A service ecosystem refers to a multilateral network composed of heterogeneous service entities, where the exchange of data, resources, and value through interactions among specific participants forms a service pattern. As service ecosystems like virtual hospital alliance (VHA) evolve towards large-scale, multi-domain integration to meet complex user needs, service pattern fusion has emerged as a fundamental approach to leverage data, resources, and value aggregation. By converging elements from multiple patterns, service pattern fusion enables the fulfillment of composite business objectives with reduced redundancy and lower costs. Existing works primarily address fusion requirements by reorganizing existing services through approaches such as service composition and business process management where only service functions and workflows are considered. However, they lack formalization of pattern fusion constraints and fail to support comprehensive integration of participants, data, resources, and value, let alone identifying optimal fusion solutions that account for participant collaboration and service integration. In this study, we formally define the Service Pattern Fusion Problem (SPFP) as an optimization task aimed at identifying the most efficient and cost-effective pattern by integrating, combining, and pruning elements from multiple patterns while preserving their objectives and meeting business constraints. We adapt traditional heuristic methods to SPFP and propose the Fusion-Oriented Confidence-Aware genetic algorithm (FoCa). FoCa dynamically adjusts the search space and transition probabilities in each iteration, achieving optimal fusion results with a 41.09% reduction in pattern loss and the fastest convergence. In addition, we designed a set of pattern features and conducted random fusion experiments on the public service pattern dataset S-SPD, to explore the correlation between those features and the optimization magnitude across various metrics. The analysis helps identify which types of service patterns benefit most from fusion, providing valuable insights for researchers and practitioners in both academic and engineering contexts.

Recently, multimodal recommendation (MMRec) has received much attention, which models user preferences based on both user behaviors and modality information. Although current graph neural network based methods yield notable results in MMRec, certain limitations persist among these methods. 1) Most methods rely on pre-trained networks to extract modality features but fail to remove modality noise. 2) Recent methods leverage InfoNCE strategy to align representation, while ignoring the effect of feature redundancy and lacking sufficient alignment between different modality features. Such limitations ultimately harm the recommendation performance. To this end, we propose a Dual Mutual Information-Driven Multimodal Recommendation Model with Denoising Graph Autoencoder (DMIGA). Specifically, to reduce the noise within modality features, we design a denoising graph autoencoder with a cross-modal consistency constraint. Furthermore, we propose a dual mutual information learning mechanism on both feature and instance levels, to reduce the feature redundancy and align different representations. Experimental results on three real-world datasets consistently demonstrate that DMIGA outperforms state-of-the-art methods, with an average of 3.8% improvement.

With the vast range of available services, it has become an important challenge to recommend the optimal service for mashup developer. Recent studies are mainly limited by the service similarity, resulting in challenges such as discrepancy in textual semantics, implicity of inter-service relationships, and the sparsity of historical interactions. Service bundles, which offer a set of services, present a novel approach to address the mashup development problem. In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE), to tackle the issues. Specifically, we introduce the Graph Propagation Module to encode potentially implicit semantic relations in the Mashup-Service-Bundle heterogeneous graph. Furthermore, we build a unified representation for the bundle in the Bundle Prediction Module by combining an autoencoder and spatial attention mechanism, enabling the integration of relationships across different node and edge types. Extensive experiments on real-world datasets demonstrate that HGAE notably outperforms state-of-the-art methods on all metrics, with improvements of 8.69% in NDCG and 9.55% in Recall on the ProgrammableWeb dataset.

Driven by the flexibility, reliability, and scalability of microservice architecture, an increasing number of enterprises are decomposing monolithic applications into microservices. However, existing deep learning-based decomposition methods rely heavily on partition number selection, which, if unreasonable, can lead to frequent microservice communication and reduced performance. Moreover, manual partition suggestions not only decrease automation but also fail to adapt to rapid business iteration, and existing methods inadequately capture the relationship characteristics between application classes. To address these issues, this paper proposes a novel graph-based partitioning technique, GGRME. It constructs a system dependency graph through static, dynamic, and semantic analysis, and then employs a self-supervised gated graph neural network combined with cross-supervised optimization for community detection to automatically generate microservice decomposition results. Experiments demonstrate that GGRME outperforms benchmark methods in 65 % of tests, yielding superior microservice decomposition performance.

Neuromorphic hardware systems—designed as 2D-mesh structures with parallel neurosynaptic cores—have proven highly efficient at executing large-scale spiking neural networks (SNNs). A critical challenge, however, lies in mapping neurons efficiently to these cores. While existing approaches work well with regular, fully functional mesh structures, they falter in real-world scenarios where hardware has irregular shapes or non-functional cores caused by defects or resource fragmentation. To address these limitations, we propose a novel mapping method based on an innovative space-filling curve: the Adaptive Locality-Preserving (ALP) curve. Using a unique divide-and-conquer construction algorithm, the ALP curve ensures adaptability to meshes of any shape while maintaining crucial locality properties—essential for efficient mapping. Our method demonstrates exceptional computational efficiency, making it ideal for large-scale deployments. These distinctive characteristics enable our approach to handle complex scenarios that challenge conventional methods. Experimental results show that our method matches state-of-the-art solutions in regular-shape mapping while achieving significant improvements in irregular scenarios, reducing communication overhead by up to 57.1%.

In the context of the development of the modern service industry, emerging technologies such as the Internet of Things (IoT) and 5G have promoted the integration of a large number of service devices, increasing the complexity of the service ecosystem and accelerating its evolution process. Although the service model has summarized the business relationship in the service ecosystem from the four aspects of workflow, data flow, resource flow and value flow, it has not formed a systematic description of the heterogeneous devices where the service is deployed. Therefore, future service ecosystem modeling methods need to solve the following two problems: how to describe heterogeneous devices to provide guidance for the deployment of services, and how to enable the service ecosystem to adapt to the dynamic adjustment of services.In this paper, in order to solve the problem of dynamic addition and deletion of services and dynamic replacement of deployment carriers, we propose a service deployment description method(SDDM) based on holon concept, and integrate it with service pattern, then extend service pattern description language , namely carrier SPDL (SPDL-C). To validate our framework, we empirically conducted a case study in which we selected an intelligent warehouse management service pattern as the object of study to reveal how our approach could address future challenges. Finally, we summarize and discuss the innovation and significance of the work.

Recommendation is a key mechanism for modern users to access items of their interests from massive entities and information. Recently, graph contrastive learning (GCL) has demonstrated satisfactory results on recommendation, due to its ability to enhance representation by integrating graph neural networks (GNNs) with contrastive learning. However, those methods often generate contrastive views by performing random perturbation on edges or embeddings, which is likely to bring noise in representation learning. Besides, in all these methods, the degree of user preference on items is omitted during the representation learning process, which may cause incomplete user/item modeling. To address these limitations, we propose the Adaptive Fusion of Multi-View Graph Contrastive Recommendation (AMGCR) model. Specifically, to generate the informative and less noisy views for better contrastive learning, we design four view generators to learn the edge weights focusing on weight adjustment, feature transformation, neighbor aggregation, and attention mechanism, respectively. Then, we employ an adaptive multi-view fusion module to combine different views from both the view-shared and the view-specific levels. Moreover, to make the model capable of capturing preference information during the learning process, we further adopt a preference refinement strategy on the fused contrastive view. Experimental results on three real-world datasets demonstrate that AMGCR consistently outperforms the state-of-the-art methods, with average improvements of over 10% in terms of Recall and NDCG. Our code is available on https://github.com/Du-danger/AMGCR.

With the growth of cloud computing, a large number of innovative mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for API recommendation. These approaches face several challenges such as API compatibility dependencies, ambiguous definition and boundary dilemmas of APIs, and sparse API invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and APIs to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail Web API recommendations.

Neuromorphic hardware is a multi-core computer system specifically designed to run Spiking Neuron Network (SNN) applications. As the scale of neuromorphic hardware increases, it becomes very challenging to efficiently map a large SNN to hardware. In this paper, we proposed an efficient approach to map very large scale SNN applications to neuromorphic hardware, aiming to reduce energy consumption, spike latency, and on-chip network communication congestion. The approach consists of two steps. Firstly, it solves the initial placement using the Hilbert curve, a space-filling curve with unique properties that are particularly suitable for mapping SNNs. Secondly, the Force Directed (FD) algorithm is developed to optimize the initial placement. The FD algorithm formulates the connections of clusters as tension forces, thus converts the local optimization of placement as a force analysis problem. The proposed approach is evaluated with the scale of 4 billion neurons, which is more than 200 times larger than previous research. The results show that our approach achieves state-of-the-art performance, significantly exceeding existing approaches.

In the big data era, Federated Learning (FL), which allows multiple participants to collaboratively train a global model without sharing their raw data, emerges as a promising solution to address the challenges of isolated data silos and privacy protection. Federated learning has two main communication strategies: synchronous and asynchronous. Synchronous FL ensures stable convergence but may encounter model quality degradation and server crash risks. Asynchronous FL avoids the straggler effect and supports more participants, but unstable convergence and non-IID data could affect the model performance. In this paper, inspired by real-world FL scenarios, we propose a highly efficient K-Asynchronous FL framework, KFLBSV, which addresses the limitations of synchronous and asynchronous strategies to some extent, leading to improved model performance and convergence speed. The framework allows clients to upload updates multiple times within the same round instead of blocking after each upload, thereby enhancing training efficiency. To ensure the stability and performance of the global model, we introduce a novel aggregation method. By approximating Shapley value to assess model consistency and balancing client contribution frequency and model staleness, we allocate weights more accurately to each participating client. We extensively conducted experiments on benchmark datasets using three distinct models, and the results show that KFLBSV outperforms existing algorithms in terms of both model performance and convergence speed.

Nowadays an increasing number of software developers have joined in open-source software development communities such as GitHub, and develop and share softwares in these communities. Those online communities contain a huge volume of open-source repositories. Developers commonly search from existing repositories and intend to find suitable repositories to their development requirements. However, it is time-and energy-consuming to discover suitable repositories from such a large number of candidates and it may be also hard for developers to choose accurate keywords. So an effective repository recommendation service becomes an indispensable tool for developers. There have been some solutions for repository recommendation, but existing solutions have several defects such as mediocre accuracy and ignorance of useful features. In this paper, we develop a new personalized repository recommendation service with multi-modal features learning. We propose to mine two modes of features and jointly utilize the mined multimodal features. One of the features is the developers’ sequential behavior features and the other is text features of repositories. We design novel features learning mechanisms for the two modes of features. We performed sufficient experiments on a real-world dataset and the experimental results demonstrate that our model generates superior recommendation results and produces an improvement of 15.3% and 14.5% in Precision and Recall compared to well-known existing methods.

With the development of the Internet of Medical Things (IoMT) , indoor wireless sensor networks (WSNs) have been used to monitor Alzheimer's disease patients daily and guide their behaviors. Alzheimer's disease may seriously impact patients’ memory, and thoughts of “what should I do” can unexpectedly form in their mind. This cognitive impairment can affect patients’ independence and well-being. As a basic infrastructure for future healthcare systems, WSN can collect patient behaviors, such as their positions and states, to support safety and health analyses. Therefore, this paper proposes a probabilistic model checking-based method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception. First, the layout of the home environment is abstracted as a formal grid, and a user activity model (UAM) is proposed in the form of discrete-time Markov chain (DTMC) to describe patients’ activity based on data collected by sensors. Second, because Alzheimer's patients with mild cognitive impairment (A-MCI) often forget their next daily activities, we classify and describe their daily behaviors as verification requirements in the form of probabilistic computational tree logic (PCTL) . Then, the UAM is input into a probabilistic model checking tool and compared against the verification property PCTL to calculate the probability values and assess temporal behaviors. As result, the activity with the largest probability is selected for behavior guidance. Third, we demonstrate the process of detecting abnormalities, including activities with abnormal temporal behaviors and activities with normal temporal behaviors but unexpected probabilities that may be repeated more than twice. The key states are extracted from the UAM to specify the verification properties for abnormality detection. Finally, a case study is presented to demonstrate the usability and feasibility of our proposed method.

The service pattern is an abstraction of the business relationship among various participants from the service ecosystem in four aspects: workflow, data flow, resource flow, and value flow. In order to optimize service patterns, it is necessary to consider the collaboration between participants as well as the interaction among different servers. The existing works either optimize the former by adjusting service orchestration, such as business process optimization and workflow optimization, or focus on the latter through adjusting service distribution, such as cloud service distribution optimization and edge service deployment optimization. However, the prevalence of service ecosystems and distributed computing has begun to make multi-user, multi-server scenarios commonplace, placing greater importance on fast and effective optimization of service patterns. In this work, we summarize the constraints and objectives and formally define the service pattern optimization problem. Beyond that, we propose a service pattern optimization-oriented confidence aware recurrent simulated annealing algorithm (PooCa). Experiments conducted on an existing dataset show that our method outperforms the other three baselines on the overall dataset as well as on the eight subsets. Also, our method can reduce the number of search iterations by 41.15% on average with the same search space. We also carry out case studies on the online travel booking service pattern and investigate factors that make patterns perform better.

Microservice architecture promotes the cost reduction, efficiency increase, and quality improvement of software development. However, with the diversification of manufacturers’ technology and the complexity of the services, the existing research on unified access to microservice lacks a specification that can be summarized, and more intrusive transformations of access service are required in the access process. Aiming at the standardization of unified access of microservice, the Service Unified Access Model (SUAM) is proposed. The main purpose of the model is to solve the complexity of multi-language and multi-platform access of the microservice and the standardization of the access process. The model makes a contribution to the service from three aspects: service resources, product resources, and function properties. This model can not only describe the functionality of the service in more detail but also can help reduce the amount of access code by 15% without affecting the business function of the accessed service.

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.

Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively. In this article, we propose a service pattern assessment framework which consists of two parts. As part one, we complement the service pattern description language (SPDL) with extended elements and observable attributes to empower it with quantitative analysis, namely Quantitative SPDL (SPDL-Q). In part two, a set of service pattern assessment metrics are designed to assess not only the quality of the services but also the cooperation efficiency of the participants and the orchestration effect of the service patterns elements. The proposed framework was then further validated by a case study, of which four E-commerce service patterns were studied to reveal their evolvement processes. Correlation experiments were also performed to identify the pattern features that have the greatest impact on each metric, so to provide guidance and suggestions for pattern design. Finally, the innovation and significance of the work are outlined and discussed.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively.

Mobile Edge Computing (MEC) has already developed into a key component of the future mobile broadband network due to its low latency. In MEC, mobile devices can access data-intensive applications deployed at edge, which are facilitated by service and computing resources available on edge servers. However, it is difficult to handle such issues while data transmission, user mobility and load balancing conditions change constantly among mobile devices, edge servers and the cloud. In this paper, we propose an approach for formulating Data-intensive Application Edge Deployment Policy (DAEDP) that maximizes the latency reduction for mobile devices while minimizing the monetary cost for Application Service Providers (ASPs). The deployment problem is modelled as a Markov decision process, and a deep reinforcement learning strategy is proposed to formulate the optimal policy with maximization of the long-term discount reward. Extensive experiments are conducted to evaluate DAEDP. The results show that DAEDP outperforms four baseline approaches.

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.

Web API recommendations have recently been studied extensively. However, recommending an API for a service is different than service intelligence. Web API automatic calls are widely used in question–answer dialog applications and service-composed workflow systems to achieve intelligent services. To finish an automatic Web API call not only requires the Web API ID, but also its input parameters. In this paper, we propose an end-to-end Web API automatic calls approach, named WAAC, that translates a goal’s natural language sentences directly to the Web API invoking sequences including its ID and parameters. This end-to-end approach based on the seq2seq encoder–decoder framework, adopts character-level RNN for the Chinese sentences and introduces a copying mechanism to retrieve API parameters. To train the network, a Chinese version dataset of over 1 million natural sentences and API invoking sequence pairs are generated with some manually labeled data and 72 real Web API invoking logs. Experiments obtain a 96% precision on predicting API invoking sequences and show that the character-level RNN and copying mechanism both contribute considerably to achieving a high precision Web API automatic call system for goal-oriented services.

As more business workflow systems are being deployed in modern enterprises and organizations, more employee-activity log data are being collected and analyzed. In this paper, we develop a latent ability model (LAM) as a generative probabilistic learning framework for workforce analytics over employee-activity logs. The LAM development is novel in three aspects. First, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. Second, we construct the latent ability model by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. Finally, we leverage LAM to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation. We evaluate the accuracy and efficiency of our approach using real log datasets collected from a workflow system deployed in the government of the city of Hangzhou, China, which consists of 5,287,621 log records over two years involving 744 activities and 1,725 employees. We show that LAM approach outperforms existing representative methods in both accuracy and efficiency.

The sentiment behind financial text has been observed to have correlations with stock market trend. Though widely discussed, the study on this topic faces the challenge coming from the lack of open dataset and labeled financial text. In this work, we collected a large amount of Chinese financial text from financial news, research report, stock BBS and corporate announcements. It contains 3 million articles about 128 stocks from 2010 to 2018. And then we proposed a model mapping from the text and latent sentiment to the abnormal market trend. It combines the posting amount, daily market index with the RBM-embedded document vector, and extracts the abnormal features via LSTM. After that a neural net is employed to identify the abnormal trend. The experimental results on our dataset show the effectiveness of our approach comparing to baseline methods.

Previous work has demonstrated that typography design has a great influence on users' reading experience. However, current typography design guidelines are mainly for general purpose, while the individual needs are nearly ignored. To achieve personalized typography designs, an important and necessary step is accurately evaluating user satisfaction with the typography designs. Current evaluation approaches, e.g., asking for users' opinions directly, however, interrupt the reading and affect users' judgments. In this paper, we propose a novel method to address this challenge by mining users' implicit feedbacks, e.g., touch interaction data. We conduct two mobile reading studies in Chinese to collect the touch interaction data from 91 participants. We propose various features based on our three hypotheses to capture meaningful patterns in the touch behaviors. The experiment results show the effectiveness of our evaluation models with higher accuracy on comparing with the baseline under three text difficulty levels, respectively.

Crossover service is a kind of services, which can provide multi-dimension service, great user experience and high values, through deeply converging services from different industries, different organizations and different value chains. Convergence is the key challenges for crossover service application. Using Alibaba's crossover service case, this paper illustrates five challenges of the service convergence process: pattern convergence, ecosystem convergence, environment convergence, quality convergence and value convergence. In addition, we propose a technical framework addressing these technical challenges, which includes all the major theories and models, techniques and methods, and tools and platforms supporting enterprises' crossover service convergence in the modeling phase, the design phase, the running phase and the management phase.

NgramCNN is a deep convolutional neural network developed for classification of graphs based on common substructure patterns and their latent relationships in the collection of graphs. Our NgramCNN deep learning framework consists of three novel components: (1) The concept of n-gram graph block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. (2) The diagonal convolution layer to extract local patterns and connectivity features hidden in the n-gram blocks by performing n-gram normalization before conducting deep learning through the network of convolution layers. (3) The extraction of deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep convolutional neural network. Extensive evaluation of NgramCNN using five real graph repositories from bioinformatics and social networks domains show the effectiveness of NgramCNN over the existing state of art methods with high accuracy and comparable performance.

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.

With rapid development of the Internet, all walks of life are engaged in the tide of Internet.In the era of "Internet+", traditional industries are widely developed and expand plenty of emerging business, such as online transactions, Internet finance and so on.However, various problems arise at the same time in this revolution.On one hand, business processes become increasingly intricate, and different fields may have difficulty in communication.On the other hand, developers are hard to understand the real demands from users and the rate of code reuse is not high.In order to solve these problems, we propose a middle-end and project manager (PM) oriented service language model, which could help decouple software development and user requirements, improve work efficiency and reduce development costs.

Deploying deduplication for distributed primary storage is a sophisticated and challenging task, considering that the demands of low read/write latency, stable read/write performance, and efficient space saving are all of paramount importance. Unfortunately, existing schemes cannot present a satisfactory solution for the aforementioned requirements simultaneously. In this article, we propose D$^{3}$, a dynamic dual-phase deduplication framework for distributed primary storage. Several major innovations are established in D$^{3}$. First, we formulate a deduplication-oriented taxonomy calledDedup-Type, to group data with similar deduplication-related characteristics into larger categories. It serves as coarse-grained filter and one of the prioritizing references in D$^{3}$. Second, D$^{3}$is a dual-phase framework—inline-phase and offline-phase deduplication processes work in concert with each other. Third, D$^{3}$operates in a dynamic manner. We design two critical mechanisms:context-aware threshold adjustment(CTA) for local inline-phase deduplication, anddeferred priority-based enforcement(DPE) for global offline-phase deduplication. The CTA mechanism enables selective deduplication under a periodically updated threshold. Data skipped during the inline phase is regarded as a candidate for offline phase, and is handled in a prioritized order under the governance of DPE mechanism. Evaluation results demonstrate that, compared with conventional inline and offline deduplication schemes, D$^{3}$achieves more efficient and stabler read/write performance with competitive space saving.

RESTful Web APIs have no description files like WSDL in traditional Web service. Although some REST API definition models have been arising recently, there is still lacking in structured description format for existing large mounts of Web APIs. Almost all Web APIs are documented in semi-structured web pages, and these documentation formats are various for different sites. It's hard for machine to read the semantics of Web APIs. In this paper, we have proposed a novel hierarchical recurrent neural network to convert REST API documentation to structured machine-readable description format -- the Swagger REST API specification. The network extracts the Swagger defined attributes of a REST API from HTML web pages without any feature engineering. With the extracted API specifications, we built an API repository to index, search and compose Web APIs. Experiment showed that the hierarchical RNN model performed well even with only a few training samples.

As a new service paradigm of Software-as-a-Service (SaaS),Business Process-as-a-Service (BPaaS).BPaaS is used to build a cost-effective Business Process Management (BPM) system.Based on universal Artifacts, we develop a framework named SeGA (Self-Guided Artifact).In SeGA, a BPM system is capable of executing business processes from multiple clients, and responding query at runtime.what's more, by the template-based cascading data mapping method, entity to be synchronized with database automatically, so each BP instance can modify its process entity without worrying about the database access.In this paper, we conduct a deep research and implement a prototype system for Artifact process design.

High reliability, efficient I/O performance and flexible consistency provided with low storage cost are all desirable properties of cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimum on all these properties is impractical. N-way Replication and Erasure Coding, two extensively-applied storage schemes with high reliability, adopt opposite and unbalanced strategies on the tradeoff among these properties, thus considerably restraining their effectiveness on wide range of workloads. To address the aforementioned obstacle, we propose a novel storage scheme called ASSER, an ASSembling chain of Erasure coding and Replication. ASSER stores each object in two parts: a full copy and a certain amount of erasure-coded segments. We establish dedicated read/write protocols for ASSER leveraging the unique structural advantages. On the basis of elementary protocols, we implement sequential and PRAM (Pipeline-RAM) consistency to make ASSER feasible for various services with different performance/consistency requirements. Evaluation results demonstrate that under the same fault tolerance and consistency level, ASSER outperforms N-way replication and pure erasure coding in I/O throughput under diverse system and workload configurations with superior performance stability. More importantly, ASSER delivers stably efficient I/O performance at much lower storage cost than the other comparatives.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Convolutional Neural Network (CNN) has gained attractions in image analytics and speech recognition in recent years. However, employing CNN for classification of graphs remains to be challenging. This paper presents the Ngram graph-block based convolutional neural network model for classification of graphs. Our Ngram deep learning framework consists of three novel components. First, we introduce the concept of n-gram block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. Second, we introduce a diagonal convolution step to extract local patterns and connectivity features hidden in these n-gram blocks by performing n-gram normalization. Finally, we develop deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep learning model using convolutional neural network. We evaluate the effectiveness of our approach by comparing it with the existing state of art methods using five real graph repositories from bioinformatics and social networks domains. Our results show that the Ngram approach outperforms existing methods with high accuracy and comparable performance.

Modern service industry (MSI) is becoming a leading and pillar industry in recent years. Its theory construction, however, has not kept up with the industry development. Specifically, the business process designing and reconstructing, the critical part in business transformation and competition of modern service enterprise, are still handled manually. The existing models, e.g., BPMN and EPC, mainly adopt the business activities and events without considering the resource and data generated and consumed in interaction with business collaborators, which is ubiquitous in MSI business process. Hence, the deficiency of resource and data in these models hindered them from popularization in MSI. In this paper, with a systematic analysis of the above issues, we introduce the service pattern of MSI business process from the point view of resource and data with formalized description and some concrete basic patterns. To support the process designing and reconstructing better, we propose a pattern-centered formalization language, called Service Pattern Description Language (SPDL). Furthermore, a service pattern matching approach, in the Constructing-As-Identifying style, is studied and a service process designing tool, called SPDL-Editor, is developed. Additionally, a case of the famous video-on-demand service (Youku) in China is given, though out of this paper, for a better understanding of our theories.

Specific requirements and characteristics of primary storage make designing desirable deduplication schemes a sophisticated task. In this paper, we propose DIODE, a Dynamic Inline-Offline DEduplication scheme that provides salient read/write performance and space-saving simultaneously for primary storage systems. DIODE orchestrates inline and offline processes in a dynamic manner. We propose two innovative mechanisms in DIODE: Context-aware Threshold Adjustment (CTA) for inline-phase deduplication and Deferred Priority-based Enforcement (DPE) for offline-phase deduplication, respectively. CTA mechanism enables selective inline deduplication under a dynamically updated threshold. Data skipped during inline-phase is regarded as candidates for offline-phase, and is handled in a prioritized order under the governance of DPE mechanism. Inline deduplication works in concert with offline deduplication so as to complement the weakness of each other.

Service-Oriented Architecture (SOA) is a widely accepted and engaged paradigm for the realization of business processes that incorporate several distributed, loosely coupled partners. However, how to work with service computing in a cloud environment is the latest challenge. Formal methods can play a fundamental and important role in service computing and cloud computing. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. The aim of FM-S&C 2016 is to encourage academic researchers and industry practitioners to present and discuss all formal analysis, modeling and verification related to research and experiences in a broad spectrum of services and cloud computing.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Modern service industry (MSI) is an information and knowledge intensive service industry, relying on information technology and modern management philosophy. The development of MSI is of high significance for promoting rapid global economy, accelerating social progress, and building an innovation-oriented society and harmonious realm. This paper first presents the history and trends of MIS in China in terms of the worldwide MSI development status. It then proposes the concept of Crossover Services based upon a survey of 62 MSI-related listed firms in China, whose business models, products, and services evidently exploit the concept. After elaborating on the crossover, convergence, and complex characteristics of crossover services, it proposes a technical framework that facilities addressing the scientific issues and technical challenges on crossover service realization. Finally, it illustrates how a new cloud-based middleware platform, named JTang++, supports the realization of crossover services.

The procedure of picking services bound to abstract tasks is usually called service selection in Service Oriented Architecture. In recent years, most studies focus on improving the Quality of Service (QoS) of the composed service. These techniques, however, are facing a new challenge, brought by the big data era, namely, the time and money wasted in data transmission, called transmission cost, cannot be optimized locally like QoS. To address this challenge, in this paper, we study and formalize the problem of transmission cost aware service selection, named TcSS. Owing to the insufficient service transfer rates, we propose a framework on a relaxation problem by making use of the service network ontology structure. The entire framework comprises two stages, an off-line stage to arrange the service network information from logs and an online stage to satisfy the service selection requirement efficiently. The solution of the relaxation problem is an approximation of the original TcSS with the approximate ratio guarantees. Finally, extensive experiments on real data establish the effectiveness and efficiency of our approach.

Web service has been an important solution to achieve resource sharing and application integration in the Internet era, which can develop the most promising software application with the on-demand changing computing paradigm, through service reuse and dynamic synthesis. One of the latest challenges is how to work with service computing in a cloud computing environment. There is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. The topic of FM-S&C 2015 is the theory aspect of data intensive services and formal methods. It encourages academic researchers and industry practitioners to present and discuss formal analysis, modeling and verification related researches and experiences.

High reliability, low space cost, and efficient read/write performance are all desirable properties for cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimality on these properties is unrealistic. Since reliable storage is indispensable prerequisite for services with high availability, tradeoff should therefore be made between space and read/write efficiency when storage scheme is designed. N-way Replication and Erasure Coding, two extensively-used storage schemes with high reliability, adopt opposite strategies on this tradeoff issue. However, unbalanced tradeoff designs of both schemes confine their effectiveness to limited types of workloads and system requirements. To mitigate such applicability penalty, we propose MICS, a MIngling Chained Storage scheme that combines structural and functional advantages from both N-way replication and erasure coding. Qualitatively, MICS provides efficient read/write performance and high reliability at reasonably low space cost. MICS stores each object in two forms: a full copy and certain amount of erasure-coded segments. We establish dedicated read/write protocols for MICS leveraging the unique structural advantages. Moreover, MICS provides high read/write efficiency with Pipeline Random-Access Memory consistency to guarantee reasonable semantics for services users. Evaluation results demonstrate that under same fault tolerance and consistency level, MICS outperforms N-way replication and pure erasure coding in I/O throughput by up to 34.1% and 51.3% respectively. Furthermore, MICS shows superior performance stability over diverse workload conditions, in which case the standard deviation of MICS is 70.1% and 29.3% smaller than those of other two schemes.

Emerging paradigm of cloud computing provides a new service delivery platform. One of the latest challenges is how to work with service computing in a cloud computing environment. Meanwhile, the convergence of service computing and cloud computing is becoming a major driving force for the adoption of both of these technologies. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. Also, formal methods can play a fundamental and important role in service computing and cloud computing. The topic of FM-S&C 2014 is the theory aspect of data intensive services. There is no doubt in the industry and research community that the importance of data intensive computing has been raising and will continue to be the foremost fields of research. As a result, the data intensive services have become the important type of Web service. Also, it has become a hot issue in the academia and industry. Potentially, this could have a significant impact on the on-going researches for services and data intensive computing. The scope of the FM-S&C workshop series is not limited to technological aspects. In fact, there is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. Potentially, this might have a significant and lasting impact on the ongoing standardization efforts in cloud computing technologies. All papers accepted by the workshop are included in the proceedings of the IEEE 10th World Congress on Services (SERVICES 2014) which will be published by IEEE Computer Society. Hard copies may be obtained from IEEE Computer Society according to its ordering reprints policies. The electronic copies can be obtained from IEEE Xplore Digital Library.

The research on the business models is a hot topic in recent years. It is an interesting problem to study the business model of the service. There are three kinds of models related: classical service models, Business process (BP) models and the enterprise business (EB) models in management. However, none of them covers all the properties of the service business model. In this paper, we define the business model of the service as the combination of four kinds of strategies and name it as the service pattern. We also propose a language named Service Pattern Description Language (SPDL) covering all the elements involved in these strategies. We formulate the language syntax and two basic extraction rules assisting economic analysis. Furthermore, we extend Business Process Model Notation (BPMN) to support SPDL, which is named BPMN for Service Pattern (BPMN4SP). The example of Mobile Application Platform is studied in detail for a better understanding of SPDL.

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

Process retrieval is critical for workflow repository management. Structural similarity metric based on graph matching could achieve highest retrieval quality. Nowadays, researchers mainly adopt graph edit distance (GED) as the approach for comparing process models. However, the computation complexity of GED based methods are high and their cost functions depend heavily on the application domain. To overcome these shortcomings, we use the maximal common subgraph (MCS) approach instead and propose a depth-first search (DFS) code based method to implement the MCS. The minimum DFS codes are used to canonically label the process models and their fragments. By comparing the minimum DFS codes of the fragments, the maximal common subgraphs between the search model (i.e., a given process model or fragment) and the processes in the repository could be found. The experimental evaluations show that our method is feasible for real applications.

This paper introduces a service selection model with the service location considered. The location of a service represents its position in the network, which determines the transmission cost of calling this service in the composite service. The more concentrated the invoking services are, the less transmission time the composite service costs. On the other hand, the more and more popular big data processing services, which need to transfer mass data as input, make the effect much more obvious than ever before. Therefore, it is necessary to introduce service location as a basic feature in service selection. The definition and membership functions of service location are presented in this paper. After that, the optimal service selection problem is represented as an optimization problem under some reasonable assumptions. A shortest-path based algorithm is proposed to solve this optimization problem. At last, the case of railway detection is studied for better understanding of our model.

Predicting the Quality of Service (QoS) values is important since they are widely applied to Service-Oriented Computing (SOC) research domain. Previous research works on this problem do not consider the influence of user location information carefully, which we argue would contribute to improving prediction accuracy due to the nature of Web services invocation process. In this paper, we propose a novel collaborative QoS prediction framework with location-based regularization (LBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate geographical information to identify the neighborhood. Different neighborhood situations are considered to systematically design two location-based regularization terms, i.e. LBR1 and LBR2. Finally we combine these regularization terms in classic MF framework to build two unified models. The experimental analysis on a large-scale real-world QoS dataset shows that our methods improve 23.7% in prediction accuracy compared with other state-of-the-art algorithms in general cases.

The environment of enterprise applications is characterized by frequently changing market demands, time-to-market pressure and fierce competition. To seamlessly integrate complex computing activities, Web Service Composition (WSC) has been regarded as an emerging E-Commerce solution to support interoperable machine-to-machine business interactions over network. To guarantee the composite Web service can be successfully produced, testing is a preferred validation technique to efficiently verify the correctness of functional and nonfunctional requirements of WSC behaviors. BPEL4WS is a high level and semi-formal abstract description language for WSC orchestration. Manually generating test cases from BPEL4WS is tedious, time-consuming, and error prone. Thus, the automated test case generation plays a critical role in all the phases of Web service life cycle. Considering the uncertain environment, an extended WSC model, namely probabilistic timed interface automata for Web service (PTIA4WS), is pro-posed to transform and extend BPEL4WS with regard to the stochastic and time-related behaviors. Based on PTIA4WS model, we propose an approach for generating quantitative test cases from counterexamples of violated PTCTL formulae using coverage criterions. After that, timed test case with fastest execution response time and probabilistic test case with maximal execution success rate are discussed. The series of experiments show that our method gains better performance than traditional methods.

This paper proposes a novel trust evaluation method for service choreography. Compared with current work towards this problem, it considers not only the trust for individual partner services and the explicit trust relation among partner services that have logical dependencies for each other, but also the implicit trust relation implied in data-dependencies among services. A serial of experiments, using the simulation tool Net Logo, are carried out to compare the evaluation results between the proposed method and the method without data-dependency consideration. The result shows that taking consideration of the data-dependency trust improves the accuracy of trust evaluation to a great extent.

With the number of available Web services is rapidly increasing, how to compose multiple Web services automatically to fulfill a given request has attracted much attention. This paper proposes a dedicated planner named AWSP (Automatic Web Service Planner) toward this problem. Compared with other AI planners for automatic Web service composition, AWSP is characterized by its two different heuristic functions to reduce the search space greatly. A series of experiments based on test sets generated by WSBen show that 1) AWSP performs well even when the scale of the test set expands significantly. 2) AWSP has a smaller search space and performs better when using the backward search strategy than using the forward search strategy, 3) AWSP with the A* heuristic function can get the solution with the shortest invocation path.

Service-based applications are typically based on dynamic reconfiguration, since it can be regarded as compositions of multiple Web services. Because service-based applications usually run in open, dynamic, ever-changing environments, providing function-consistent application becomes a big challenge. The paper proposes an effective dynamic reconfiguration approach for services based applications. The approach tries to replace each faulty service firstly. If the attempts fail, it will construct regions for each faulty service and tries to replace the region. In order to ensure the correctness of dynamic reconfiguration, we use services-behavioral type to formally describe services and propose subtype rule services-behavioral type to judge the substitutability between services. Also, the case study is given to show that how to verify the correctness of dynamic reconfiguration.

Dynamic reconfiguration can help SOA based applications to update, modify, add and remove their functions, improve their performance, enhance their reliabilities and robustness. However few works focus on the QoS-based dynamic reconfiguration of SOA-based applications. This paper presents an approach for QoS-based dynamic reconfiguration of SOA based applications. The proposed approach can reconfigure a SOA based application to comply with a new QoS constraint by replacing its individual or multiple component services. An important factor named global significance value is introduced to show the significance of each component service. The individual component services are attempted to replace according to the descending order relative to the value. If the attempts fail, multiple component services will be replaced together. In the case study, an example is given to show the approach is feasible to reconfigure a SOA based application to meet a new QoS constraint. The experiment shows the effectiveness and efficiency of our approach.

In this paper, we propose a time-sensitive probability skyline (TPS) approach to recommend services with uncertainty. We project services to n-dimensional data space and recommend services in TPS. Experimental evaluation on real data shows the great performance of TPS in service recommendation by comparing the experiment result with results of other approaches.

SOA based software is typically based on dynamic reconfiguration, since it is the composition of services. But few works focus on the non-functional reconfiguration of the SOA-based software. This paper presents an approach for QoS driven dynamic reconfiguration of the SOA based Software. The approach can reconfigure a SOA based software to comply with a new QoS constrains by replacing its individual or multiple component services. The individual component services are replaced according to the descending order relative to the critical factors. While if the attempts fail, multiple component services will be replaced together. In our case study, an example is given to show the approach is efficient to reconfigure a SOA based software to meet a new QoS constraints.

This paper presents a novel approach for semantic web service composition based on traditional state space search approach. We regard automatic web service composition problem as an AI problem-solving problem and propose an enhanced state space search approach toward web service composition domain. This approach can not only be used for automatic service composition, but also for general problem-solving domain. In addition, in order to validate the feasibility of our approach, a prototype system is implemented.

Composition of Web Services has emerged as a new method to support business-to-business application integration. And the industrial world has already proposed several xml-based business protocol specification languages. In order to address the correct integration of web services, we specify a new method based on the c_net formal model for verifying the composition of web services. This new method can use the semantic information included in the c_net model to verify the flow composition at the semantic level. The mapping between c_net and BPEL4WS is also discussed.

Skyline attracts more and more attention from academic circle and industrial circle because of its application in multi-criterion decision support, preference answering and data analysis. However, it seems unnecessary to recommend all services in skyline while the number of skyline points is large. The number of services in skyline is always large for the reason that comparability decreases with the increase of data dimensionality. Users always want to get only 2 or 3 recommendations instead of all services in skyline. Motivated by this, we propose to compute the representative skyline which contains some points that best describe the contour of the full skyline. In this paper, we propose a new definition which we call “similarity-based representative skyline”. We provide an algorithm SBRSA, which is based on a traversal approach to compute the value of similarity. In particular, we propose an algorithm to maintain the result of SBRSA in dynamic data environment. An extensive performance study using real and synthetic service data is reported to verify its great performance in representation and computing cost.

Interoperability promised by Web service makes it a most promising technology for the development of next generation distributed heterogeneous software systems. Services should be compliant at signature, behavioral and semantic level to make the interoperation successful and correct. Service adaptation provides an effective approach to bridge the incompatibility of services to make them interoperate as well as possible. In this paper, we aim to contribute to the definition of a methodology to develop adaptors that are capable of making two incompatible services interoperate not only successfully but also correctly at semantic level. To achieve this goal, we proposed service specifications for both atomic and composite services with semantic dependency between outputs and inputs specified; then we proposed adaptor specification consisting of three parts, which are message mapping, action mapping and treatment for non-mapping messages. Based on service and adaptor specifications, an incremental derivation approach of a concrete adaptor is given.

Service adaptation is one of the most important issues in SOC (Service Oriented Computing). This paper focuses on the issue of service behavioral adaptation and proposes an adapting method based on dependency graph. It can be divided into three sequential sub-problems: (1) service description-the foundation of service adaptation. We propose a formal approach to describing service behavior protocols; (2) mismatch definition-the identification of service mismatches. We define several kinds of behavior mismatches based on dependency graph; (3) service adaptation-the adaptor construction process. We detect all possible behavior mismatches and then generate different adaptors correspondingly.

Consistency of Web services behavior is the key to ensure correctness and reliability of Web services choreography technology. In the paper, we introduce Martin-Löf type theory (MTT) and extend it to have a strong expressive capacity to describe formally Web services behavior. Based on this idea behind MTT, the paper applies extended-MTT to formally describe Web services behavior. Then, the rules of consistency are proposed based on combination of extended-MTT and type discipline. Next, the procedures of proofs are given that verify the consistency between behavior of vendor and behavior of vendor-s. In one word, our way is a suitable trade-off between expressiveness and amenability to efficiently verify.

Web services behavior is the key aspect to consistency of Web services, which can ensure correctness and reliability in Web services choreography. But few methods can conduct the trade-off between expressiveness and amenability of efficient verification, this paper gives a better answer to solve the problem. It constructs service-behavioral type discipline based on extended the Martin-Löf’s Type Theory (for short, MTT) which supports a type-theoretic formulation of services behavior structured patterns, so that services behavior in a distributed system can be verified by type checking. Then, the type rules for subtype, duality, and consistency of Web services behavior are discussed. The deductions are gave to show that how to verify the consistency between behavior of vendor and behavior of vendor-s.

We bring forward a two-phase semantic service discovery mechanism which supports both the operation matchmaking and operation-composition matchmaking. A serial of experiments on a service management framework show that the mechanism gains better performance on both discovery recall rate and precision than a traditional matchmaker.

Over the last decade, the architectures and technologies used for enterprise application integration have been improved, and many companies have proposed varied solutions for integration. But these traditional approaches have some disadvantages such as high cost, poor flexibility and vendor lock-in. And now, the service oriented architecture, advanced in loose-couple, low cost and high integration ability, is thought to be the most advisable approach for business integration. In this paper, we introduce an SOA based enterprise application integration platform named JTang Synergy. JTang Synergy is composed of a basic integration platform, a supported platform, administration tools and development tools. As the core of JTang Synergy, the basic integration platform is implemented according to Java Business Integration which makes JTang Synergy more flexible and vendor-neutral. And we employed an event-based enterprise service bus to realize integration. The supported framework and the tools ease JTang Synergy to develop and integrate services. And a scenario is introduced to illustrate the integration process using JTang Synergy.

Recently Web service composition is a hot research spot, but there is no any transitional management issues concerned, including the time management. The time management is a crucial issue in web service composition. This paper presents a time management model for the environment. In this model a service should provide necessary time value,. In this way Web service definition language will be expanded, and time management in web service composition can be simplified. On the other hand, it preserves the autonomy of the web services.

The responsibility of resource server is to store and manage the reusable and product-specific assets produced in the process of software development applying software product line principals. Separately designed for each specific resource type, classical resource server could not satisfy the demands for mass storage of various resource types in collaborative development environment. Interception design pattern is introduced as a novel means to implement resource server. Interceptors with specific functionalities are developed and configured for various resource types in domain to provide a uniform framework to deal with distinct types of resource, including customized resource type. The description of the resource handling process using Pi calculus demonstrates the validity of the approach.

Service discovery becomes a key to hastening the evolution of web services as the number of services is expected to increase dramatically. In this paper, we propose to index all the ontology-annotated outputs in registered services. For each ontology-annotated output, there is a service list which records all the services in the registry that deliver the output. Based on the indexing, we propose a composition-oriented service discovery algorithm, which greatly accelerates the filtering of irrelevant atomic services by making use of the inverted indexing, and increases the likelihood of finding a possible candidate by exploring service composition. Experimental results show that the proposed algorithm provides a better performance on response time than the sequential matchmaking, and a better recall rate than the algorithms without the exploration of composition.

BPEL can automate orchestrations for cross-organizational Web services; however, it meets a serious challenge from modeling human-intensive business activities, especially from addressing access control for human coordination considering complex interpersonal relationship in modern business. This paper analyzes the importance of human-intensive processes and introduces several additional types of BPEL constructs, then discusses RBAC Model extracted from BPEL process, finally uses improved FOAF to enhance RBAC Model in BPEL. The goal of our work is to enhance human coordination capability in BPEL-based business processes by using RBAC model and improved FOAF.

A Web service is a basic software component that can be accessed by standard Internet protocols. It provides a new approach to cooperative and federated computing among different organizational units. There are many specifications which can describe the elements of Web services and make the end-users interact with each other. However, they are remaining at the descriptive level, without supporting any kind of mechanisms or tools for the verifying the specified attributes of the Web services. In the paper, we provide a mathematical scheme, type theory, to describe the basic elements of Web services and the specified attributes. We also present the mechanism to deduce the automated programs in other languages (for example ML) from the type theory. Thus we can verify the behavioral properties of a Web service as well as analyzing and verifying Web services composition

With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM) to perform service recognition. During the training process, the feature vectors of training services and their categories are learned by the BPM. The element in the feature vector is the semantic similarity between the feature word in the system dictionary and the occurrence in the feature set for a service. During the recognition process, the characteristics of the test service are analyzed by the BPM and the output shows the category of the test service. Furthermore, the BPM is adapted with correctly recognized test services, which results in better modeling over time. Based on extensive experiments, we show that using the adaptive BPM is a promising way to realize automatic service classification, and the adaptive BPM using semantic similarity as the element in feature vector provides better performance than the general BPM using word frequency

Service discovery becomes a key to accelerating the evolution of Web services as the number of services is expected to increase dramatically. Foregoing work on service discovery is primarily based on the interfaces of services through the use of ontology. Ongoing work targets at service behavior, with not only individual message exchanges being captured, but also constraints between these message exchanges. In this paper, we propose a formal approach to expressing the service and query behavior using pi-calculus for service matchmaking. The resulting pi-calculus expressions of services and queries are precise in defining single operations involving message exchanges as well as execution sequence between operations. Based on the formalizations, service matchmaking between a service query and a service description is reasoned through the capability of pi-calculus. Expressing service behavior using pi-calculus is expected to be a promising way to realize intelligent service discovery

ITSGrid is an undergoing joint engineering project designed and developed by advanced computing and system (CCNT) lab in Zhejiang University and Hangzhou Enjoyor Electronics Co. Ltd (Enjoyor). The new features of ITSGrid are originated from two important research projects - DartGrid and DartFlow, and one key engineering project - JTang application server, in CCNT lab. Its goal is to build an integrated intelligent transportation information and service platform (ITISP), to integrate traffic data resources collected by Enjoyor and cooperate existing ITS subsystems and services deployed by Enjoyor, finally serve for transportation construction in China. During building this project, we utilize systematically the grid technology, the semantic Web technology, the Web service technology, the messaging oriented middleware technology

A programmable parallel digital signal processor (DSP) core for embedded applications is presented which combines the concepts of single instruction stream over multiple data streams (SIMD) and reconfigurable architecture. Equipped with eight SIMD-controlled 16-bit datapaths which can also be reconfigured as two 32-bit datapaths, the DSP core can process both 16-bit and 32-bit data in parallel, showing high performance, especially in the applications preferring parallel data flow computations, such as image processing. The SIMD scheme is extended with the instant-scalability of datapaths (ISSIMD), which offers the DSP a capability of dimension-controllable vector processing, so that to provide flexibility for different embedded applications. A first prototype in 0.18-/spl mu/m CMOS technology has been fabricated, which achieves IGMACS performance at the clock of 125 MHz.