带标记云的协作OLAP：Web 2.0 OLAP形式化和实验评估
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
摘要：
越来越多的商业项目是短暂的。新的商业智能工具必须支持临时数据源和快速浏览。同时，标签云是一种流行的社区驱动的可视化技术。因此，我们研究了支持OLAP操作的标记云视图，如汇总、切片、切片、聚类和下钻。作为一个案例研究，我们实现了一个应用程序，用户可以上传数据并立即浏览其ad hoc维度。为了支持社交网络，视图可以很容易地共享和嵌入其他网站。从数学上讲，我们的标签云视图是自发数据立方体上的近似范围top-k查询。我们目前的实验证据表明，冰山长方体提供足够的在线近似。我们测试了几个浏览器不经意的标签云布局优化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
