Karim Benouaret

On the Use of Fuzzy Dominance for Computing Service Skyline Based on QoS

Nowadays, the exploding number of functionally similar Web services has led to a new challenge of selecting the most relevant services using quality of service (QoS) aspects. Traditionally, the relevance of a service is determined by computing an overall score that aggregates individual QoS values. Users are required to assign weights to QoS attributes. This is a rather demanding task and an imprecise specification of the weights could result in missing some user desired services. Recent approaches focus on computing service skyline over a set of QoS aspects. This can completely free users from assigning weights to QoS attributes. However, two main drawbacks characterize such approaches. First, the service skyline often privileges services with a bad compromise between different QoS attributes. Second, as the size of the service skyline may be quite large, users will be overwhelmed during the service selection process. In this paper, we introduce a new concept, called alpha-dominant service skyline, to address the above issues and we develop a suitable algorithm for computing it efficiently. Experimental evaluation conducted on synthetically generated datasets, demonstrates both the effectiveness of the introduced concept and the efficiency of the proposed algorithm.

CrowdSC: Building Smart Cities with Large-Scale Citizen Participation

A platform that connects citizens effectively to local government, letting them contribute to their communitys general well-being, would be an elegant way to make cities smarter. CrowdSC is a crowdsourcing framework designed for smarter cities. The framework lets users combine data collection, selection, and assessment activities in a crowdsourcing process to achieve sophisticated goals within a predefined context. Depending upon this processs execution strategy, different outcomes are possible. The authors describe CrowdSCs process model and evaluate three execution strategies.

Selecting Skyline Web Services from Uncertain QoS

Quality of service (QoS) has been considered as a significant criterion for selecting among functionally similar Web services. Recent approaches focus on computing the skyline over a set of QoS attributes. This can completely free users from assigning weights to QoS attributes. However, these approaches are not sufficient in a dynamic Web service environment where the delivered QoS by a Web service is inherently uncertain. In this paper, we tackle the problem of skyline on uncertain QoS. We represent each QoS attribute of a Web service using a possibility distribution and introduce two skyline extensions on uncertain QoS called pos-dominant skyline and nec-dominant skyline. We then develop appropriate algorithms to efficiently compute both the pos-dominant skyline and nec-dominant skyline. Finally, we present our experimental results that show both the effectiveness of the introduced skyline extensions and the efficiency of the proposed algorithms.

WS-Sky: An Efficient and Flexible Framework for QoS-Aware Web Service Selection

With the development of Service oriented Computing (SOC), more and more functionally similar Web services are deployed over the Web. Quality of service (QoS) aspects (e.g., availability, response time, etc.) are thus crucial for selecting among functionally similar Web services. Moreover, the skyline has been considered as an important concept for selecting Web service based on QoS. In this paper, we propose WS-Sky, a Web service selection framework based on QoS. Our framework leverages two variants of the notion of skyline to effectively and efficiently select Web services that better fit the user needs. Our experimental evaluation on both real-world and synthetic datasets demonstrates that WS-Sky assists users to find the most relevant Web services in a flexible way, and allows users to control the size of the retrieved Web services.

Selecting Skyline Web Services for Multiple Users Preferences

In this paper, we introduce a novel concept called collective skyline to deal with the problem of multiple users preferences. We then conduct a set of experiments that demonstrate the effectiveness of the introduced concept.

A fuzzy framework for efficient user-centric Web service selection

Graphical abstractDisplay Omitted HighlightsThis work falls within Web service query optimization and make use of explicit/implicit preference.An appropriate inference mechanism, borrowed from the fuzzy/approximate reasoning field, is used.A priority-based method is introduced to aggregate the elementary similarities.The top-k results are provided with user.A set of experiments is done to show the feasibility and the effectiveness of our proposal. With the development of Web technologies and the increasing usage of Internet, more and more Web Services (WS) are deployed over Internet. Therefore, there will be a large number of candidate services for fulfilling a desired task. In the last decade, several WS selection approaches are proposed to cope with this challenge. In sharp contrast to the existing WS selection approaches that focus only on user-specified preferences, in this paper, we propose a flexible and effective WS selection framework, which gives users an adequate way to express their preferences using linguistic terms, and enhance the WS selection by leveraging their contexts and profiles. The satisfaction of the candidate WS is expressed by an objective score that takes into consideration no only the user-specified preferences, but also additional preferences extracted from both his/her context and profile using fuzzy inference rules, so as to improve the effectiveness of the selection. We then introduce an effective strategy that allows for priority between the two kinds of preferences, for ranking candidate services. Experimental evaluation on a real case study demonstrates the effectiveness of our proposed strategy.

Web Service Compositions with Fuzzy Preferences: A Graded Dominance Relationship-Based Approach

Data-driven Web services build on service-oriented technologies to provide an interoperable method of interacting with data sources on top of the Web. Data Web services composition has emerged as a flexible solution to answer users’ complex queries on the fly. However, as the number of Web services on the Web grows quickly, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. User preferences are a key factor that can be used to rank candidate services/compositions and retain only the best ones. In this article, we present a novel approach for computing the top-k data service compositions based on user preferences. In our approach, we model user preferences using fuzzy sets and incorporate them into the composition query. We use an efficient RDF query rewriting algorithm to determine the relevant services that may be used to answer the composition query. We match the (fuzzy) constraints of the relevant services to those of the query and determine their matching degrees using a set of matching methods. We then rank-order the candidate services based on a fuzzification of Pareto dominance and compute the top-k data service compositions. In addition, we introduce a new method for increasing the diversity of returned top-k compositions while maintaining as much as possible the compositions with the highest scores. Finally, we describe the architecture of our system and present a thorough experimental study of our proposed techniques and algorithms. The experimental study demonstrates the efficiency and the effectiveness of our techniques in different settings.

Answering fuzzy preference queries over data web services

This paper describes a system that supports preference query answering over a set of data Web services. The proposed system is capable to rank-order the query results in the presence of fuzzy preferences. To do so, we provide different software components organized into two main modules. The first module provides the top-k service compositions. It is mainly based on (i) query rewriting techniques to generate relevant services and compositions, (ii) fuzzy dominance relationship to rank both individual and composite services. The second module adopts a fuzzy database approach to provide a graded service composition execution engine ranking returned data results.

Majority-Rule-Based web service selection

In many Web service selection scenarios, the responsibility to decide which is the appropriate service is shared among multiple parties, e.g., among the department heads of a university. The standard approach is to discard services which are unanimously inappropriate, and return the rest. However, as the involved parties may have conflicting interests, it is possible that only few services are eliminated, and thus almost all discovered services need to be considered. This work addresses this shortcoming, by enforcing the majority rule: a service is discarded if the majority of the parties find it inappropriate. We formulate the majority-rule-based service selection problem based on the notions of dominance and skyline. Furthermore, we propose an algorithm that returns a more manageable set of services, eliminating many inappropriate ones, and is more efficient that standard skyline techniques.

Measuring the Radicalisation Risk in Social Networks

Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnerability factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicators.