Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

TL;DR

This paper explores integrating tag clouds with OLAP operations to support quick, ad hoc data analysis and sharing in Web 2.0 environments, demonstrating effective approximations and layout optimizations.

Contribution

It introduces a formalism for tag-cloud based OLAP views supporting key operations and provides experimental evaluation of algorithms and layout techniques for social, web-based BI.

Findings

Iceberg cuboids offer effective online approximations.

Tag-cloud views can perform approximate range top-k queries.

Layout optimizations improve visualization performance.

Abstract

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.