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
2035953875 204790106 1983936280 2115035636 2030684081
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. We describe a framework for automatically selecting a summary set of photos from a large collection of geo-referenced photographs. Such large collections are inherently difficult to browse, and become excessively so as they grow in size, making summaries an important tool in rendering these collections accessible. Our summary algorithm is based on spa-tial patterns in photo sets, as well as textual-topical patterns and user (photographer) identity cues. The algorithm can be expanded to support social, temporal, and other factors. The summary can thus be biased by the content of the query, the user making the query, and the context in which the query is made.A modified version of our summarization algorithm serves as a basis for a new map-based visualization of large collections of geo-referenced photos, called Tag Maps. Tag Maps visualize the data by placing highly representative textual tags on relevant map locations in the viewed region, effectively providing a sense of the important concepts embodied in the collection.An initial evaluation of our implementation on a set of geo-referenced photos shows that our algorithm and visualization perform well, producing summaries and views that are highly rated by users. 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. We describe a social bookmarking service de-signed for a large enterprise. We discuss design principles addressing online identity, privacy, information discovery (including search and pivot browsing), and service extensi-bility based on a web-friendly architectural style. In addi-tion we describe the key design features of our implementa-tion. We provide the results of an eight week field trial of this enterprise social bookmarking service, including a de-scription of user activities, based on log file analysis. We share the results of a user survey focused on the benefits of the service. The feedback from the user trial, comprising survey results, log file analysis and informal communica-tions, is quite positive and suggests several promising en-hancements to the service. Finally, we discuss potential extension and integration of social bookmarking services with other corporate collaborative applications.