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Building a quality service-based system (SBS) is one of the most important research topics in software engineering. Many studies investigate intelligent methods to simplify the process of building SBSs. In particular, some keyword-based SBS building methods allow service users to automatically build an SBS by only providing a few of keywords. This type of work usually constructs a directed weighted graph of a service repository. A set of minimum-weight group Steiner trees (MSTs) is extracted from the graph to represent the service functions and their relations. However, to the best of our knowledge, none of the existing keyword-based SBS building methods allow the relaxation of the function requirements for a user. A relaxed SBS may achieve a comparable functionality versus a complete SBS containing all the query functions. To fill in the above gap, we define a new problem: a bounded skyline SBS building problem, whose solution is more adaptive and less limited than the traditional keyword-based SBS building methods. To solve this problem, we propose two algorithms based on skyline query, dynamic programming, and lower bound pruning. In the experiments, we collect real-world datasets and label the nodes with keywords. We conduct a comprehensive study to demonstrate the time efficiency of our algorithms on automatically finding SBSs. We make the annotated real-world datasets and our source code open to peer researchers.

We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service's trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework., Comment: 14 pages, accepted and to appear in IEEE Ttransactions on Services Computing

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets., Comment: 14 pages, accepted and to appear in IEEE Ttransactions on Services Computing

We propose a novel framework to select IaaS providers according to a consumer's long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework., Comment: 14 pages, accepted and to appear in IEEE Ttransactions on Services Computing

We propose a novel privacy-preserving QoS forecasting approach - Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments). Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users' accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novel energy sharing methods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach., Comment: 15 pages, 12 figures. This is an accepted paper appearing in the IEEE Transactions on Services Computing (IEEE TSC)

We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider’s long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider’s long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.

The papers in this special section focus on sensor data computing as a service in the Internet of Things (IoT). The Internet-of-Things (IoT) has gained significant attention over the past decade. It “allows people and things to be Anytime, Anyplace, with Anything and Anyone, ideally using Any path/ network and Any service”. The IoT promises to create a world where everyday objects (also called things) around us are connected to the Internet and communicate with each other with minimum human intervention. The number of things connected to the Internet exceeded the number of people on Earth in 2008. The sensory data these objects produce have significant value to many different parties from supply chain management to healthcare services to many more.

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

A service-oriented System of Systems (SoS) considers a system as a service and constructs a robust and value-added SoS by outsourcing external component systems through service composition techniques. Online reliability prediction for the component systems for the purpose of assuring the overall Quality of Service (QoS) is often a major challenge in coping with a loosely coupled SoS operating under dynamic and uncertain running environments. It is also a prerequisite for guaranteeing runtime QoS of a SoS through optimal service selection for reliable system construction. We propose a novel online reliability time series prediction approach for the component systems in a service-oriented SoS. We utilize Probabilistic Graphical Models (PGMs) to yield near-future, time series predictions. We assess the approach via invocation records collected from widely used real Web services. Experimental results have confirmed the effectiveness of the approach.

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41\% on average.

We propose a model for clustering data with spatiotemporal intervals. This model is used to effectively evaluate clusters of spatiotemporal interval data. A new energy function is used to measure similarity and balance between clusters in spatial and temporal dimensions. We employ as a case study a large collection of parking data from a real CBD area. The proposed model is applied to existing traditional algorithms to address spatiotemporal interval data clustering problem. Results from traditional clustering algorithms are compared and analysed using the proposed energy function.

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients’ privacy.

We propose a cloud service composition framework that selects the optimal composition based on an end user's long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers’ historical QoS data and short-term advertisements represented using Time Series . The quality of the QoS prediction is improved by incorporating QoS attributes’ intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series’ inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users’ credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers’ customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.

The articles in this special issue focus on the use of cloud computing for social computing applications. Currently, two complimentary Internet based research areas are emerging: social computing and cloud computing. On the one hand, social computing empowers individual users with relatively low technological sophistication to use the web to engage in social interactions, contribute their expertise and share their content, experiences and opinions. On the other hand, cloud computing offers everyone sophisticated computing infrastructures and resources as utilities, so that individual users with relatively low computing knowledge can have at their disposal a high performing computing infrastructure with little investment. Together, these two complementary technological advances form the backbone of our digitized world, when coupled with the rise of sensors,mobile devices and the internet of things. Of course, they also face significant challenges. These articles look at each area and describe how they complement each other. The Social Web has become an important means of communication for everyone: individuals, organizations, and governments all use it to disseminate and share information, offer opinions and engage in discussions.

Quality of service (QoS) is a major concern in the design and management of a composite service. In this paper, a systematic approach is proposed to calculate QoS for composite services with complex structures, taking into consideration of the probability and conditions of each execution path. Four types of basic composition patterns for composite services are discussed: sequential, parallel, loop, and conditional patterns. In particular, QoS solutions are provided for unstructured conditional and loop patterns. We also show how QoS-based service selection can be conducted based on the proposed QoS calculation. Experiments have been conducted to show the effectiveness of the proposed method.

Service composition is emerging as an effective vehicle for integrating existing web services to create value-added and personalized composite services. As web services with similar functionality are expected to be provided by competing providers, a key challenge is to find the “best” web services to participate in the composition. When multiple quality aspects (e.g., response time, fee, etc.) are considered, a weighting mechanism is usually adopted by most existing approaches, which requires users to specify their preferences as numeric values. We propose to exploit the dominance relationship among service providers to find a set of “best” possible composite services, referred to as a composite service skyline. We develop efficient algorithms that allow us to find the composite service skyline from a significantly reduced searching space instead of considering all possible service compositions. We propose a novel bottom-up computation framework that enables the skyline algorithm to scale well with the number of services in a composition. We conduct a comprehensive analytical and experimental study to evaluate the effectiveness, efficiency, and scalability of the composite skyline computation approaches.

We propose a system, called EVolution of Long-term Composed Services (Ev-LCS), to address the change management issues in long-term composed services (LCSs). An LCS is a dynamic collaboration between autonomous web services that collectively provide a value-added service. It has a long-term commitment to its users. We first present a formal model, which provides the grounding semantics to support the automation of change management. We present a set of change operators that allow to specify a change in a precise and formal manner. We then propose a change enactment strategy that actually implements the changes. We develop a prototype system for the proposed Ev-LCS to demonstrate its effectiveness. We also conduct an experimental study to assess the performance of the change management approach.

This paper presents a service-oriented digital government infrastructure focused on efficiently providing customized services to senior citizens. We designed and developed a Web Service Management System (WSMS), called WebSenior, which provides a service-centric framework to deliver government services to senior citizens. The proposed WSMS manages the entire life cycle of third-party web services. These act as proxies for real government services. Due to the specific requirements of our digital government application, we focus on the following key components of WebSenior: service composition, service optimization, and service privacy preservation. These components form the nucleus that achieves seamless cooperation among government agencies to provide prompt and customized services to senior citizens.

Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.

The performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is rather a demanding task and an imprecise specification of the weights could miss user-desired services. We present a novel concept, called p-dominant service skyline. A provider S belongs to the p-dominant skyline if the chance that S is dominated by any other provider is less than p. Computing the p-dominant skyline provides an integrated solution to tackle the above two issues simultaneously. We present a p-R-tree indexing structure and a dual-pruning scheme to efficiently compute the p-dominant skyline. We assess the efficiency of the proposed algorithm with an analytical study and extensive experiments.

We propose a shot-based interest point selection approach for effective and efficient near-duplicate search over a large collection of video shots. The basic idea is to eliminate the local descriptors with lower frequencies among the selected video frames from a shot to ensure that the shot representation is compact and discriminative. Specifically, we propose an adaptive frame selection strategy called furthest point voronoi (FPV) to produce the shot frame set according to the shot content and frame distribution. We describe a novel strategy named reference extraction (RE) to extract the shot interest descriptors from a keyframe with the support of the selected frame set. We demonstrate the effectiveness and efficiency of the proposed approaches with extensive experiments.

Reputation systems rely on past information to establish trust among unknown participants. Reputation bootstrapping - that is, assessing the reputations of newly deployed Web services (newcomers) - is a major issue in service-oriented environments because historical information often isn't present regarding newcomer behaviors. The authors examine different techniques for fairly and accurately bootstrapping newcomers' reputations in a service-oriented environment. They also present experimental results that evaluate the proposed techniques.

The Web is transforming from a Web of data to a Web of both Semantic data and services. This trend is providing us with increasing opportunities to compose potentially interesting and useful services from existing services. While we may not sometimes have the specific queries needed in top-down service composition approaches to identify them, the early and proactive exposure of these opportunities will be key to harvest the great potential of the large body of Web services. In this paper, we propose a Web service mining framework that allows unexpected and interesting service compositions to automatically emerge in a bottom-up fashion. We present several mining techniques aiming at the discovery of such service compositions. We also present evaluation measures of their interestingness and usefulness. As a novel application of this framework, we demonstrate its effectiveness and potential by applying it to service-oriented models of biological processes for the discovery of interesting and useful pathways.

The Web is revolutionizing the way citizens interact with businesses and government agencies. Almost all key functions of modern society are being reshaped to exploit opportunities the Web opens. As part of an effort to improve government-citizen interactions, government agencies are providing a wide spectrum of online services. Virginia Tech is collaborating with the Virginia Department for the Aging (VDA) on a project called WebSenior. A middleware infrastructure, WebSenior automatically delivers e-government services customized for seniors. It uses ontologies to automatically generate Web services customized to senior citizens' needs and government program laws and regulations.

We propose a composability model to ascertain that Web services can safely be combined, hence avoiding unexpected failures at runtime. Composability is checked through a set of rules organized into four levels, syntactic, static semantic, dynamic semantic, and qualitative levels. We introduce the concepts of composability degree and /spl tau/-composability to cater for partial and total composability. We also propose a set of algorithms for checking composability. Finally, we conduct a performance study (analytical and experimental) of the proposed algorithms.

Power conservation and client waiting time reduction are two important aspects of data access efficiency in broadcast-based wireless communication systems. The intention of data access methods is to optimize client power consumption with the least possible overhead on client waiting time. We propose an adaptive data access method which builds on the strengths of indexing and hashing techniques. We show that this method exhibits better average performance over the well-known index tree-based access methods. A new performance model is also proposed. This model uses more realistic assessment criteria, based on the combination of access and tuning times, for evaluating wireless access methods. This new model provides a dynamic framework to express the degree of importance of access and tuning times in an application. Under this new model, the adaptive method performance also outperforms the other access methods in the majority of cases.

For Web services to expand across the Internet, users need to be able to efficiently access and share Web services. The authors present a query infrastructure that treats Web services as first-class objects. It evaluates queries through the invocations of different Web service operations. Because efficiency plays a central role in such evaluations, the authors propose a query optimization model based on aggregating the quality of Web service (QoWS) parameters of different Web services. The model adjusts QoWS through a dynamic rating scheme and multilevel matching in which the rating provides an assessment of Web services' behavior. Multilevel matching allows the expansion of the solution space by enabling similar and partial answers.

Mobile commerce (m-commerce) refers to the conduct of business using wireless devices and communications. Driven by the success of e-commerce and impressive progress in wireless technologies, m-commerce is rapidly taking place in the business forefront. However, most of the concepts developed for e-commerce may not be easily applicable to wireless environments. This is due to the peculiarities of these environments such as limited bandwidth, unbalanced client-server communication, and limited power supply. Web services are undeniably one of the most significant e-commerce concepts worth of being adapted to the wireless world. Mobile services, also called m-services, promise several benefits compared with their wired counterparts. They provide larger customer base and cater for "anytime and anywhere" access to services. In this paper, we propose an infrastructure for organizing and efficiently accessing m-services in broadcast environments. We define a multichannel model to carry information about m-services available within a given geographic area. The UDDI channel includes registry information about m-services. The m-service channel contains the description and executable code of each m-service. The data channel contains the actual data needed while executing the m-service. We also introduce three techniques to enable efficient access to wireless channels. These techniques extend well-known mobile databases' access methods to m-services: B+ tree, signature indexing, and hashing. We finally present an analytical model and conduct an extensive experimental study to evaluate and compare the proposed techniques.

Despite important regulatory and technical efforts aimed at tackling aspects of the problem, privacy violation incidents on the Web continue to hit the headlines. The authors outline the salient issues and proposed solutions, focusing on generic Web user's Web privacy.

In efforts to use information and communication technologies for the civil and political conduct of government, many countries have begun supporting e-government initiatives. The ultimate goal is to improve government-citizen interactions through an infrastructure built around the "life experience" of citizens. To facilitate the use of welfare applications and expeditiously satisfy citizens' needs, we wrapped these applications in modular Web services. Adopting Web services in e-government enables government agencies to provide value-added services by defining a new service that outsources from other e-government services; to uniformly handle privacy issues: and to standardize the description, discovery, and invocation of social programs. The core of our research is to develop techniques to efficiently access e-government services while preserving citizens' privacy. To that end we designed and implemented an infrastructure called Web Digital Government.

The ubiquity of the World Wide Web offers an ideal opportunity for the deployment of highly distributed applications. Now that connectivity is no longer an issue, attention has turned to providing a middleware infrastructure that will sustain data sharing among Web-accessible databases. We present a dynamic architecture and system for describing, locating, and accessing data from Web-accessible databases. We propose the use of flexible organizational constructs service links and coalitions to facilitate data organization, discovery, and sharing among Internet-accessible databases. A language is also proposed to support the definition and manipulation of these constructs. The implementation combines Java, CORBA, database API (JDBC), agent, and database technologies to support a scalable and portable architecture interconnecting large networks of heterogeneous and autonomous databases. We report on an experiment to provide uniform access to a Web of healthcare-related databases.

The Web offers a single user interface to data sharing across heterogeneous and autonomous databases, but it was not designed to handle the rigid DBMS protocols and data formats used by relational and object-oriented databases. WebFindIt is an ongoing project to develop the database equivalent of the World Wide Web-namely, a World Wide Database-through a middleware infrastructure for describing, locating, and accessing data from any kind of Web-accessible database. A special-purpose language, Web-Tassili, supports the definition and manipulation of middleware constructs for organizing the information space. An implementation of WebFindIt combines Java, CORBA and database technologies.

Workflow systems are receiving increased attention as they intend to facilitate the operations of enterprises by coordinating and streamlining business activities. The need for automated support and operational models that allow workflow applications to coordinate units of work across multiple servers-according to business defined rules and routes-is becoming critical for the proper management of such activities. In this paper, we describe a Transaction-Oriented Workflow Environment (TOWE) for programming workflow activities. The novelty of our approach resides in the proposed unified abstraction, class libraries, to support workflow activities. The fundamental concept used in the TOWE system is based on the symbiosis of object-oriented programming and interprocess communication concepts. In TOWE, the concurrency abstractions are represented by process objects, active objects acting as processes, which involve asynchronous, location-independent, and application specific process invocations.

We focus on the stability and behavior of three widely used clustering algorithms. Various correlation coefficients are computed to help us understand the sensitivity of object clustering. Surprisingly, the results indicate that there is almost no difference among any clustering approach. Furthermore, all methods appear to be near stable. These findings tend to show that the clustering algorithms are independent of the way objects are inherently clustered.

SERVICE-ORIENTED computing is gaining momentum as the next technological vehicle to leverage the huge investments in web application development. Web services are poised to take center stage as part of this adoption [4]. The ever-increasing number of web services will have the effect of transforming the web from a data-oriented repository to a service-oriented repository, also known as the Service Web [1]. In this new paradigm, existing business logic would be wrapped as web services to be accessible on the web via a web services middleware [2]. As the number of web services is expected to substantially increase, this would have the effect of introducing competition among web services that offer similar functionalities. Service users are enabled to select the “best” web services and/or their combinations with respect to their expected quality, such as price, response time, and reputation. There is a need to provide a sound framework to organize web services. This would form as a platform to query web services. Building this framework is especially important due to the ever-increasing large and heterogeneous web service deployment. A key ingredient of such a service framework is a formal service query model that can capture the key features of services to filter interactions and accelerate service searches. The query models must be congruent with the dynamic, active, autonomous, and highly heterogeneous nature of web services and their environment. Query languages and efficient selection techniques can then be developed once such a service model is in place. Existing service discovery technologies, such as service registries and service search engines, mainly support the simple keyword-based search on web services. However, keyword search cannot always precisely locate web services, partially because of the rich semantics embodied in these services. Due to the ambiguity of the keywords, which are typically described using natural language, either too many irrelevant services may be returned or some highly relevant services may be missed. As a key facilitator for application outsourcing, a common usage pattern of web services is to be programatically integrated into other applications (e.g., a travel package, navigation system, etc.). This further requires a service query mechanism that is more precise and reliable than keyword-based search. Query processing on web services is a novel concept that goes beyond the traditional data-centric view of query processing, which is mainly performance centered. It focuses on user quality parameters to select multiple services that are equivalent in functionality but exhibit a different quality of web service [3]. This special issue provides insights into the latest research on web service querying and efficient selection. Five articles were selected through a rigorous review process. They cover a set of key research topics including modeling techniques for web services, service query languages, algorithms for efficient service selection, as well as quality of web service modeling and quality-based service selection. The article by Skoutas et al., “Ranking and Clustering Web Services Using Multicriteria Dominance Relationships,” proposes a service selection framework that integrates the similarity matching scores of multiple parameters obtained from various matchmaking algorithms. The framework relies on the service dominance relationships to determine the relevance between services and users’ requests. Instead of using a weighting mechanism, the dominance relationship adopts a multi-objective strategy that simultaneously considers the matching scores of all the parameters for ranking the relevant services. A clustering algorithm is also proposed that captures the trade-offs among different parameters with respect to the considered matching criteria. The article by Grigori et al., “Ranking BPEL Processes for Service Discovery,” proposes a service discovery approach based on behavioral descriptions expressed in BPEL. Behavioral matchmaking goes beyond interface matchmaking as it considers the constraints on the invocation order of operations in service interfaces. Graph matching algorithms are applied, which enable the delivery of approximate behavioral matches. The article by Michlmayr et al., “End-to-End Support for QoSAware Service Selection, Binding, and Mediation in VRESCo,” describes a runtime environment for serviceoriented computing, called VRESCo. The proposed VRESCo framework provides a service metadata model. Service discovery and selection approaches are developed using this model. In addition, other important issues, such as QoS monitoring, dynamic binding, and service mediation, are addressed. The article by Barhamgi et al., “A Query Rewriting Approach for Web Service Composition,” proposes a service querying approach to compose dataproviding services. The data-providing services are modeled as RDF views over a mediated ontology specified in RDF to capture the consensual and shared knowledge in a IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 3, NO. 3, JULY-SEPTEMBER 2010 161

Databases are as ubiquitous as the Internet. The Web's widespread growth has dramatically increased the number of data sources available (data warehouses, scientific data banks, digital libraries, and so on), and it has increased the opportunities to harness these data sources in unanticipated ways.