5,112 research outputs found
Incremental Discovery of Prominent Situational Facts
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
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which
explores the tremendous data collected by mobile smart devices with prominent
spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing
Systems, temporally recruited mobile users can provide agile, fine-grained, and
economical sensing labors, however their self-interest cannot guarantee the
quality of the sensing data, even when there is a fair return. Therefore, a
mechanism is required for the system server to recruit well-behaving users for
credible sensing, and to stimulate and reward more contributive users based on
sensing truth discovery to further increase credible reporting. In this paper,
we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile
Crowdsensing Systems, which achieves credibility-driven user recruitment and
payback maximization for honest users with quality data. Via theoretical
analysis, we demonstrate the correctness of our design. The performance of our
scheme is evaluated based on extensive realworld trace-driven simulations. Our
evaluation results show that our scheme is proven to be effective in terms of
both guaranteeing sensing accuracy and resisting potential cheating behaviors,
as demonstrated in practical scenarios, as well as those that are intentionally
harsher
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