The rapid proliferation of social media platforms has led to a surge in multimodal fake news, where deceptive content often combines text and images to mislead audiences. Traditional unimodal detection methods struggle to address the complexity of such content, necessitating holistic multimodal approaches. While the latest advancements in Multimodal Large Language Models (MLLMs) offer new opportunities for enhancing detection performance by analyzing multi-dimensional features, including source credibility, cross-modal contradictions, emotional bias, and manipulative writing patterns, these methods suffer from a key flaw: a susceptibility to hallucinations or erroneous reasoning, which can lead to flawed conclusions and ultimately biased detection results. We propose the Multimodal Fake News Detection via Multi-perspective Rationale Generation and Verification (MMRGV) model to mitigate this challenge. Our method employs a cross-verification mechanism to screen and reconcile contradictions among different rationales, thereby preserving the LLM's analytical advantages while mitigating the impact of erroneous reasoning or hallucinations on the final detection. Subsequently, these optimized rationales are fused via an adaptive weighting strategy to output a robust final prediction. Extensive experiments on three benchmark datasets (Twitter, Weibo, and GossipCop) demonstrate the superiority of our method, achieving state-of-the-art accuracy of 0.9972, 0.9663, and 0.8772, respectively, and significantly outperforming existing baselines. These results validate the effectiveness of multi-perspective rationale generation and cross-verification in enhancing multimodal fake news detection, offering a resilient solution to combat misinformation in the era of generative AI.

Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely influenced by the allocation of ranks and scaling factors, as well as initialization.Existing LoRA variants typically address only one of these factors, often at the cost of increased training complexity or reduced practical efficiency.In this work, we present Task-aware Low-Rank Adaptation (TLoRA), a unified framework that jointly optimizes initialization and resource allocation at the outset of training.TLoRA introduces a data-driven initialization strategy that aligns the LoRA A matrix with task-relevant subspaces by performing singular value decomposition on the product of pre-trained weights and input activation covariance.After this, the A matrix is frozen, and only the B matrix is trained.Furthermore, TLoRA employs a sensitivitybased importance metric to adaptively allocate ranks and scaling factors across layers under a fixed parameter budget.We conduct extensive experiments that demonstrate TLoRA consistently performs excellently across various tasks, including natural language understanding, commonsense reasoning, math reasoning, code generation, and chat generation, while significantly reducing the number of trainable parameters.

In cloud computing, static resource configurations create a trade-off: tenants overprovision to avoid resource starvation, causing inefficiency and cost, while providers suffer low utilization despite high allocations. To improve efficiency, providers often use overcommitted environments where multiple workloads share hosts, but this leads to interference and potential Quality-of-Service (QoS) violations. This paper introduces a realtime dynamic control framework that mitigates interference by adaptively pinning workloads to CPU groups. Using deep reinforcement learning (DRL) with the Proximal Policy Optimization (PPO) algorithm, an intelligent agent continuously adjusts CPU pinning based on real-time feedback to maintain Service- Level-Agreement (SLA) compliance. Experiments under two optimization objectives—overall-performance-first and priorityperformance- first—show that the proposed approach improves overall QoS by$\approx$25% compared with static pinning. When prioritization is enabled, high-priority workloads gain significant performance improvements while lower-priority ones remain within SLA limits. These results demonstrate that a DRL-based CPU-pinning strategy effectively manages resource contention in overcommitted clouds, enhancing utilization while upholding tenant SLAs.

As an essential part of modern healthcare systems, extracting valuable insights from electronic medical records (EMRs) remains challenging due to the complexity of structured and unstructured data. Data visualization is essential for transforming complex data into comprehensible visuals that enable professionals to identify patterns and trends. This process involves selecting data attributes, transforming the data, choosing appropriate visual encoding methods, and rendering graphical representations using declarative visualization languages (DVLs). However, achieving proficiency in DVLs requires a deep understanding of domain-specific data and expertise in these languages, which poses a significant barrier for beginners and non-technical users. To address these challenges, we present HealthLens, the first user-friendly visualization tool in the EMR domain that eliminates the need for prior knowledge of DVLs. Built on the MedCodeT5 model developed by us and leveraging a large language model with a bilevel optimization approach, HealthLens enables the generation of EMR visualizations from natural language queries. This demonstrates the feasibility of creating sophisticated visualizations with minimal technical expertise, advancing accessibility in the EMR field.

Automated medication recommendation is a crucial task within the domain of artificial intelligence in healthcare, where recommender systems are supposed to deliver precise, personalized drug combinations tailored to the evolving health states of patients. Existing approaches often treat clinical records (e.g., diagnoses, procedures) as isolated or unified entities, neglecting the inherent set-structured nature of medical data and the need to model interdependencies among clinical elements. To address the gap, we propose SSPNet, a novel end-to-end framework designed to process complete clinical record sets and directly generate optimal medication sets. SSPNet employs a set-based encoder to effectively capture and represent a patient's health condition from the electronic health records (EHRs), while a permutation-consistent decoder predicts the entire medication combination as a set. In addition, we introduce a novel personalized representation mechanism to capture the drugs previously used by individual patients. Extensive experiments on MIMIC-Ⅲ and MIMIC-Ⅳ data sets reveal that SSPNet surpasses existing state-of-the-art methods in the accuracy of medication recommendations.

Unlabeled text representation learning (UTRL), encompassing static word embeddings such as Word2Vec and contextualized word embeddings such as bidirectional encoder representations from transformer (BERT), aims to capture semantic word relationships in a low-dimensional space without the need for manual labeling. These word embeddings are invaluable for downstream tasks such as document classification and clustering. However, the surge of short texts generated daily on social media platforms results in sparse word cooccurrences, compromising UTRL outcomes. Contextualized models such as recurrent neural network (RNN) and BERT, while impressive, often struggle with predicting the next word due to sparse word sequences in short texts. To address this, we introduce CGraphNet, a contrastive graph context prediction model designed for UTRL. This approach converts short texts into graphs, establishing links between sequentially occurring words. Information from the next word and its neighbors informs the target prediction, a process referred to as graph context prediction, mitigating sparse word cooccurrence issues in brief sentences. To minimize noise, an attention mechanism assigns importance to neighbors, while a contrastive objective encourages more distinctive representations by comparing the target word with its neighbors. Our experiments demonstrate CGraphNet's superior performance over other baselines, particularly in classification and clustering tasks on real-world datasets.

Node classification tasks aim to assign labels or categories to entire graphs based on their structural properties or node attributes. It can be adopted for various types of graph systems, including but not limited to network traffic, biological networks, knowledge graphs, etc., especially to social networks. This problem is well-studied, and solutions have demonstrated significant success in numerous real-world applications. However, in the situation where emerging categories are scarce or even have no labeled data, classical methods perform poorly on the whole, which has attracted growing attention. Based on this, in this article, we divide researches for node classification in social networks into two broad categories: traditional methods and novel strategies (few-shot/zero-shot learning). In traditional node classification methods, we summarize some classical methods for both homogeneous and heterogeneous networks, which includes unsupervised classifier, matrix factorization techniques, supervised methods, random-walk, and meta-path. Meanwhile, we introduce novel methods in few-shot or zero-shot learning. The article outlines the technical principles of various methods and analyzes their performance across different classes. It further summarizes the benchmark datasets used for evaluating node classification tasks. Finally, the major opportunities, challenges, and future research directions in few-shot and zero-shot learning for node classification in graph scenarios are discussed.

Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation ($> $80%) but with low utilization ($< $25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called$MMAM$, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind$MMAM$is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators,$MMAM$successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications.

The papers in this special section focus on data centric big data services. As an overwhelming amount of data is generated at a faster rate every day from all sources, and applications such as cloud services, the Internet of Things (IoT), social network services and intelligent terminals, it has become more urgent than ever to design, deploy and provision services more wisely so that the provisioned services could support effective acquisition, storage, transformation, process, management and utilization of such data. Manipulating and getting the most out of the Big Data can bring unprecedented value and new opportunities that are critical to business success. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, and improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of the Big Data. Datacentric big service is an inevitable evolution of services with the emergence of big data in the last decade.

The papers in this special issue are designed to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The concept of Cloud Service represents a prime facility and feature of services in a cloud computing environment that can be made available to users on demand. Due to the flexibility of cloud computing in scaling IT resources up and down, cloud services gradually become valuable to attract the gaze of researchers and engineers from both academia and industry when they are faced with dynamically changing business requirements. Different stakeholders, such as consumers, providers, and operators, are generating a vast amount of data on such services per minute on the Internet, which increasingly comes to show the “4V” characteristics of big data. Therefore, new methodologies and techniques are urgently required for designing, validating, developing, testing, and deploying cloud services on demand in this specific scenario based on big data, as well as for efficiently being adaptive to business dynamics and users’ explicit and implicit requirements.

This is the second part of a special issue on "Cloud Services Meet Big Data" organized to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The seven papers included in this special investigate the most challenging issues in the areas of IaaS (Infrastructure as a Service) design and operations, requirements engineering and knowledge engineering for cloud services development, and service search and discovery.

This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments.

With the economic globalization, urban sustainable innovation ability has become one of the important factor of urban comprehensive competitiveness. Urban sustainable innovation ability is referred to as an ability that a city transforms the various factors such as information into new products, new services, which is also directly related to the sustainable development driving force and long-term competitiveness of city or region. In this paper, we mainly study the comprehensive evaluation of urban sustainable innovation ability based on factor analysis method. Firstly, we decompose the urban sustainable innovation ability and select the evaluation indices, and then establish evaluation index system of sustainable innovation ability. Secondly, we set up the evaluation model of urban sustainable innovation ability using the factor analysis method, and evaluate the sustainable innovation ability of the sample city and calculate the comprehensive score. Finally, according to the evaluation result, through the analysis on the aspects of urban sustainable innovation ability, what the strengths and weaknesses of the innovation work for the city can be got, which can enhance the sustainable innovation ability of the city and make the corresponding measures for the construction of innovation system improved. This whole paper mainly emphasizes on the sustainability for urban innovation ability.

This article proposes to identify and recommend scientific workflows to promote their reuse and repurposing. Specifically, a scientific workflow is converted into a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows in order to construct a scientific workflow network model. A graph-skeleton based clustering method is adopted for grouping layer hierarchies into clusters. Barycenters in clusters are identified for facilitating cluster identification and workflow ranking and recommendation. Experimental result shows that this technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows.

In the era of Internet, service-oriented development becomes a popular software development paradigm for developing large-scale systems and cloud apps. Followed by this new emerging paradigm, functions would be designed and developed as components, deployed separately and provided as services for integration. In generally, experienced developers tend to introduce some reusable distributed service frameworks in their projects. These frameworks are usually developed based on Service-Oriented Architecture (SOA), and take charge of Remote Procedure Call (RPC), distributed service collaboration, remote service communication and other common duties. It has been proved that these distributed service frameworks are a kind of effective software infrastructures to facilitate development. However, in large-scale cloud ecosystems development, chaos might occur if interdependent remote services composed together to enable cloud services and complex systems. Towards this problem, this paper proposes a novel framework developed by leveraging a platform as a unified access gateway of remote services for different cloud services in the ecosystem. Experimental results show that the framework could effectively reduce the development difficulty of the large-scale cloud ecosystems, and improve the performance of the developed systems.

As the number of Web services surges rapidly, recommending the right service for various users on demand has become one of the most challenging research issues in the fields of services computing and cloud computing. This special issue focuses on the recommendation techniques for services computing and cloud computing including classic recommendation algorithms for services computing and cloud computing, emerging recommendation techniques for service selection and composition, and applications of recommendation techniques in composition of complex service mashups, ad-hoc social networks, and green cloud environment. The papers introduced in this special issue illustrate the efficiency and effectiveness of services computing and cloud computing, while demonstrating a variety of challenges that arise. It is expected that this special issue, as a whole, will provide integrated and synthesized view of the current state of the art, identify key challenges, possible research directions and opportunities for investigation, and promote community-building efforts among researchers and practitioners in the related fields.

The articles in this special section focus on emerging Web Services. The papers address the latest advances in Web Services selection, discovery and recommendation. These advances consider context awareness, quality of service, quality of experience, service usage history and evolution, and cost effectiveness.

With the development of cloud computing,as a cloud computing service, cloud storage widely applied to enterprises and people's daily life in the form of the public cloud storage, hybrid cloud storage , internal cloud storage. In the current internet environment, the annual investment in providing public cloud storage services is more than ?500 million. More and more companies have joined the R & D team on cloud storage. Because the product differentiation of Cloud storage products is small, the personal preferences of user groups is completed, the market is a buyer's market, and other reasons. Further, the profit of private cloud storage model is also not clear. Cloud storage pricing model has become the focus for the user and vendor. In this article, the authors will be starting from different pricing strategies to discuss the pricing model of cloud storage.

This paper mainly develops the operation strategy of an enterprise drive based on viral marketing. Firstly, it introduces the concept, the features and successful cases of viral marketing. Next, it introduces what is Kingdee cloud drive and shows the comparative results among Kingdee cloud drive and other drives. With respect to the market positioning, this paper designs the operation strategy. Based on viral marketing, the detailed progress and reward mechanism are put forward in order to attract more users. Finally, the lottery model is developed based on the analytic network process in the reward mechanism.

Matrix multiplication is used in a variety of applications. It requires a lot of computation time especially for large-scale matrices. Parallel processing is a good choice for matrix multiplication operation. To overcome the efficiencies of existing algorithms for parallel matrix multiplication, a matrix multiplication processing scheme based on vector linear combination (VLC) was presented. The VLC scheme splits the matrix multiplication procedure into two steps. The first step obtains the weighted vectors by scalar multiplication. The second step gets the final result through a linear combination of the weighted vectors with identical row numbers. We present parallel matrix multiplication implementations using MapReduce (MR) based on VLC scheme and explain in detail the MR job. The map method receives the matrix input and generates intermediate (key, value) pairs according to the VLC scheme requirement. The reduce method conducts the scalar multiplication and vectors summation. In the end, the reduce method outputs the result in the way of row vector. Then performance theoretical analysis and experiment result comparing with other algorithms are proposed. Algorithm presented in this paper needs less computation time than other algorithms. Finally, we conclude the paper and propose future works.

Enterprise Social Network (ESN) service is getting more popular recently. It can help employees to communicate and collaborate efficiently with colleagues, with customers and with suppliers. One significant phenomenon happening on ESN is question & answer: people posting questions to the network to get answers from friends or friends-of-friends. However, existing ESN platforms do not have good support to this process. In this paper, we propose a method to better support question & answer on ESN, purely by using a graph analysis approach. Based on the questioner's initial input list of potential answerers, it can extract a shared-interest group of people, whose interest is close to the initial list of potential answerers, and sort the group of people according to a score of interest distance, and then recommend them to the questioner. To evaluate its applicability, the method is implemented in KDWeibo the most popular ESN platform in China, and the results are promising.

With the development of the society, distance education and training gradually shows some characteristics similar to the service industry, such as invisible, cannot be stored, heterogeneous, serving and consuming in the same time and so on. To gain a bigger market share in competition and more reputation and brand value in development, distance education and training institutions must pay great attention to the satisfaction and acceptation of its students for its teaching. The rapid development of modern Information Technology (IT) brings new opportunities to promote the quality of distance education services. In this paper, we design an evaluation index system based on an "IT application fields - service quality promotion" two dimensions framework. The dimension of IT application fields includes academic and non-academic student support services. The dimension of service quality promotion includes tangibles, reliability, responsiveness, assurance and empathy. The quality of student support services is measured by the SERVPERF method. Finally, we also provide a case study from The Open University of China(http://ouchn.edu.cn/) to validate our assumptions.

This paper studies a predicting model of the reverse logistics and its application in beer bottle recycling process. The model is composed of long-term growth trend and seasonal variation where long-term growth trend is described by gray model and seasonal variation is described by seasonal index. The reverse logistics value chain is analyzed by RMM. Then this paper studies two recovery paths of the beer bottle and all links of paths which reveals the application scenarios of the predicting model of the reverse logistics. Some suggestions are put forward in the end.

Positioned as the backbone of service asset management console, a service registry has to enable real-time and offline service selection in an effective manner. This paper presents an analytic algorithm that is used to guide the architectural design of service exploration in a service registry. Service assets are proposed to be framed into a well-established categorical structure based on pattern recognition algorithm. This design aims to provide systematic methodology and enablement architecture for analyzing, clustering, and adapting heterogeneous services for dynamic application integration. The exploitation of pattern recognition algorithm maps a large amount of services into a manageable feature space, which consists of attributes that are related to static description and dynamic features, such as historical QoS and service-level agreement. The proposed architecture and associated service exploration methodology have been integrated into an industry strength service-oriented architecture solution design platform. We also present a case study using the developed platform to illustrate the proposed algorithm for business service clustering and selection.

Cloud computing has been attracting much attention since its birth. How to cloudify software systems especially legacy applications in the cloud era is becoming increasingly important. Based on RGPS meta-model framework and International standards-ISO/IEC 19763, an architecture for cloudification of legacy applications is proposed, which consists of three parts: a Web portal, a SaaS service supermarket, and a SaaS application development platform. In this paper, we take an open source software as an example to illustrate the proposed approach. Based on the architecture and supporting techniques on software virtualization and multi-tenancy, we develop a prototype Cloud CRM to demonstrate the basic procedure for cloudification of legacy applications, as well as the feasibility of the proposed approach.

Enterprise management is improving its capacities, with its practice being advanced. However, it is facing greater challenge of transformation and upgrading, as the result of fiercer competition and accelerated globalization driven by global economic changes and IT revolution. Based on three years of studies of thousands of enterprises in 12 industries, jointly conducted by the experts and scholars from Kingdee International Software Group Company Limited and famous business schools, the article puts forward Radar Management Model (RMM) and Enterprise Management Maturity Model (E3M) from the perspective of system engineering, which provides systematic theory and engineering guidance for enterprise transformation and updating. Using the models, enterprises are able to diagnose the problems with their business and management, and identify their management maturity level, as well as priority and strategy of business improvement.

Cloud computing, a new paradigm of distributed computing, introduces many new ideas, concepts, principals, technologies and architectural styles into enterprise service-oriented computing. The enterprise service-oriented architecture (ESOA) style is an abstraction of concrete enterprise service-orientated architectures, which includes SOA architectural elements, service design patterns as well as principles, and SOA quality attributes. It can be extended to a new style for realizing enterprise cloud computing. Meanwhile, the principles and style of enterprise service-oriented computing facilitate the enterprise-wide adoption of cloud computing. This paper extends the ESOA style to a new hybrid architectural style, Enterprise Cloud Service Architecture (ECSA). The style is described by extending enterprise service-oriented formula for ESOA. We model the style through specifying each element in the formula with both service-oriented and cloud architectural styles.

Software architectures evolve over time, and so do the models that represent them. For a domain like Service Oriented Architecture (SOA) this is particularly true because most SOA solution designs are based on modification of existing assets that change over time. However, today there exists only limited work that reasons about this evolution. In this work we present our framework for traceability of evolving architectures that we apply for SOA solution design. Our design approach is based on an iterative process that utilizes a set of solution patterns to guide architects in the SOA solution design. Our approach utilizes historical data about pattern enablement and uses that to guide architects in selecting the right patterns. To ensure that the right patterns are used, we use a template matching approach that enforces conformance by allowing only the right set of artifacts to be composed together. We demonstrate how our framework can be applied to compose and trace evolving SOA solutions based on three views - the artifact view, profile view and compliance view.

SOA and cloud computing have brought new opportunities for the long expected agility, reuse and the adaptive capability of IT to the ever changing business requirements and environments. But due to the immature nature of the rapidly evolving technologies, especially in the areas of security, service or information integrity, privacy, quality of service and their possible detrimental consequences, many enterprises have been hesitating to make the shift. This paper adopts the concept of insurance and establishes a framework and the supporting reference model for cloud computing. We utilize the value-at-risk (VAR) approach to establish several appropriate mechanisms, and use a set of measurable metrics. Those quantitative or qualitative metrics can be applied as the basis for the business value and risk assessment, and eventually for insurance premium and compensation calculation for the failures of the services offered in Cloud environment. This model can also establish a potential new innovative market branch for the insurance industry.

Services Computing has become an increasingly important area in the IT and business sectors. In particular, Services now account for more than half of the economy in the United States and other countries. Numerous Services Computing-related degree programs and accreditation processes are being created. However, very few systematic guidelines exist for building graduate programs for Services Computing. In this paper, we present a reference model of the Masters Program in Services Computing for academic institutions and accreditation agencies as a relevant curriculum guideline. Specifically, the core and elective courses are introduced to help build the reference program. The inter-connections between core and elective courses are also illustrated to help create concentration programs based on the introducing sequences of the courses. Some practices of delivering Services Computing related courses and conducting accreditation application process are presented in this paper to help others more rapidly initiate the adoption process of the Services Computing curriculum.

The Editor-in-Chief (EIC) welcomes you to the first issue of the IEEE Transactions on Services Computing in 2010. In this issue, he is pleased to publish six research papers, which include two regular submissions and four papers from a Special Section on Transactional Web Services. In this editorial preface, he introduces these papers in the context of the body of knowledge areas.

Service oriented architecture (SOA) is one central technical foundation supporting the rapidly emerging cloud computing paradigm. To date, however, its application practice is not always successful. One major reason is the lack of a systematic engineering process and tool supported by reusable architectural artifacts. Toward this ultimate goal, this paper proposes a variation oriented analysis method of performing architectural building blocks (ABB)-based SOA solution design for enabling cloud application design. We present the modeling of solution-level architectural artifacts and their relationships, whose formalization enables event-based variation notification and propagation analysis. We report a prototype tool and describe how we extend the Unified Modeling Language (UML) mechanism to implement the system and enable solution-level variation analysis and enforcement in business cloud as an example.

The Service registry, the yellow pages of Service-Oriented Architecture (SOA), plays a central role in SOA-based service systems. The service registry has to be scalable to manage large number of services along with their requirements on storage and discovery. Based on our previous work on feature-based services quantification, we characterize services according to their diverse functional and non-functional requirements, and represent them as string formats which can be stored, probed, and indexed by efficient data structures, such as hash table and Bloom filter. Then, we propose a comprehensive service-storage solution using the counting Bloom filter (CBF). The application of CBF enables us to structure candidate services into separate groups, resulting in an accelerated services discovery process. The contributions of this research work include a new approach to manage large number of services based on quantified service features, and a storage architecture design to support service discovery. Experimental results strongly support these claims.

This paper provides an artifact-pattern-matching framework and mathematical model to analyze the dynamic behaviors of the SOA solution design in model driven fashion and provide recommendations for optimal solution pattern enablement for solution artifacts. The artifact-pattern-matching system can be dynamically tuned based on the practitionerspsila final selections of there commendations. Specifically, we propose a set of solution patterns to guide SOA solution architects through the process of consuming and configuring SOA artifacts for composing SOA solutions. The resulting multi-dimensional cascading flagging method is also presented in this paper. As an example, impact analysis patterns are used as solution patterns to support traceability enablement. We present some future directions of leveraging reinforcement learning algorithms to enrich the design quality analytics of SOA solution.

Cloud computing is evolving as a key computing platform for sharing resources that include infrastructures, software, applications, and business processes. Virtualization is a core technology for enabling cloud resource sharing. However, most existing cloud computing platforms have not formally adopted the service-oriented architecture (SOA) that would make them more flexible, extensible, and reusable. By bridging the power of SOA and virtualization in the context of cloud computing ecosystem, this paper presents seven architectural principles and derives ten interconnected architectural modules to form a reusable and customizable cloud computing open architecture (CCOA). Two case studies on infrastructure and business cloud are used to deliver business and practical value of infrastructure and business process provisioning services over the Internet. We also present some potential value-added services of the proposed CCOA to guide strategic planning and other consulting practices of cloud computing.

In this paper, we present an architecture-centric assessment approach for model evaluation over reference architecture to quantitatively estimate architecture maturity and quality. Such assessment is essential to support design-level refinement for an enterprise solution. To achieve this analytic goal, we select a nine-layer SOA solution stack (S3) as reference architecture, and introduce the necessary mathematical definitions and formulation. The baseline for such assessment is a model template composed of S3 solution patterns. A template is the starting point of creating a design model. The selection of such template will largely determine the architecture properties of the final SOA solution.The maturity analysis is carried out at different granularity levels (architecture building block, architecture layer, and architecture model) to justify the 'completeness' of a design. The quality assessment is accomplished through a set of quality-indicators to justify the 'goodness' of an architecture based on the relationships of architecture building block instances. Finally, using UML 2.0 to capture the model of S3, we provide a real assessment prototype developed over IBM RSA platform.

This tutorial will take the audience through an "aggregated" SOA engagement and discuss key processes, activities, and deliverables through the entire life cycle, especially in service modeling, realization, integration through an enterprise service bus and governance. Best practices and some anti-patterns will also be presented and discussed. It is mostly based on some of the speakers' pioneering project practices and lessons learned since SOA and Web service's inception.

Summary form only given. Services computing has become a foundational discipline of modernizing services and software industry. Services computing curriculum initiative (SCCI) is a community-driven professional activity which is sponsored by the IEEE computer society technical committee on services computing (TC-SVC). This session will present the latest advancements of SCCI in terms of newly formalized knowledge areas, case studies, and best practices of creating and delivering services computing courses. Several adoption approaches were introduced based on co-design and reuse principles for various degree programs. This paper also shares with you some long-term visions and latest lessons learnt from experienced professors and practitioners in the community.

The rapid adoption of model-driven design (MDD) methodology in SOA-based solution design requires an adaptive tooling environment that can systematically improve designers' productivity. Ideally, the environment should be flexible enough to both handle frequently changing requirements and support new features without intensive coding efforts. In this paper, we provide a coding-free enablement framework to realize such extensible tooling environments based on a mathematical abstraction of key models in SOA solution design using graph theory definition. This abstraction formalizes the SOA modeling logic and semantics, and also guides the implementation of an extensible and customizable tooling environment. As a case study, we illustrate how our framework is able to transform the development style from Java programming to text editing through our implementation of a UML 2.0 based SOA modeling environment using IBMpsilas Rational Software Architect (RSA) development platform.

The current design for the service registry architecture lacks a well-organized categorical structure and service- aware exploration method to enable effective real-time and offline services selection. To address this issue, this paper proposes an architectural framework and enabling technology for a business services analyzer that supports analyzing, clustering and adapting heterogeneous services for dynamic application integration. The proposed systematic services exploration methodology includes services categorization, services clustering and services exposure. By applying pattern recognition algorithm, we build a manageable feature space that is able to select and expose a service to serve the request from a repository with "large" amount of available services. To illustrate our design, we also provide a research prototype called Services Litmus Test (SLT) toolkit, which provides a flexible software platform for executing systematic services exploration procedures. The GUI based human assisted tune-up interface makes it very convenient for the services system designers to customize their design according to the adaptive system requirements.

In our earlier work, competitive Web services market was proposed to address one of the major business concerns, namely "competitiveness", of the current Web services research. This paper aims to attack the issues of how to model and analyze competitive Web services market, which ranges from the service node at the micro level to the competitive composite service providers at the macro level. Moreover, in order to tackle and contain the randomness embedded in a Web services system, we apply the Markov chain theory to analyze the availability of Web services market. Moreover, we analyze the stochastic performance of the Web services market using queuing theory and propose the adaptive control methodology to improve the performance.

This panel is devoted to the topic of Services Architectures, which play a significant role in the effective operations and delivery of services businesses today.

Web services technology has received much attention in the last few years, and a lot of research efforts have been devoted to utilizing services on the Internet to fulfill consumers' requirements. However, little research has been done on the current status of web services on the Internet, which has a great impact on current research. Enlightened by this situation, we made an exploratory study of the current status of web services on the Internet. Our study mainly focused on the investigation of four aspects, including the number, complexity, quality of description and the function diversity of available web services on the Internet. A web services investigation system is built up to harvest web services from the Internet and calculate the statistical results. The investigation results are reported in this paper, and, based on our study, the development trend of web services technology is also discussed in this paper.

In this tutorial, we present the results of abstracting a reference architecture for SOA based on multiple projects during the past 6 years. As an example SOA Solution Reference Architecture, the SOA Solution Stack (S3) defines the layers, architectural building blocks, design decisions, patterns, options and architectural decisions and the separation of concerns needed to model, architect, assemble, deploy and manage an end-to-end solution in the context of a service-oriented approach. The SOA Solution Stack (a.k.a. Service-oriented Solution Stack) provides a blue print for an enterprise or application architecture scope. The SOA Solution Stack is based on establishing the building blocks of SOA: services, components and flows that collectively support business processes and goals. The meta-data underlying each layer and relationship between layers can further facilitate SOA in bridging the gap between business and IT from solution modeling to solution realization.

The word "Web X.o" represents "Web eXpandoo" which is derived from "Web" and a misspelled word "Expandnoon". "noon" is interpreted as "the highest, brightest, or finest point or part" or "the highest point". This paper introduces some basic features of Web X.o that expands the current Web to the "highest and brightest" point. The key concept of Services HyperChain in the Web eXpandoo framework is introduced to provide an enabling technology architecture that supports the future Web, which coordinates multimedia presentations, human interactions, business processes, computing resources, and business applications in a uniform fashion. Today's Web 2.0 technologies are mainly dealing with the presentation and community-based interaction aspects in the Web X.o framework. The boundary among Web, desktop applications, and backend resources are removed by creating Services HyperChain in the Web eXpandoo framework. The IEEE Body of Knowledge on Services Computing system is used as a prototype to illustrate how the Service-Oriented Architecture (SOA) and Web 2.0 technologies are used to to realize the Services HyperChain with unified services resource and services ecosystem enablement architecture of Web X.o.

The rapidly emerging technology of Web services paves a new cost-effective way of engineering software to quickly develop and deploy Web applications by dynamically integrating other independently developed Web-service components to conduct new business transactions. This paper reports our efforts on designing and developing a Web service of pass-through authentication (PTA) for 12 online electronic-payment Web applications. In accordance with how a PTA service is developed and integrated with a corresponding back-end e-payment system, our strategies can be categorized in three stages: end-to-end integration stage, Web-services-enabled stage, and Web-services-oriented stage. Derived from real-world industrial experience, this three-stage pathway can be applied to a broad range of Web-application development projects to guide smooth transformation from a specific application-oriented design and development model toward a reusable Web-services-oriented model. Furthermore, this paper contributes to an engineering process that leads to practical Web-services-oriented software development. New research issues revealed by this project are also reported.

For period of time customers have demand for more reusable and manageable service-oriented components for order-to-cash (O2C) solution so they can be easily reconfigured and managed to adapt to business changes quickly. In this paper, we present a service-oriented business process optimization model that minimizes potential revenue leakage through process improvements. We introduce service componentization approach to decompose business processes to identify reusable services in SOA solution context. We adopt Really-Simple-Syndication (RSS) technology to realize a collaborative dispute management solution based on service-oriented architecture (SOA). The proposed approach can help streamline the dispute management process with revenue increase and higher customer satisfaction

A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback

Summary form only given. Service-oriented architecture (SOA) has been proven to be a flexible and extensible architecture for designing and realizing industry solutions and applications. Enterprise Service Bus (ESB) is a hub for integrating different kinds of services through messaging, event handling, and business performance management. This tutorial will focus on a SOA solution framework; the critical role and value proposition of an ESB in SOA and Web services; ESB (and SOA) analysis and design methodology; best practices for the practical design and implementation of an ESB, including ESB design using the enterprise integration and application integration patterns; ESB and business process integration tools and techniques for ESB implementation; and performance, security and transaction management. This tutorial is based on numerous projects and solution architectures that the authors and colleagues have been engaged in the last 3 years in various industries, including government, financial, retail, electronics and distribution.

As Web services become more prevalent, the need to ensure their quality increases. This paper explores the criteria of reliability of Web services-oriented systems, and discusses how to design and generate test cases to conduct tests over Web services. A prototype system is constructed to test the effectiveness and efficiency of our algorithms. The preliminary results show that our approach facilitates the testing of services-oriented systems.

In this paper, we propose a dynamic service discovery framework from Web services representation chain. The major components include Web service chains precategorizing module, service exploration engine, services container, chain change detection module with associated control parameters. From the working research prototype, our proposed mechanism enables businesses to easily retrieve up-to-date Web services linked and nested multi-level deep in the service description documents. In addition, a federated Web services discovery portal is illustrated to show how the research results are aggregated from hyperlinked WSIL documents as well as UDDl registries for achieving more accurate services exploration result.

In this paper, we present the Web services outsourcing manager framework via a mathematical model for dynamic business processes configuration using existing Web services to meet customers' requirements. An XML-based annotation document is proposed to capture the business requirements and used to dynamically generate search scripts for an advanced Web services discovery engine to find Web services from UDDI registries and Web service inspection language documents. A list of available Web services is returned for further composition and optimization to produce the final business process. This paper proposes a novel mechanism to map a service selection problem into a solution space {0,1} to utilize gold optimization algorithms such as genetic algorithms (GA). A working research prototype has been implemented to demonstrate the feasibility of the on-demand Web services flow composition.

In this paper, a manageable Web services hub framework and enabling technologies for e-sourcing are proposed for buyers, suppliers, service providers and trading partners to register business, user and service information as well as to provision and subscribe to services. The proposed framework enables a business to dynamically configure its business processes to integrate with existing Web services provided by other enterprises based on service requirements. Next, a multi-level security mechanism ensuring secure communications in a hub or among hubs is presented. An advanced Web services discovery mechanism is introduced after that for looking up multiple UDDI registries and aggregating results in an efficient manner. A working system of this framework, Managed e-Hub, which provides an infrastructure for enabling next generation e-utilities in a secure and controlled environment, is also presented in this paper. Some further research ideas are given at the end of the paper.

HotMedia is a novel scalable solution for delivering interactive rich media over the Internet. It is a delivery-suitable file format that can contain heterogeneous compositions of media bit streams as well as meta-data that define the behavior, composition, and interaction semantics. This enables the creation of lightweight single-file representations of interactive, multiphase presentations involving multiple media-type content. The HotMedia client has a smart content algorithm that infers types from the incoming data stream and fetches the media renderer components, user-interface components, and hyper-linked action components, all just-in-time, resulting in progressive and contest driven enrichment of the user experience. Internet users get a simple initial experience with minimal latency followed by enrichment of this experience. HotMedia has an open and extensible architecture, which enables and encourages the inclusion of new media-types, user-interfaces, and hyper-linked actions. By separating media-rendering from action-performing, it lets all media handle the same actions. HotMedia can also track on the server-side, user interactions and user-experience associated parameters. In its simplest usage HotMedia requires no more than a regular web server for delivery and no client-side preinstallation.