, because many visualization tasks require data wrangling in addition to generating plots, we need to decompose the end-to-end synthesis task into two separate sub-problems. Second, because the intermediate specification that results from the decomposition is necessarily imprecise, this makes the data wrangling task particularly challenging in our context. In this paper, we address these problems by developing a new compositional visualization-by-example technique that (a) decomposes the end-to-end task into two different synthesis problems over different DSLs and (b) leverages bi-directional program analysis to deal with the complexity that arises from having an imprecise intermediate specification. We implemented our visualization-by-example algorithm and evaluate it on 83 visualization tasks collected from on-line forums and tutorials. Viser can solve 84% of these benchmarks within a 600 second time limit, and, for those tasks that can be solved, the desired visualization is among the top-5 generated by Viser in 70% of the cases.
rate research
Read More
The use of adaptive workflow management for in situ visualization and analysis has been a growing trend in large-scale scientific simulations. However, coordinating adaptive workflows with traditional procedural programming languages can be difficult because system flow is determined by unpredictable scientific phenomena, which often appear in an unknown order and can evade event handling. This makes the implementation of adaptive workflows tedious and error-prone. Recently, reactive and declarative programming paradigms have been recognized as well-suited solutions to similar problems in other domains. However, there is a dearth of research on adapting these approaches to in situ visualization and analysis. With this paper, we present a language design and runtime system for developing adaptive systems through a declarative and reactive programming paradigm. We illustrate how an adaptive workflow programming system is implemented using our approach and demonstrate it with a use case from a combustion simulation.
Programming by Example (PBE) is a program synthesis paradigm in which the synthesizer creates a program that matches a set of given examples. In many applications of such synthesis (e.g., program repair or reverse engineering), we are to reconstruct a program that is close to a specific target program, not merely to produce some program that satisfies the seen examples. In such settings, we wish that the synthesized program generalizes well, i.e., has as few errors as possible on the unobserved examples capturing the target function behavior. In this paper, we propose the first framework (called SynGuar) for PBE synthesizers that guarantees to achieve low generalization error with high probability. Our main contribution is a procedure to dynamically calculate how many additional examples suffice to theoretically guarantee generalization. We show how our techniques can be used in 2 well-known synthesis approaches: PROSE and STUN (synthesis through unification), for common string-manipulation program benchmarks. We find that often a few hundred examples suffice to provably bound generalization error below $5%$ with high ($geq 98%$) probability on these benchmarks. Further, we confirm this empirically: SynGuar significantly improves the accuracy of existing synthesizers in generating the right target programs. But with fewer examples chosen arbitrarily, the same baseline synthesizers (without SynGuar) overfit and lose accuracy.
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.
Visual representation of information is a fundamental tool for advancing our understanding of science. It enables the research community to extract new knowledge from complex datasets, and plays an equally vital role in communicating new results across a spectrum of public audiences. Visualizations which make research results accessible to the public have been popularized by the press, and are used in formal education, informal learning settings, and all aspects of lifelong learning. In particular, visualizations of astronomical data (hereafter astrovisualization or astroviz) have broadly captured the human imagination, and are in high demand. Astrovisualization practitioners need a wide variety of specialized skills and expertise spanning multiple disciplines (art, science, technology). As astrophysics research continues to evolve into a more data rich science, astroviz is also evolving from artists conceptions to data-driven visualizations, from two-dimensional images to three-dimensional prints, requiring new skills for development. Currently astroviz practitioners are spread throughout the country. Due to the specialized nature of the field there are seldom enough practitioners at one location to form an effective research group for the exchange of knowledge on best practices and new techniques. Because of the increasing importance of visualization in modern astrophysics, the fact that the astroviz community is small and spread out in disparate locations, and the rapidly evolving nature of this field, we argue for the creation and nurturing of an Astroviz Community of Practice. We first summarize our recommendations. We then describe the current make-up of astrovisualization practitioners, give an overview of the audiences they serve, and highlight technological considerations.
Automatic documentation generation tools, or auto docs, are widely used to visualize information about APIs. However, each auto doc tool comes with its own unique representation of API information. In this paper, I use an information visualization analysis of auto docs to generate potential design principles for improving their usability. Developers use auto docs as a reference by looking up relevant API primitives given partial information, or leads, about its name, type, or behavior. I discuss how auto docs can better support searching and scanning on these leads, e.g. by providing more information-dense visualizations of method signatures.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
