Today's social platforms, such as Twitter and Facebook, continuously generate massive
volumes of data. The resulting data streams exceed any reasonable limit for permanent …

Massive sizes of real-world graphs, such as social networks and web graph, impose serious
challenges to process and perform analytics on them. These issues can be resolved by …

Table summarization can be of great help, which generates a concise and informative
overview of a table to assist users to understand the table easily and unambiguously. A high …

A valuable small subset strategically selected from massive online reviews is beneficial to
improve consumers' decision-making efficiency in e-commerce. Existing review selection …

In this paper we analyze the robustness of stochastic variants of the greedy algorithm for
submodular maximization. Our main result shows that for maximizing a monotone …

Representative subset selection (RSS) is an important tool for users to draw insights from
massive datasets. Existing literature models RSS as the submodular maximization problem …

Traditional topic models are insufficient for topic extraction in social media. The existing
methods only consider text information or simultaneously model the posts and the static …

Machine learning algorithms must be able to handle large volume in big data. Nowadays,
data science languages such as Python and R, are widely popular to compute machine …

Producing online reputation summaries for an entity (company, brand, etc.) is a focused
summarization task with a distinctive feature: issues that may affect the reputation of the …

The conventional topic models for topic extraction of events on social media are insufficient
due to the data sparsity and the noise of microblog posts. The existing researches use word …