A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.

Visualization has been successfully applied to analyse time-dependent data for a long time now. Lately, a number of new approaches have been introduced, promising more effective graphs especially for large datasets and multiparameter data. In this paper, we give an overview on the visualization of time-series data and the available techniques. We provide a taxonomy and discuss general aspects of time-dependent data. After an overview on conventional techniques we discuss techniques for analysing time-dependent multivariate data sets in more detail. After this, we give an overview on dynamic presentation techniques and event-based visualization.

Abstract—The NameVoyager, a Web-based visualization of historical trends in baby naming, has proven remarkably popular. We describe design decisions behind the application and lessons learned in creating an application that makes do-it-yourself data mining popular. The prime lesson, it is hypothesized, is that an information visualization tool may be fruitfully viewed not as a tool but as part of an online social environment. In other words, to design a successful exploratory data analysis tool, one good strategy is to create a system that enables “social ” data analysis. We end by discussing the design of an extension of the NameVoyager to a more complex data set, in which the principles of social data analysis played a guiding role. Index Terms—Design study, time-varying data visualization, human-computer interaction, social data analysis. 1

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques that have been developed over the last two decades to support the exploration of large data sets. In this star report, we provide an overview of information visualization and visual data mining techniques, and illustrate them using a few examples.

A number of recent computing applications involve information arriving continuously over time in the form of a data stream, and this has led to new ways of thinking about traditional problems in a variety of areas. In some cases, the rate and overall volume of data in the stream may be so great that it cannot all be stored for processing, and this leads to new requirements for efficiency and scalability. In other cases, the quantities of information may still be manageable, but the data stream perspective takes what has generally been a static view of a problem and adds a strong temporal dimension to it. Our focus here is on some of the challenges that this latter issue raises in the settings of text mining, on-line information, and information retrieval. Many information sources have a stream-like structure, in which the way content arrives over time carries an essential part of its meaning. News coverage is a basic example; understanding the pattern of a developing news

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.

As more people take part in online conversations, awareness of the varying conversational styles and social mores afforded by different software is growing. However, this awareness is largely built on personal impressions as varying styles of social interactions are hard to discover in text-based presentations. Through visualization we explore social and temporal interactions in instant messaging. CrystalChat visualizes personal chat history. Rather than showing online social networks that indicate merely who talks to who, CrystalChat reveals the patterns in an individual’s communications with those people who are part of their personal chat history. The patterns revealed come from instant messaging data that includes information about temporal clustering, conversation initiation, conversation termination, length of conversations, length of postings, patterns of repetitive or alternating postings, and emotional tone as represented by emoticons. communication is less rich than face-to-face conversations, thus, visuals are added to augment the communication itself [11,15,18]. Another reason to use visuals is to provide ways to better understand people’s interactions and the social networks that develop around them [7,10,11,21]. CrystalChat, an interactive visual representation of instant messaging data, falls into this latter category. 1.

This work describes two pixel-based paradigms for visual financial time series data analysis that allow analyzing assets in overview and in detail, and offer improved insights into the characteristics of assets compared to traditional visualization methods. We contribute a two-dimensional color coding scheme for inter-/intra-asset analysis that extends the two paradigms and supports the discovery of significant characteristics of assets, such as atypical ”against-the-market”-behavior in form of exceptional stability in case of whole market losses, or the discovery of assets in a portfolio that- while still being profitable-notably underperform the market median. We apply our techniques on real world data sets, and focus on assets of the banks of Switzerland. 1

The combination of map displays with icon techniques is well-suited for visualizing geospatial dependent multivariate data. The requirements for icon placement on the map, although not trivial, are not properly attended by most interactive visualization systems. In cartography, a number of methods for automatic text label placement and map generalization has been developed. These are focused on high-quality results, leading to long response times not suited for interactive environments. In this paper an interactive, scalable approach borrowing from cartographic methods is presented which is based on an iterative displacement of icons combined with a focus & context technique. 1

In this paper, we have developed a novel framework to enable more effective investigation of large-scale news video database via knowledge visualization. To relieve users from the burdensome exploration of well-known and uninteresting knowledge of news reports, a novel interestingness measurement for video news reports is presented to enable users to find news stories of interest at first glance and capture the relevant knowledge in large-scale video news databases efficiently. Our framework takes advantage of both automatic semantic video analysis and human intelligence by integrating with visualization techniques on semantic video retrieval systems. Our techniques on intelligent news video analysis and knowledge discovery have the capacity to enable more effective visualization and exploration of large-scale news video collections. In addition, news video visualization and exploration can provide valuable feedback to improve our techniques for intelligent news video analysis and knowledge discovery.