  A fundamental problem in data fusion is to determine the veracity of
multi-source data in order to resolve conflicts. While previous work in truth
discovery has proved to be useful in practice for specific settings, sources'
behavior or data set characteristics, there has been limited systematic
comparison of the competing methods in terms of efficiency, usability, and
repeatability. We remedy this deficit by providing a comprehensive review of 12
state-of-the art algorithms for truth discovery. We provide reference
implementations and an in-depth evaluation of the methods based on extensive
experiments on synthetic and real-world data. We analyze aspects of the problem
that have not been explicitly studied before, such as the impact of
initialization and parameter setting, convergence, and scalability. We provide
an experimental framework for extensively comparing the methods in a wide range
of truth discovery scenarios where source coverage, numbers and distributions
of conflicts, and true positive claims can be controlled and used to evaluate
the quality and performance of the algorithms. Finally, we report comprehensive
findings obtained from the experiments and provide new insights for future
research.
