Abstract:
Real time monitoring of twitter tweet streams
for trends has popularity in the last decade. This
provide effective information for government,
business and other organization to know what
happening right now. The task comprise many
challenges including the processing of large volume
of data in real time and high levels of noise. The
main objective of this work is timely detection of
bursty trends which have happened recently and
discovery of their evolutionary patterns along the
timeline. We present burst detection in adaptive time
windows. It is the task of finding unexpected change
in some quantity in real time tweet stream. Burst is
highly depend on the sampled time window size and
threshold values. So in this work, we describe how to
adjust time windows sizes and threshold values in
real time. Our experimental results show both
processing time is efficient and effectiveness of our
approach.
