@misc{kim:anomaly,
    author = {Minkyong Kim and David Kotz},
    title = {Tag: Identifying Interesting Days},
    year = {2007},
    month = {September},
    copyright = {the authors},
    howpublished = {In preparation},
    keyword = {wireless network, mobile computing, mobility, location, security,
anomaly detection},
    abstract = {Pervasive applications such as digital memories or patient
monitors collect a vast amount of data. One key challenge in these systems is
how to extract ``interesting" information. Because users cannot anticipate
their future interests in the data when the data is stored, it is hard to
provide appropriate indexes. In this paper, we present an automatic approach
to identify ``interesting" data using users' location information. As
location tracking devices such as GPS are readily available, digital memories
or other pervasive systems may record location information along with the
data. Given the location information, our {\tm Tag} system identifies
``interesting" days. We evaluated {\em Tag} using a real wireless trace and
demonstrated its capabilities. Using {\em Tag}, we were able to identify days
when mobility patterns change and differentiate days when a user follows a
regular pattern from those when she shows a irregular pattern. We also
discovered general mobility characteristics such as most users have one or
more repeating mobility patterns and repeating mobility patterns do not
depend on certain days of the week, except for no activity for weekends.}
}