Incremental Discovery of Prominent Situational Facts

TL;DR

This paper introduces a novel method for quickly identifying emerging, prominent situational facts in large, continuously updated datasets, aiding journalists in timely reporting of noteworthy events.

Contribution

It proposes algorithms that efficiently discover contextual skyline tuples representing prominent facts, using tuple reduction, constraint pruning, and shared computation techniques.

Findings

Algorithms outperform brute-force methods in speed and accuracy.

Effective in real datasets for timely fact discovery.

Ranking mechanism helps prioritize the most prominent facts.

Abstract

We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy---e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a "contextual" skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas---tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques.