Visual Analytics: Scope and Challenges

Keim, Daniel A.; Mansmann, Florian; Schneidewind, Jörn; Thomas, Jim; Ziegler, Hartmut

doi:10.1007/978-3-540-71080-6_6

Cited by 362 publications

(254 citation statements)

References 17 publications

Supporting

Mentioning

222

Contrasting

Unclassified

Order By: Relevance

“…We are currently working on such an extension. Furthermore, we aim to expand our visualization interface to facilitate active exploration of linguistic structures with Brat and thus add to the static display of structures the dimension of Visual Analytics (Keim et al, 2008). …”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

DeNero

Finlayson

Reddy

2015

View full text Add to dashboard Cite

We describe the design, development, and API for two discourse parsers for Rhetorical Structure Theory. The two parsers use the same underlying framework, but one uses features that rely on dependency syntax, produced by a fast shift-reduce parser, whereas the other uses a richer feature space, including both constituent-and dependency-syntax and coreference information, produced by the Stanford CoreNLP toolkit. Both parsers obtain state-of-the-art performance, and use a very simple API consisting of, minimally, two lines of Scala code. We accompany this code with a visualization library that runs the two parsers in parallel, and displays the two generated discourse trees side by side, which provides an intuitive way of comparing the two parsers. IntroductionThis paper describes the design and development of two practical parsers for Rhetorical Structure Theory (RST) discourse (Mann and Thompson, 1988). This work contributes to the already vast body of research on RST parsing (see, inter alia, Soricut and Marcu, 2003; Feng and Hirst, 2012; Joty et al., 2013, Joty et al., 2014) with the following:1. We propose two parsers that use constituentbased and dependency-based syntax, respectively. The underlying framework, other than the syntax-based features, is identical between the parsers, which permits a rigorous analysis of the impact of constituent and dependency syntax to RST parsing. We describe the parsers in Section 2 and empirically compare the impact of the two syntactic representations in Section 3. Our analysis indicates that both parsers achieve state-of-the-art performance. The parser based on dependency syntax performs marginally worse (by 0.1 F 1 points) but runs approximately 2.5 times faster than the parser based on constituent syntax. On average, the faster parser processes one document from the RST corpus in 2.3 seconds. Both parsers have been released as open-sourceScala code with a very simple API; consisting of, minimally, two lines of code. We discuss this API in Section 4.3. We also introduce a visualization tool that runs the two parsers in parallel, and displays the two generated discourse structures side by side. This allows users to directly compare the runtimes and outputs of the two parsers. This visualization tool will be the centerpiece of the proposed demo session. We summarize this tool in Section 5. The Two ParsersThe proposed parsing approach follows the architecture introduced by Hernault et al. (2010), andFeng and Hirst (2012). The parser first segments the text into elementary discourse units (EDUs) using an i.i.d. classifier that identifies which tokens end an EDU. Then the parser iteratively constructs the discourse tree (consisting of binary relations between discourse units) using a greedy bottom-up approach that interleaves two classifiers: the first de-1 tects which two adjacent discourse units are most likely to be connected given the current sequence of units; and the second labels the corresponding relation. The resulting discourse unit produced by the new rel...

show abstract

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

DeNero

Finlayson

Reddy

2015

View full text Add to dashboard Cite

show abstract

“…Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, Jim Thomas, and Hartmut Ziegler [2] here authors descibed the scope of visual analytics and what all the things that visual analytics consists off such as information analytics , geospatial analytics, knowledge discovery etc. Also described the visual analytic process, how the visual analytic work, Also mentioned about the application challenges of the visual analytics.…”

Section: Related Workmentioning

confidence: 99%

DataViz Model: A Novel Approach towards Big Data Analytics and Visualization

More¹,

Goudar²

2017

IJEM

View full text Add to dashboard Cite

Big Data is the collection of large data sets which contains large amount of data. There are different areas which are generating huge data, this data may be present in the form of semi-structured or unstructured and to get useful information from such raw data there is need of data analysis. Due to Big Data's excessive volume, variety, and velocity it is very difficult to store and process huge data. The process of extracting the information from such raw data is called Big Data Analytics. Big data Analytics processes data gives result in the form of structured data. Again this data is huge size and very difficult to understand since it is present in the form of CSV or excel or simple text files. So for effective decision making and to understand the information quickly the data need to be visualized as human mind understands images and graphs better and faster than text data. In this paper a model called Data Visualization (Viz) is designed which integrates big data analytics and the data visualization. This model first takes the data from various sources and then processes it and converts it into structured form, if want this data can be stored to RDBMS. Finally the text result can be visualized with the help of Visualization module of the DataViz. Here text result is represented in the form of charts and graphs.

show abstract

“…Strategies for stratification using prior workloads [10] and methods to increase sensitivity for low-selectivity attributes [54] have also been considered before. Data Interaction: The proliferation of business intelligence tools that leverage visualization and interactive interfaces [4], [6], [15], [28], [57] to explore large multidimensional datasets highly motivate the need for a distributed interactive cube exploration system. Tools such as Tableau [21] translate visual interactions into a series of SQL queries, and interactive loops correlate directly with our session-based model.…”

Section: Related Workmentioning

confidence: 99%

Distributed and interactive cube exploration

Kamat

Jayachandran

Tunga

et al. 2014

2014 IEEE 30th International Conference on Data Engineering

111

View full text Add to dashboard Cite

Abstract-Interactive ad-hoc analytics over large datasets has become an increasingly popular use case. We detail the challenges encountered when building a distributed system that allows the interactive exploration of a data cube. We introduce DICE, a distributed system that uses a novel session-oriented model for data cube exploration, designed to provide the user with interactive sub-second latencies for specified accuracy levels. A novel framework is provided that combines three concepts: faceted exploration of data cubes, speculative execution of queries and query execution over subsets of data. We discuss design considerations, implementation details and optimizations of our system. Experiments demonstrate that DICE provides a subsecond interactive cube exploration experience at the billion-tuple scale that is at least 33% faster than current approaches.

show abstract

Visual Analytics: Scope and Challenges

Cited by 362 publications

References 17 publications

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

DataViz Model: A Novel Approach towards Big Data Analytics and Visualization

Distributed and interactive cube exploration

Contact Info

Product

Resources

About