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@MastersThesis{danny-msc, 
  author = {Danny Shimoni}, 
  title = {Gathering Data for User-Modeling in Environment of Multiple Users and Multiple Applications},
  school = 	 {{B}ar {I}lan {U}niversity}, 
  year = 	 {2005}, 
  OPTkey = 	 {}, 
  OPTtype = 	 {}, 
  OPTaddress = 	 {}, 
  OPTmonth = 	 {}, 
  note = 	 {(in Hebrew)}, 
  abstract = {
User-Modeling is a research field in artificial intelligence, which deals with
the acquisition and use of a user model, by gathering data about her actions.
Information about a user's profile and her repeated actions can be used for
filtering the information the user gets and to predict the user's next
action. Previous work has a number of assumptions for the data-gathering
process, assumptions which are not practical. For example, an assumption that
the source code of the applications is available for changing, while most
applications do not come with there source code. In addition, previous work
that deals with user modeling typically addressed only the case of a single
user.
In this work we present a new system, TMA (Tracking Multiple Applications and
Multiple Users). The TMA automatically gathers data about the user's actions,
without need for making changes in the source code of the applications. The
data is composed from information about API: Application Programming
Interface functions. The TMA system can monitor every application which works
in Windows environment. The TMA system was built for gathering data in
real-time from numbers of users, thus it contributes for modeling group of
users.
In order to examine the quality of the data which was gathered by the TMA
system we made three experiments. In the first experiment we examined if
sequences of API functions can be used to build scripts which has meaning for
the user's actions. In the second experiment we examined if sequences of API
functions can be used as input for classification algorithms. In the third
experiment we examined if the TMA system can track pairs of users, while
the data of this experiment was used as input for prediction algorithm.
  }, 
  wwwnote = {}, 
 OPTannote = 	 {} 
}