Katia Abbaci

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Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as process model). However, these approaches still remain with a high selectivity rate, resulting in a large number of services o ering similar functionalities and behavior. One way to improve the selectivity rate and to provide the(More)
One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled(More)
Skyline queries are a popular and powerful paradigm for extracting interesting objects from a d-dimensional dataset. They rely on Pareto dominance principle to identify the skyline objects, i.e., the set of incomparable objects which are not dominated by any other object from the dataset. Two main problems may be faced when using skyline queries: (i) a(More)
In this paper, we propose a novel approach for service retrieval that takes into account the service behavior (described as process model) and relies both on preference satisfiability and structural similarity. User query and target process models are represented as annotated graphs, where user preferences on QoS (Quality of Service) attributes (such as(More)
Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as a process model). However, these approaches have high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate is to cope with user preferences(More)
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