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2008
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| |  | Aigner, Wolfgang | Visual Methods for Analyzing Time-Oriented Data read moreAbstract: Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis.We will discuss three major aspects \— visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations.For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis.We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users. | 2008 |
| | | Hearst, Marti | Tagclouds: Data Analysis tool or Social Signaller? read moreAbstract: Sorry no abstract available for this article | 2008 |
2007
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| | | Leskovec, Jure | Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network read moreAbstract: We present a study of anonymized data capturing high-level communication activities within the Microsoft Instant Messenger network. We analyze properties of the communication network defined by user interactions and demographics, as reported and as derived from one month of data collected in June 2006. The compressed dataset occupies 4.5 terabytes, composed from 1 billion conversations per day (150 gigabytes) over one month of logging. The dataset contains more than 30 billion conversations among 240 million people. We focus on analyses of high-level characteristics and patterns that emerge from the collective dynamics of 240 million people, rather than the actions and characteristics of individuals. Analyses center on numbers and durations of conversations; the content of communications was neither available nor pursued. From the data we construct a communication graph with 190 million nodes and 1.3 billion undirected edges. We find that the graph is well connected, with an effective diameter of 7.8, and is highly clustered, with a clustering coefficient decaying slowly with exponent −0.4. We also find strong influences of homophily in activities, where people with similar characteristics overall tend to communicate more with one another, with the exception of gender, where we find cross-gender conversations are both more frequent and of longer duration than conversations with the same gender. | 2007 |
| | | Heer, Jeffrey | Design Considerations for Collaborative Visual Analytics read moreAbstract: Sorry no abstract available for this article | 2007 |
| | | mackinlay, jock | Show Me: Automatic Presentation for Visual Analysis read moreAbstract: This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users. | 2007 |
| | | Stasko, John | Jigsaw: Supporting Investigative Analysis through Interactive Visualization read moreAbstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents. | 2007 |
| | | Stasko, John | Jigsaw: Supporting Investigative Analysis through Interactive Visualization read moreAbstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents. | 2007 |
| | | Adams, Summer | Making Sense of VAST data read moreAbstract: We view the task of sensemaking in intelligence as that of abducing a story whose plot explains the current data and makes verifiable predictions about the future and the past. We have developed a computational system, called STAB, that abduces stories from data. The story plots in STAB are represented as processes with goals and states, and organized in an abstraction hierarchy. STAB analyzes the VAST dataset generated by PNNL. This dataset pertains to normal and typical activities, as well as illegal and unethical activities, in a fictitious town in the United States. Given the VAST data incrementally, STAB retrieves and invokes multiple story plots as explanatory hypotheses and generates expectations about future data. It uses supporting and contradicting evidence to build justifications for its final conclusions. | 2007 |
| | | Brunsdon, Chris | Geographically Weighted Discriminant Analysis read moreAbstract: Sorry no abstract available for this article | 2007 |
| | | Telles, G. P. | Normalized compression distance for visual analysis of document collections read moreAbstract: In a world flooded by text of various sources, it is of strategic importance to find ways to map information present in written documents in a form that helps users locate and associate important information within a particular text data set. Content-based maps can support extremely useful explorations of text data sets. This paper proposes and evaluates the use of Kolmogorov complexity approximations as a means to detect similarity between general textual documents, in order to support mapping and visualization techniques for corpora exploration. The calculation of this similarity measure requires no intermediate representation of a corpus (such as vector representation) and therefore no pre-processing or parametrization steps. That makes it very attractive for a wider range of exploratory applications compared to conventional measures that need vector-based text representations. The visual layout used here is based on fast distance multi-dimensional projections. It is shown that the similarity measure and the resulting maps present very good precision and that the approach can be used successfully for visual analysis of automatically generated text maps. | 2007 |
| | | Zhu, Weizhong | Storylines: Visual exploration and analysis in latent semantic spaces read moreAbstract: Tasks in visual analytics differ from typical information retrieval tasks in fundamental ways. A critical part of a visual analytics is to ask the right questions when dealing with a diverse collection of information. In this article, we introduce the design and application of an integrated exploratory visualization system called Storylines. Storylines provides a framework to enable analysts visually and systematically explore and study a body of unstructured text without prior knowledge of its thematic structure. The system innovatively integrates latent semantic indexing, natural language processing, and social network analysis. The contributions of the work include providing an intuitive and directly accessible representation of a latent semantic space derived from the text corpus, an integrated process for identifying salient lines of stories, and coordinated visualizations across a spectrum of perspectives in terms of people, locations, and events involved in each story line. The system is tested with the 2006 VAST contest data, in particular, the portion of news articles. | 2007 |
2006
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| | | Bradford, R. B. | Application of Latent Semantic Indexing in Generating Graphs of Terrorist Networks read moreAbstract: Understanding networks of connections among individuals is an important element of counterterrorism analysis. Determining nodes and links for such networks is one of the most labor-intensive aspects of counterterrorism analysis. This paper presents an automated approach for generating and displaying an initial estimate of nodes and links relevant to a chosen topic. This work combines the use of entity extraction and latent semantic indexing (LSI). | 2006 |
2005
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| | | Freeman, Hp | Excess Cervical Cancer Mortality A Marker for Low Access to Health Care in Poor Communities read moreAbstract: Sorry no abstract available for this article | 2005 |
| | | Martins, Bruno | Indexing and ranking in Geo-IR systems read moreAbstract: Sorry no abstract available for this article | 2005 |
2003
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| | | | Interactive maps for visual exploration of grid and vector geodata read moreAbstract: Sorry no abstract available for this article | 2003 |
| | | | Power comparisons for disease clustering tests read moreAbstract: Sorry no abstract available for this article | 2003 |
1992
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| | | Aspinall, Richard | An inductive modelling procedure based on Bayes theorem for analysis of pattern in spatial data read moreAbstract: Sorry no abstract available for this article | 1992 |
1981
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| | | Gould, Peter | Letting the Data Speak for Themselves read moreAbstract: Sorry no abstract available for this article | 1981 |
Undefined
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| | | Palumbo, Francesco | Exploratory data analysis leading towards the most interesting simple association rules read moreAbstract: Association Rules represent one of the most powerful and largely used approaches to detect the presence of regularities and paths in large databases. Rules express the relations (in terms of co-occurence) between pairs of items and are defined in two measures: support and confidence. Most techniques for finding AR scan the whole data set, evaluate all possible rules and retain only rules that have support and confidence greater than thresholds, which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. A multi-step approach aims to the identification of potentially interesting items exploiting well known techniques of multidimensional data analysis. In particular, interesting pairs of items have a well-defined degree of association: an item pair is well defined if its degree of co-occurrence is very high with respect to one or more subsets of the considered set of transactions. | |
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