Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.
Tag clouds have been popularized by the Web site Flickr launched in 2004. Several optimization opportunities exist: similar tags can be clustered together @cite_6 , tags can be pruned automatically @cite_28 or by user intervention @cite_25 , tags can be indexed @cite_25 , and so on. Tag clouds can be adapted to spatio-temporal data @cite_22 @cite_9 .
@cite_22 @cite_28 @cite_9 @cite_6 @cite_25
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With the emergence of social tagging systems and the possibility for users to extensively annotate web resources and any content enormous amounts of unordered information and user generated metadata circulate the Web. Accordingly a viable visualisation form needs to integrate this unclassified content into meaningful visual representations. We argue that tag clouds can make the grade. We assume that the application of clustering techniques for arranging tags can be a useful method to generate meaningful units within a tag cloud. We think that clustered tag clouds can potentially help to enhance user performance. In this paper we present a description of tag clouds including a theoretical discourse on the strengths and weaknesses of using them in common Web-based contexts. Further recent methods of semantic clustering for visualizing tag clouds are reviewed. Findings from user studies that investigated the visual perception of differently arranged depictions of tags follow. The main objective consists in the exploration of characteristical aspects in perceptual phenomenons and cognitive processes during the interaction with a tag cloud. This clears the way for useful implications on the constitution and design factors of that visualisation form. Finally a new approach is proposed in order to further develop on this concept. Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well. Cloudalicious is an online visualization tool that has been designed to give insight into how ?tag clouds?, or folksonomies, develop over time. A folksonomy is an organic system of text labels attributed to an object by the users of that object. The most common object so far to be the subject of this tagging has been the online bookmark. Stabilization of a URL's tag cloud over time is the clearest result of this type of visualization. Any diagonal movement on the graphs, indicative of a change in the tags being used to describe a URL, should garner further discussion. Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.