| Abstract | | This demonstration presents Apollo, a new sensor information
processing tool for uncovering likely facts in noisy participatory
sensing data. Participatory sensing, where users proactively
document and share their observations, has received significant
attention in recent years as a paradigm for crowd-sourcing
observation tasks. However, it poses interesting challenges in
assessing confidence in the information received. By borrowing
clustering and ranking tools from data mining literature, we show
how to group data into sets (or claims), corroborating specific
events or observations, then iteratively assess both claim and
source credibility, ultimately leading to a ranking of described
claims by their like-lihoold of occurrence. Apollo belongs to a
category of tools called fact-finders. It is the first fact-finder
designed and implemented specifically for participatory sensing.
Apollo uses Twitter as the underlying engine for sharing
participatory sensing data. Twitter is widely popular, can be
interfaced to cell-phones that share sensor data, and comes with a
powerful search API, as well as a publish-subscribe mechanism. We
evaluate it using a participatory sensing application that collects
and posts noisy vehicular traffic data on Twitter, as well as a set
of 60,000 (human) tweets collected during the Haiti tsunami and a
set of 500,000 tweets collected about Cairo during its recent
unrest. Viewers of the demonstration will interact with Apollo for
various fact-finding tasks. |