Efficient summarization framework for multi-attribute uncertain data
Jie Xu, Dmitri V. Kalashnikov, Sharad Mehrotra · 2014
This paper studies the problem of automatically selecting a small subset of representatives from a set of objects, where objects: (a) are multi-attributed with each attribute corresponding to different aspects of the object and (b) are associated with uncertainty -- the problem that has received little attention in the past. Such object set leads to new challenges in modeling information contained in data, defining appropriate criteria for selecting objects, and in devising efficient algorithms for such a selection. We propose a framework that models objects as a set of the corresponding information units and reduces the ummarization problem to that of optimizing probabilistic coverage. To solve the resulting NP-hard problem, we develop a highly efficient greedy algorithm, which gains its efficiency by leveraging object-level and iteration-level optimization. A comprehensive empirical evaluation over three real datasets demonstrates that the proposed framework significantly outperforms baseline techniques in terms of quality and also scales very well against the size of dataset.