Yanglan Gan

An Ontology-Based Methodology for Semantic Expansion Search

Matching search technology based on query keyword has been widely used by traditional search way. It still belongs to pure keyword matching and can not acquire satisfactory search results. The essential reason is that traditional Web search lacks semantic understanding to users search behaviors. In this study, we propose a novel ontology-based framework for semantic expansion search. Based on constructed domain ontology, semantic annotation algorithm and semantic expansion reasoning algorithm are presented in detail, which are associated with semantic annotation unit and semantic expansion reasoning engine respectively. Then a semantic search prototype system is designed and implemented. The experimental results show that semantic expansion search by proposed methodology can overcome limitations in comparison with traditional keyword search mode, and achieve higher recall ratio and precision ratio.

Dynamic composition of web services using efficient planners in large-scale service repository

Web services as independent software components are published by service providers over the Internet and invoked by service requesters for their desired functionalities. In many cases, however, there is no single service in a Web service repository satisfying a service request. So how to design an efficient method for composing a chain of connected services has become an important research issue. Recently, much research has been done into the search time reduction when finding a composite service. However, most methods take a long time for traversing all of the Web services in a service repository, thus it makes their response time significantly overrun a users waiting patience. This paper develops an efficient approach for automatic composition of Web services using the state-of-the-art Artificial Intelligence (AI) planners, where a Web service composition (WSC) problem is regarded as a WSC planning problem. Unlike most traditional WSC methods that traverse a Web service repository many times, our approach converts a Web service repository into a planning domain in PDDL just once, which will only be regenerated when the Web service repository changes. This treatment substantially reduces the response time and improves the scalability of solving WSC problems. We have implemented a prototype system and conducted extensive experiments on large-scale Web service repositories. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art.

Towards automated choreography of Web services using planning in large scale service repositories

Automated composition of Web services is becoming a prominent paradigm for implementing and delivering distributed applications. A composed service can be described either by orchestration or choreography. Service orchestration has a centralized controller which coordinates the services in a composite service. Differently, service choreography assumes that all of the participating services collaborate with each other to achieve a globally shared task. Choreography has received great attention and demonstrated a few key advantages over orchestration such as data efficiency, distributed control, and scalability. Although there is extensive research on the languages and protocols of choreography, automated design of choreography plans, especially distributed plans for multiple roles, is more complex and not studied before. In this paper, we propose a novel planning-based approach, including compilation of contingencies, stateful actions, dependency analysis and communication control, which can automatically convert a given composition task to a distributed choreography specification. The experimental results conducted on large scale service repositories show the effectiveness and efficiency of our approach for automated choreography of Web services.

Towards optimal discovery of web services for multiple QoS constraints and preferences

Web service discovery (WSD) is the task of matchmaking a set of relevant web services. Quality of service (QoS) has recently been applied to represent non-functional properties of web services. Therefore, when those services provide the same functionality but have different QoS values, how to effectively filter out the services that cannot satisfy the QoS constraints and rank the remaining services is still an open research issue. In this paper, we propose an integrated approach that allows a service requester to specify a functionality request, multiple QoS constraints and their preferences, and our method discovers a set of the most appropriate ranked services with QoS utility aggregation. By conducting empirical experiments on simulated web services, we validate the feasibility of our service discovery approach. The running example shows that, our approach can find more appropriate services by the satisfiability of multiple QoS constraints and the ranking of aggregated QoS.

Towards Uncertain QoS-Aware Service Composition via Multi-Objective Optimization

QoS-aware Web service composition has recently become one of the most challenging research issues. Although much work has been investigated to solve the problem, they mainly focus on certain QoS of Web services, while QoS with uncertainty exposes the most important characteristic in a real and highly dynamic environment on the Internet. In this paper, with the consideration of uncertain service QoS, we model the issue of Web service composition with QoS uncertainty that is translated into a multi-objective optimization problem via uncertain interval number, which can be solved by our proposed approach via an non-deterministic multi-objective evolutionary algorithm using the strategy of decomposition. Large-scale empirical experiments have been conducted on our simulated datasets. The experimental results demonstrate that our proposed approach can effectively and efficiently find an optimum composite service solution set with satisfactory convergence.

Service composition and user modeling for personalized recommendation in cloud computing

In recent years, cloud computing is gradually evolving as a popular computing paradigm, which offers a uniform platform for service providers to publish their applications as cloud services. In many cases, however, single cloud service cannot satisfy a service request due to its simple functionality. Furthermore, current service composition systems have seldom taken into account user interests for personalized recommendation. In this paper, we propose a novel framework for personalized service recommendation in cloud computing platform by Web service composition and user modeling. The proposed framework first models cloud services together with a service request as a Web service composition problem, called cloud service recommendation (CSR) planning problem. It is fed into our self-developed service planner to compose a cloud service with complex business workflow. Second, our framework also applies user modeling for checking whether the generated composite cloud service can be matched with the interests of service consumer. To validate the feasibility of CSR framework, we have designed and implemented two prototype systems, QoS-aware service composition system and service platform based on user model.

Identifying influential individuals in microblogging networks using graph partitioning

Abstract Identifying influential individuals who lead to faster and wider spreading of influence in social networks is of theoretical significance and practical value to either accelerating the speed of propagation in the case of product promotion, or hindering the pace of diffusion involved in rumors. Conventional methods, ranging from centrality indices to diffusion-based processes, already take into account the number and influences of followers, but fail to make full use of the characteristics of social media. A novel approach called PartitionRank for finding a pre-fixed number of influential individuals in microblogging scenarios is proposed in this study to maximize the impact; it combines interest similarity with social interaction between users via graph partitioning. Experimental results on artificial and real-world microblogging networks illustrate that our scheme outperforms the other state-of-the-art methods in effectiveness and efficiency.

Clustering-based uncertain QoS prediction of web services via collaborative filtering

Although collaborative filtering (CF) has been widely applied for QoS-aware web service recommendation, most of these approaches mainly focus on certain QoS prediction. However, they failed to take the natural characteristic of web services with QoS uncertainty into account in service-oriented web applications. To solve the problem, this paper proposes a novel approach for uncertain QoS prediction via collaborative filtering and service clustering. We first establish uncertain QoS model for a service user, where each service is formalised as a QoS matrix. To mine the similar neighbourhood users for an active user, we then extend the Euclidean distance to calculate the similarity between two uncertain QoS models. Finally, we present two kinds of QoS prediction strategies based on collaborative filtering and clustering, called U-Rec and UC-Rec. Extensive experiments have been carried on 1.5 million real-world uncertain QoS transaction logs of web services. The experimental results validate the effectiveness of our proposed approach.

Computing uncertain skyline of Web services via interval number

QoS values may significantly vary due to the invocation of Web services under dynamical network environment. Although some of approaches apply uncertain QoS to computing the skyline for the reduction of the number of candidate services, they have no uncertain QoS model that needs to be aligned to realistic invocation and execution of Web services. To solve the issue, this paper presents a novel approach to uncertain service skyline via Chebyshevs inequality and interval number. We model uncertain QoS of a Web service by a QoS matrix and then each dimension is shrank to an uncertain QoS scope by interval number. Finally, based on the QoS model, we propose an uncertain service skyline algorithm to compare the QoS of two Web services with domination relationship strategy. Extensive experiments have been conducted on 1,558,224 Web service invocation records. The experimental results demonstrate the effectiveness of our proposed approach.

UCLAO* and BHUC: Two Novel Planning Algorithms for Uncertain Web Service Composition

The inherent uncertainty of Web service is the most important characteristic due to its deployment and invocation within a real and highly dynamic Internet environment. Web service composition with uncertainty (U-WSC) has become an important research issue in service computing. Although some research has been done on U-WSC via non-deterministic planning in Artificial Intelligence, they cannot handle the situation that uncertain Web services with the same functionality exist in a service repository and could not get all of possible solution plans that constitute an uncertain composition solution for a given request. To solve above research challenges, this paper models a U-WSC problem into a U-WSC planning problem. Accordingly, two novel uncertain planning algorithms using heuristic search called UCLAO* and BHUC, are presented to solve the U-WSC planning problem with state space reduction, which leads to high efficiency of finding a service composition solution. We have conducted empirical experiments based on a running example in e-commerce application as well as our large-scale simulated datasets. The experimental results demonstrate that our proposed algorithms outperform the state-of-the-art non-deterministic planning algorithms in terms of effectiveness, efficiency and scalability.