Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs. In SFSS, we identified a set of high-level exception handling strategies based on the QoS performances of different component services and the preferences articled by the service consumers. Multiple recovery plans are generated and evaluated according to the performance of the selected component services, and then we execute the best recovery plan. We assess the overall user dependence (i.e., the service is independent of other services) using the generated plan and the available invocation information of the component services. Due to the experiment results, the given technique enhances the service selection quality by choosing the services that have the highest score and betters the overall system performance. The experiment results indicate the applicability of SFSS and show improved performance in comparison to similar approaches.

The proliferation of private clouds that are often underutilized and the tremendous computational potential of these clouds when combined has recently brought forth the idea of volunteer cloud computing (VCC), a computing model where cloud owners contribute underutilized computing and/or storage resources on their clouds to support the execution of applications of other members in the community. This model is particularly suitable to solve big data scientific problems. Scientists in data-intensive scientific fields increasingly recognize that sharing volunteered resources from several clouds is a cost-effective alternative to solve many complex, data- and/or compute-intensive science problems. Despite the promise of the idea of VCC, it still remains at the vision stage at best. Challenges include the heterogeneity and autonomy of member clouds, access control and security, complex inter-cloud virtual machine scheduling, etc. In this paper, we present CloudFinder, a system that supports the efficient execution of big data workloads on volunteered federated clouds (VFCs). Our evaluation of the system indicates that VFCs are a promising cost-effective approach to enable big data science.

Predicting Web service interactions such as composition and substitution provides support for developers during mashup design. In this paper, we propose a deep-learning approach for predicting compositions and substitutions. To the best of our knowledge, this work is the first to adopt deep learning for interactions prediction. We use stacked autoencoders to learn latent service features. A deep feed forward neural network leverages the learned features and the history of previous interactions to predict new ones. We conducted extensive experiments on real-world Web services to illustrate the performance of our approach. We show that the use of deep learning achieves a high accuracy level and outperforms existing models such as multi-layer perceptron and support vector machine.

In this article, we propose a Statistical Cloud-Assisted Reputation Estimation (sCARE) approach for service-oriented environments in uncertain situations. sCARE uses the ratings from cooperating service consumers to uniformly describe the randomness and fuzziness of the different submitted ratings and their associated relationships in quantitative terms. We also define discriminant functions to model the honesty (or lack thereof) of the service raters. Experiment results show that our proposed model performs in a fairly accurate manner for a number of real-world scenarios. A comparison study with similar existing works is also provided to asses sCARE’s performance.

Crowdsourcing is emerging as a powerful paradigm to help perform a wide range of tedious tasks in various enterprise applications. As such applications become more complex, crowdsourcing systems often require the collaboration of several experts connected through professional/social networks and organized in various teams. For instance, a well-known car manufacturer asked fans to contribute ideas for the kinds of technologies that should be incorporated into one of its cars. For that purpose, fans needed to collaborate and form teams competing with each others to come up with the best ideas. However, once teams are formed, each one would like to provide the best solution and treat that solution as a “trade secret,” hence preventing any data leak to its competitors (i.e., the other teams). In this article, we propose a data leak--aware crowdsourcing system called SocialCrowd . We introduce a clustering algorithm that uses social relationships between crowd workers to discover all possible teams while avoiding interteam data leakage. We also define a ranking mechanism to select the “best” team configurations. Our mechanism is based on the semiring approach defined in the area of soft constraints programming. Finally, we present experiments to assess the efficiency of the proposed approach.

To guarantee privacy in service oriented environments, it is essential to check for compatibility between a client's privacy requirements and a Web service privacy policies before invoking the Web service operation. In this paper, we focus on privacy at the Web service operation level. We present an approach that integrates k-Anonymity into a privacy management framework using Web Services Conversation Language (WSCL) definitions. In particular, we use the notion of k-Anonymity to determine the extent to which the invocation of an operation can be inferred if one knows that a downstream operation was invoked. We provide both a formal definition as well as an implementation of the proposed approach.

Service Oriented Architecture (SOA) enables the creation of distributed applications from independently developed and deployed services. As with any component-based system, the overall performance and quality of the system is an aggregate function of its component services. In this article, we present a novel approach for managing bottom-up faults in service-based systems. Bottom-up faults are a special case of system-wide exceptions that are defined as abnormal conditions or defects occurring in component services, which if not detected and/or managed, may lead to runtime failures. Examples of bottom-up faults include network outage, server disruption, and changes to service provisioning (e.g., new operation parameter required) that may have an impact on the way component services are consumed. We propose a soft-state signaling-based approach to propagate these faults from participants to composite services. Soft-state refers to a class of protocols where the state of a service is constantly refreshed by periodic messages, and user/service takes up the responsibility of communicating and maintaining its state. Soft-state-based protocols have a number of advantages including implicit error recovery and easier fault management, resulting in high availability for systems. Although soft-state has been widely used in various Internet protocols, this work is the first (to the best of our knowledge) to adopt soft-state for fault management in composite services. The proposed approach includes protocols for fault propagation (pure soft-state and soft-state with explicit removal) and fault reaction (rule-based). We also present experiment results to assess the performance and applicability of our approach.

This paper investigates the problem of rating propagation in composite systems. We propose a method for reputation distribution among component services in Web service composition environments. The main idea lies in providing component services with the appropriate amount of share received for the overall rating. The amount should be proportional to the contribution and performance of the component service. The method ensures that any component service is neither over rated at the expense of a higher performing component nor penalized due to a low performing component. We make use of the textual information present in the service reviews to extract different aspects and their individual sentiments to provide a better rating distribution mechanism. The proposed method attempts to accurately distribute the rating so that it closely reflects the performance of each component in the system. The experimental results show the applicability of our approach and the improved ranking distribution.

Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new privacy concerns that traditional privacy models do not handle. In addition, DaaS composition may reveal privacy-sensitive information. In this paper, we propose a formal privacy model in order to extend DaaS descriptions with privacy capabilities. The privacy model allows a service to define a privacy policy and a set of privacy requirements. We also propose a privacy-preserving DaaS composition approach allowing to verify the compatibility between privacy requirements and policies in DaaS composition. We propose a negotiation mechanism that makes it possible to dynamically reconcile the privacy capabilities of services when incompatibilities arise in a composition. We validate the applicability of our proposal through a prototype implementation and a set of experiments.

Cloud computing enables the automated creation of business applications from independently developed and deployed services. In this paper, we propose a technique for Cloud-based services to dynamically evaluate the performance of services based on their previous history, and user requirements. The likelihood of fault occurrence is then used to create recovery plans. The `best' recovery plan is then chosen to be either executed immediately, or saved for a later execution. We assess the overall system dependability using the generated plan and the available invocation information of the included services. Experiment results indicate the applicability of our technique, and show the improved performance in comparison with similar existing approaches.

Privacy is still among the key challenges that keep hampering DaaS service composition solution. Indeed services may follow different, conflicting privacy specifications with respect to the data they use and provide within a composition. In this paper, we propose an approach for privacy-aware composition of DaaS services. Our approach allows verifying the compatibility of privacy specifications of services involved in a composition. In the case when any composition will be incompatible in terms of privacy, we introduce a novel approach based on negotiation to reach compatibility of concerned services. The negotiation approach is cautiously operated with without any privacy damaging of services. We validate the applicability of our proposal through a set of experiments.

Automated negotiation among Web services not only provides an effective way for the services to bargain for their optimal customizations, but also allows the discovery of overlooked potential solutions. A number of negotiation supporting techniques have been used to find solutions that are acceptable to all parties in the negotiation. However, employing these solutions for automated negotiations among Web services has its own challenges. In this paper, we present the design of a Negotiation Web service that would be used by both the consumers and providers of Web services for conducting negotiations. This negotiation service uses a genetic algorithm(GA) based approach for finding acceptable solutions in multi-party and multi-objective negotiations. In addition to the traditional genetic operators of crossover and mutation, the search is enhanced using anew operator called the Norm. Norm operator represents the cumulative knowledge of all the parties involved in the negotiation process. GA performance with the new Norm operator is compared to the traditional GA, hill-climber and random search techniques. Experimental results indicate the practicality of our approach in facilitating the negotiations involved in a Web service composition process. Specifically, the proposed GA with Norm operator performs better than other approaches.

In this paper, we present Meerkat, a dynamic framework for preserving privacy in Web services. We define a Web service-aware privacy model that deals with the privacy of input data, output data, and operation usage. We introduce a matching protocol that caters for partial and total privacy compatibility. Finally, we propose a negotiation model to reconcile clients' requirements with providers' policies in case of incompatibility.

Data-Providing (DP) services allow query-like access to organizations' data via web services. The invocation of a DP service results in the execution of a query over data sources. In most cases, users' queries require the composition of several services. In this paper, we propose a novel approach for querying and automatically composing DP services. The proposed approach largely draws from the experiences and lessons learned in the areas of service composition, ontology, and answering queries over views. First, we introduce a model for the description of DP services and specification of service-oriented queries. We model DP services as RDF views over a mediated (domain) ontology. Each RDF view contains concepts and relations from the mediated ontology to capture the semantic relationships between input and output parameters. Second, we propose query rewriting algorithms for processing queries over DP services. The query mediator automatically transforms a user's query (during the query rewriting stage) into a composition of DP services. Finally, we describe an implementation and provide a performance evaluation of the proposed approach.

The emergence of modern data-intensive applications requires sophisticated database techniques for processing advanced types of user queries on massive data. In this paper, we study such a new type of query, called progressive queries. A progressive query is defined as a set of inter-related and incrementally formulated step-queries. A step-query in a progressive query PQ is specified on the fly based on the results of previously-executed step-queries in PQ. Hence, a progressive query cannot be formulated in advance before its execution, which raises challenges for its processing and optimization. We introduce a query model to characterize different types of progressive queries. We then present a new index structure, called the collective index, to efficiently process progressive queries. The collective index technique incrementally evaluates step-queries via dynamically maintained member indexes. Utilizing the special structure of a collective index, the (member) indexes on the input relation(s) of a step-query are efficiently transformed into indexes on the result relation. Algorithms to efficiently process single-input (unary) linear and multiple-input (join) linear progressive queries based on the collective index are presented. Our experiment results show that the proposed collective index technique outperforms the conventional query processing methods in processing progressive queries.

The Event-driven, or notification-based, paradigm has attracted much research interest in areas such as distributed systems, databases, workflow, and grid computing. However, little attention was devoted to event-driven Service-Oriented Architectures (SOAs). In this paper, we propose a novel framework for event-based interactions in SOAs. Our contribution in this paper is threefold. First, we introduce various notification patterns for event-driven SOAs. We define two taxonomies for dissemination protocols in SOAs: the interaction taxonomy identifies the different models through which Web services interact with each other; and the filtering taxonomy classifies the events and services involved during dissemination. Second, we propose a dissemination pattern called implicit notification. In contrast to publish-subscribe, implicit notification does not require consumers to explicitly subscribe with producers. We define a model for implicit notifications and introduce a family of protocols for enabling this pattern. Finally, we describe a prototype implementation for a disaster management case study and conduct experiments to assess the performance of the proposed protocols.

introduction Introduction to special issue on semantic Web services Editors: Brahim Medjahed View Profile , Athman Bouguettaya View Profile , Boualem Benatallah View Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 8Issue 1pp 1–eshttps://doi.org/10.1145/1294148.1294149Published:01 November 2007Publication History 3citation881DownloadsMetricsTotal Citations3Total Downloads881Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

The Web is changing the way organizations are conducting their business. Businesses are rushing to provide modular applications, called Web services, that can be programmatically accessed through the Web. Despite the tremendous developments achieved so far, one of the most important, yet untapped potential, is the use of Web services as facilitators for inter-organizational cooperation. This promising concept, known as Web service composition, is gaining momentum as the potential silver bullet for the envisioned Semantic Web. The development of such integrated services has so far been ad hoc, time-consuming, and requires extensive low-level programming efforts. In this paper, we present WebBIS (Web Base of Internet-accessible Services), a generic framework for composing and managing Web services. We combine the object-oriented and active rules paradigms for such a task. We also provide a ontology-based framework for organizing the Web service space. We finally propose a peer-to-peer mechanism for reporting, propagating, and reacting to changes in Web services.

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.