  Living in the Information Age allows almost everyone have access to a large
amount of information and options to choose from in order to fulfill their
needs. In many cases, the amount of information available and the rate of
change may hide the optimal and truly desired solution. This reveals the need
of a mechanism that will highlight the best options to choose among every
possible scenario. Based on this the skyline query was proposed which is a
decision support mechanism, that retrieves the valuefor- money options of a
dataset by identifying the objects that present the optimal combination of the
characteristics of the dataset. This paper surveys the state-of-the-art
techniques for skyline query processing, the numerous variations of the initial
algorithm that were proposed to solve similar problems and the
application-specific approaches that were developed to provide a solution
efficiently in each case. Aditionally in each section a taxonomy is outlined
along with the key aspects of each algorithm and its relation to previous
studies.
