Top-k and Skyline for Cloud Services Research and Selection System

Comparative study of <i>Top<SUB align="right">k</i> based on Fagin's algorithm using correlation metrics in cloud computing QoS

“…Most of the studies aiming to refine the result of skyline find that querying a multidimensional data can lead either to a very large number of responses or to an insufficient number of responses. In both cases, this can confuse the choice of the end user (Idrissi et al, 2016). To deal with these problems, different approaches have been introduced to refine and reduce the size of skyline by selecting the most interesting points.…”

“…In the beginning, we designed an agent that uses both the skyline and outranking formalism to determine which cloud services better meet the user's requirements. The results show the effectiveness of this method for selecting the most dominant objects from a service's database (Abourezq and Idrissi, 2014b;Idrissi et al, 2016). However, the defined skyline returns a set of points that are not dominated by other points in the datasets (Abourezq and Idrissi, 2014a).…”

“…So professionals are not satisfied with the way how to store big data using internet technologies, but they are trying to analyse this data in a way which is subject to real business needs. However, due to a large amount of digital data available, it is crucial to extract the data corresponding to a most recommended need for information, an application must actually express the need of the end user (Kaboré et al, 2015;Idrissi et al, 2016;Karim et al, 2016). It is, therefore, important to classify the data elements according to their importance (Jomsri et al, 2011), and only the best data items are recovered.…”

“…For that reasons, the Top k algorithms have become increasingly used especially in this context of recommendation which requires interaction with the user. The approach represented in this paper is carried out to refine the result found by the skyline approaches which were the main subject of the work exposed in Idrissi and Abourezq (2014) and Idrissi et al (2016). The setting up of this optimisation is then based on the preferences interpretation of the cloud service user, by using multi-criteria analysis methods, and in particular the weighted sum (WS).…”

“…The setting up of this optimisation is then based on the preferences interpretation of the cloud service user, by using multi-criteria analysis methods, and in particular the weighted sum (WS). The injection of multi-criteria decision aiding (MCDA) in the heart of Top k algorithm has given birth to the algorithm called 'Top k weighted sum', or WS k Top (Idrissi et al, 2016). In the beginning, we designed an agent that uses both the skyline and outranking formalism to determine which cloud services better meet the user's requirements.…”