Currently, multimodal sentiment analysis (MSA) for personalized users under uncertain modalities missing has become a new challenging problem. To address this issue, we propose a two-step idea. First, we propose an effective MSA model under uncertain modalities missing and train it with some public datasets, thus to enable the model to possess better preliminary MSA ability. Then, we make the pretrained model to continuously learn user’s personalized characteristics with online learning methods, thereby enable the model grow into a robust model for personalized MSA. Based on this idea, we propose a Personalized MSA model under uncertain modalities missing via Pretraining and Online Learning (termed as PMSAPO). For Personalized MSA under uncertain modalities missing, PMSAPO firstly generates the fused modality and allocate weights for each modality with a Fully Connected Neural Network Evaluation Module. Then, PMSAPO completes the final sentiment classification based on the fusion modality with a Joint feature optimization module. For the pretrained PMSAPO, we make it autonomously learn the personalized users via our proposed online learning techniques, including an online meta-learning method, a learning rate adaptive adjustment strategy, and a dynamic weight assignment strategy for sample data. Finally, based on three public benchmark datasets (IEMOCAP, MELD and CMU-MOSI), we conduct extensive experiments and prove that PMSAPO completely outperforms the Twelve state-of-the-art baseline models. (Code is available at https://github.com/SHX-AI/PMSAPO .)

Abstract Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods.

Group Point-of-Interests (POI) recommendation devotes to find the optimal POIs for groups, which has extracted extensive attention. This work first brings forward a novel POI recommendation model for random groups based on Cooperative Graph Neural Networks (named as CGNN-PRRG). We have done three innovative work. (1) We propose a new fitted presentation learning method for generating the fitted representations of random groups. (2) To conquer the cold start issues in recommending POI for a new random group, we propose to take similar users’ (which have the similar representations with that of the random group) POI interaction data as the learning data. (3) We propose an Edge-learning enhanced Bipartite Graph Neural Network (EBGNN) to learn similar users’ POI comprehensive interaction preferences. Specially, EBGNN can learn the information on the edges of the graph. Meanwhile, we propose to learn similar users’ POI transfer preferences with the Session-based Graph Neural Networks (SRGNN). We verify our proposed model on the three public benchmark datasets (Foursquare, Gowalla and Yelp), which contain 124,933 to 860,888 POI check-in records. The comparison between our proposed model and ten representative baseline models demonstrates the outstanding performance of CGNN-PRRG. In terms of Precision@K and NDCG@K, our model achieves about 24.9% and 62.5% improvement compared with the best baseline models on the three benchmark datasets averagely. Adequate ablation experiments prove the effectiveness of the fitted representation generation method, similar users’ POI comprehensive interaction preferences learning method and the method for overcoming the cold start problem. The source code of the CGNN-PRRG model is available on github 1 .

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method.

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios.

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain.

Deep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent approaches have utilized high-order connectivity, they still limit themselves to simple interactions and ignore the pattern of structural sub-graphs/motifs. In this study, we first explore the commonly used motifs in the Mashup-API interaction bipartite graph and propose a dedicated algorithm to generate the motif adjacency matrix. We then propose a Motif-based Graph Attention Network for service recommendation (MGSR) that utilizes a motif-based attention mechanism to capture the high-order information of various motifs, and a Collaborative Filtering model to generate the recommendation prediction. We have conducted extensive experiments on ProgrammableWeb dataset and our results demonstrate the superior performance of our proposed framework over some state-of-the-art approaches.

Despite the continual increase in the number of Web-APIs available on the internet, it is still challenging for API consumers to discover appropriate Web-APIs that could satisfy requirements. One of the main reasons for this is that Web-APIs registered on online directories such as ProgrammableWeb are in general isolated, as they are registered by diverse providers independently and progressively, ignoring continuous interactions among these APIs, which could enhance their discoverability. In this paper, we propose a method for analyzing the Web service ecosystem, and a complex network-based approach for constructing evolving networks for Web-APIs that are capable of enhancing their discoverability. We first conduct a two-phase analysis: We investigate mashups and Web-APIs interactions in the service ecosystem, and analyze their popularity distributions, and quantitatively measure two key node attachment dimensions within the ecosystem: Preferential Attachment and Similarity. Based on the analysis, we propose two methods for constructing evolving Web-API networks using the theoretical procedures of theBarab asi-Albert and the Popularity-SimilarityOptimizationnetwork models. Finally, we comprehensively evaluate the networks and map their properties with service discoverability using the ProgrammableWeb datasets. The results presented in this work will serve as a practical guide for designing a complex network-based solution for Web-API discovery.

Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users’ Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users’ service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

By leveraging IoT Big Data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach.

Many real-world networks including the World Wide Web and the Internet of Things are graphs in their abstract forms. Graph neural networks (GNNs) have emerged as the main solution for deep learning on graphs. Recently, tremendous effort has been made to enhance the performance and expressivity of GNNs. In this paper, we review the state-of-the-art graph neural network models and frameworks with a focus on the latest developments in graph representation learning. We propose a new taxonomy which divides general GNNs into recurrent GNNs, spectral GNNs, spatial GNNs and topology-aware GNNs. We will also discuss the inductive biases behind different categories of GNNs.

Several techniques for human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid advancement of sensor technologies. However, recognizing multiple people’s activities is still challenging due to the complexity of their activities, such as parallel and collaborative activities. To address these challenges, we propose a transformer with a bidirectional gated recurrent unit (GRU) deep learning (DL) method, called TRANS-BiGRU, to efficiently learn and recognize different types of activities performed by multiple residents. We compare the proposed model with the state-of-the-art models and various DL models, such as Ensemble2LSTM (Ens2-LSTM), bidirectional GRUs (Bi-GRU), and traditional machine learning (ML) models, such as support vector machine (SVM). Our experimental results based on the center for advanced studies in adaptive system and ARAS public data sets show that our model significantly outperforms the existing models for complex activity recognition of multiple residents.

Indoor localization is a crucial component of IoT applications in many areas, such as healthcare, energy management, and security control. Passive infrared (PIR) sensor has been employed for a location estimation due to its cost effectiveness, low power consumption, and low electromagnetic interference. Compared with its binary output, PIR analog output which is an output voltage generated by a PIR sensor when its sensing elements detect changes in temperature in an environment can provide more information regarding a person’s location. However, only a few works focus on using analog signals for location estimation. During the past several years, deep learning approaches have emerged and achieved outstanding results in many applications. In this article, we harness the power of deep learning and propose a deep CNN-LSTM architecture for PIR-based indoor location estimation. In our architecture, an upper CNN network can extract features from PIR analog output automatically while a lower LSTM network can learn temporal dependencies between the extracted features. To evaluate the feasibility and performance of our proposed method, we conduct four different sets of experiments. Our results show that the proposed method can efficiently handle complex cases and can achieve the mean distance error of 0.23 m, and 80% of distance errors are within 0.4 m.

Abstract Modern software systems are continuously seeking for adaptability realizations, to generate better fit behaviours in response to domain changes. Requirements variability motivates adaptability; hence, understanding the influence of the domain changes, i.e. context variability, to requirements variability is necessary. In this paper, we propose an approach for context-based requirements variability analysis in the goal-oriented requirements modelling. We define contextual goals and contextual preferences to specify the relationships of contexts with requirements and preferences, respectively. Given a requirements problem represented through a goal model, we use the contextual goals to derive applicable solutions at a given situation. Then, from those applicable solutions, we use the contextual preferences as criteria for evaluating and selecting the ones that would best satisfy stakeholder priorities. To support our variability analysis, we develop a tool to automate the derivation and evaluation of the solutions. We further demonstrate the use of our approach in detecting modelling errors and validating the impact of prioritizations, leading to improvements in the requirements specifications. Our approach broadens the scope of requirements variability by weaving context variability with both stakeholder goals and preferences, in order to sufficiently represent the adaptability needs of software systems where contextual changes are commonplace.

Multiresident activity recognition (AR), which has become a popular research field in smart environments, aims to recognize the activities of multiple residents based on data collected from various types of sensors, and sensor events segmentation is an important technique for enhancing the performance of AR. While quite some segmentation methods have been proposed for the single person setting, few studies have been done for the multiresident setting. In this article, we first evaluate the baseline and the state-of-the-art segmentation methods using the popular multiresident data set CASAS, to confirm that the performance of multiresident AR can be improved by applying segmentation techniques; we then propose a novel Hybrid fuzzy c-means (FCM) change point detection (CPD)-based segmentation method that can further enhance the performance of multiresident AR. We combine a FCM method with a CPD-based method for sensor event segmentation. The FCM method is used to classify the sensor events in terms of sensor locations, and then the CPD technique is used to probe the transition actions to determine the segmentation sequence. Our experimental results show that the proposed method significantly improves the performance of multiresident AR in comparison with the baseline and state-of-the-art classification methods.

The complex interventions among sensor streams bring new challenges for IoT applications to derive meaningful information from large amounts of sensor streams. This paper aims to provide a data-driven service creation method for effectively capturing events based on our previous service abstraction – proactive data service. For improving the effectiveness of proactive data service, we consider the potential correlations among sensor streams besides user's pre-definitions when creating service. Based on the assumption that events frequently co-occurred in history have high probability to co-occur again, we regard frequent event sets as one kind of correlations among sensor streams, and propose an algorithm called FP-MFIM to efficiently find the maximum frequent event sets co-occurred in multiple sensor streams. For providing more effective information, we create PD-services with frequent co-occurred event types besides user-defined event types. This paper reports the tryout use of the method in China power grid for power quality event detection and location. Through a series of experiments based on real sensor data from power grid, we verified the efficiency of FP-MFIM algorithm and the effectiveness of our PD-services in real-world scenario.

Mobile devices have become major sources of context-aware data due to their ubiquity and sensing capabilities. However, deploying mobile devices as dynamic, unabridged context data provider either locally or remotely is still challenging due to their limited computing capability. Furthermore, integrating physical sensor data with social context data from online social networks is necessary for rich context data provisioning. In this paper, we present AndroCon, an Android-based context-aware middleware framework that enables mobile devices to acquire, integrate, manage, and provision context data. We have applied AndroCon to manage social and physical context data from various sources and have evaluated its performance in terms of power consumption and CPU utilization.

The landscape of modern enterprise IT environments is that a large number of distributed software systems interact and cooperate with each other to support daily business operations. With the prevalence of cloud computing, the level of system connectivity increases further. The large scale of such an environment makes it difficult to test a system's quality attributes such as performance and scalability before it is actually deployed in the production environment. Under the currently dominant iterative and incremental software development paradigm, this difficulty is even more pronounced when the quality attributes of an enterprise system need to be examined and evaluated at early stages but a large part of its operating environment is not available or accessible. In this paper, we present a Coloured Petri nets (CPN) based system behaviour emulation approach and a lightweight emulated testing framework for provisioning a virtual deployment testing environment for an enterprise software system, so that its quality attributes, especially scalability, can be evaluated without physically connecting to the real production environment. It is worth noting that the focus of this work is on the testing environment which enables virtual system deployment and testing, instead of being on the research topic of deployment test. CPN and its associated design and simulation tools have been used and integrated to model and execute the behaviour of those cooperating endpoint systems that an enterprise software system interacts with. We have successfully implemented an industry standard protocol Lightweight Directory Access Protocol in our approach and applied it in testing the scalability of a real-world enterprise application, CA Technologies’ IdentityManager. A thorough in-lab performance study has also been conducted to examine the capacity and scalability of this approach.

With the increasing abundance of traffic data from sensors and devices, the integration and analysis of such streaming data are gaining importance in many application scenarios. This paper proposes a service-based approach for integrating and analyzing the traffic sensor data to support green commute in China by automatically discovering carpooling companions within a community. Our focuses are on modeling and design of the carpooling discovery services, algorithms for implementing the services, and performance enhancement when the data volume scales up. The proposed approach is verified with experiments using real-world data.

In recent years, artifact-centric business process modeling is gaining momentum with its improved flexibility and extensibility. In order to support the rapid translation of the traditional activity-centric processes into this new type of processes, this paper proposes a novel approach to automatically transforming an activity-centric process model into a group of lifecycles of artifacts and their interactions, which represent the behavior of its corresponding artifact-centric process model. Algorithms on translating an activity-centric process model into a tree model, finding the dependencies between two object/artifact states based on the tree model, and synthesizing the lifecycles of artifacts have been proposed. Throughout the paper, we illustrate our approach with an order processing running scenario.

With the advances of Internet technologies and an explosive growth in the popularity of social media, an increasingly large part of human life is getting digitized and becoming available on the web. This phenomenon brings opportunities and motivates us to infer users’ situations by exploiting their interaction events in various social media such as online social networks, blogs and email. One of the key requirements of inferring situations from interaction events is to consider both the semantic and temporal aspects of events in the situation inference process. In this paper, we address this issue and propose a novel approach to exploiting users’ interaction events in social media to infer their situations. We present an ontology-based interaction event model that captures the properties of users’ interaction activities in social media. We further provide a rule-based situation specification technique that integrates the interaction event ontology (for semantically matching interaction events) with temporal event relationships (for correlating historical interaction events). We also provide a platform to realize the situation reasoning/inference process, which combines semantic matching and complex event processing. We conduct a performance evaluation of the platform to quantify its efficacy. The feasibility and applicability of our approach is demonstrated by developing a socially aware phone call application as a case study.

Context awareness refers to the system capability of both sensing and reacting to situational changes. It is one of the most exciting trends in computing today that holds the potential to make our daily life more productive, convenient and enjoyable. Over the years, advances in Web and mobile technologies are gradually bringing the full potential of desktop computers to potable mobile devices. As a matter of the fact that the number of mobile phone users is already much higher than desktop users, most Internet and Web-based services such as search engine querying, news reading, multimedia downloading, instant messaging, online shopping and also social networking can be accessed mainly through a large variety of mobile devices instead of desktop computers. Indeed, context-aware mobile services are emerging as an important technology to underpin the new breed of user-centric smart applications on the future ubiquitous Web. Although mobile devices naturally have the capability to capture both the physical context, such as location, and the social context, such as presence and relationships, of users, there are many hurdles to cross in order to realize the full potential of context-aware ambient services. This theme issue looks at the new development in ambient and context-aware Web information systems such as location-based applications, mobile payment systems, mobile context-aware applications and mobile Web data search engines.

We present a social context as a service (SCaaS) platform for managing adaptations in collaborative pervasive applications that support interactions among a dynamic group of actors such as users, stakeholders, infrastructure services, businesses and so on. Such interactions are based on predefined agreements and constraints that characterize the relationships between the actors and are modeled with the notion of social context. In complex and changing environments, such interaction relationships, and thus social contexts, are also subject to change. In existing approaches, the relationships among actors are not modeled explicitly, and instead are often hard-coded into the application. Furthermore, these approaches do not provide adequate adaptation support for such relationships as the changes occur in user requirements and environments. In our approach, inter-actor relationships in an application are modeled explicitly using social contexts, and their execution environment is generated and adaptations are managed by the SCaaS platform. The key features of our approach include externalization of the interaction relationships from the applications, representation and modeling of such relationships from the domain and actor perspectives, their implementation using a service oriented paradigm, and support for their runtime adaptation. We quantify the platform's adaptation overhead and demonstrate its feasibility and applicability by developing a telematics application that supports cooperative convoy.

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches:collaborative filteringandcontent-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

Context-awareness and adaptability are highly desirable features for services that are operating in dynamic environments. Recently, a number of approaches have been introduced to support the development of such services. But, validating the varying requirements of these services is still a major challenge. In this paper, we introduce a novel scenario-based approach to address this challenge. First, our approach captures a service's requirements as two sets of scenarios: functional and adaptation. The functional scenarios represent the service's core functionality, while the adaptation scenarios capture the service's runtime adaptation in response to context changes. The service properties that need to hold at runtime are also represented graphically in a form similar to the scenarios. Second, a technique is introduced to enumerate and generate the specifications of a service's variants from its scenarios. The generated variants are then validated against the service properties to ensure their validity. This technique also checks the consistency of the service's adaptation requirements (scenarios). Case studies have shown that with our approach, a small number of service scenarios specified by the software engineer is able to cover a large number of service variants, which are generated and validated automatically.

With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which possess many limitations such as insufficient recommendation performance and heavy dependence on the input from users such as preparing complicated queries. In this paper, we propose a novel approach that dynamically recommends Web services that fit users' interests. Our approach is a hybrid one in the sense that it combines collaborative filtering and content-based recommendation. In particular, our approach considers simultaneously both rating data and content data of Web services using a three-way aspect model. Unobservable user preferences are represented by introducing a set of latent variables, which is statistically estimated. To verify the proposed approach, we conduct experiments using 3, 693 real-world Web services. The experimental results show that our approach outperforms the two conventional methods on recommendation performance.

SUMMARY Composite service selection is one of the core research issues in Web service composition. Because of the complex service correlation context, candidate services may perform differently when being used with other services. Presently, most service selection approaches ignore this issue, which makes the selected composite services less efficient than expected. To solve this problem, a service correlation context‐aware composite service selection approach is proposed on the basis of the concept of single‐entry single‐exit (SESE) region. The general process of our approach is as follows: (1) mining the SESE patterns that are frequently used together in the set of efficiently executed instances of a composite service; (2) dividing the process model of the composite service into SESE regions and generating the candidate SESE pattern set of each region, using the discovered SESE pattern set; and (3) optimizing composite service selection globally on the basis of QoS using divided regions as selection units and their candidate pattern sets as candidate service sets. Because SESE patterns are testified by large amount of efficiently executed instances, they have higher quality than the results of independent selection of services in an SESE region. Experimental results demonstrated that our approach can improve the quality of selected composite services effectively in the correlation context. Concurrency and Computation: Practice and Experience, 2012.© 2013 Wiley Periodicals, Inc.

Follow the promising Web 2.0 paradigm, the telecommunications world also wants to implement the Telco 2.0 vision by inviting its users to actively participate in the creating and sharing of services accessible using handheld devices. The EU-IST research project OPUCE (Open Platform for User-Centric Service Creation and Execution) aims at providing end users with an innovative platform which allows an easy creation and delivery of personalized communication and information services. This paper introduces a novel visual semantic service browser built on top of the OPUCE service repository which enables intuitive visualized service exploring and discovery while requires no technical semantic Web knowledge from the user.

Context-aware Web services are currently emerging as an important technology for building innovative context-aware applications. Unfortunately, context-aware Web services are still difficult to build. This paper describes ContextServ, a platform for rapid development of context-aware Web services. ContextServ adopts model-driven development where context-aware Web services are specified using ContextUML, a UML based modeling language. The platform also offers a set of automated tools for generating and deploying executable implementations of context-aware Web services. This paper presents the motivation, system design, implementation, and usage of ContextServ.

The key objective of OPUCE system is to enable the participation of end-users in the management of their own services, by providing them with innovative tools which allow an easy creation and delivery of personalized communication and information services. This paper describes the OPUCE service and component repository, which extends the OMA OSPE service model storage approach XDM. By integrating an ebXML registry using the native notification mechanisms of XDM, the search capability of the repository is dramatically improved. Moreover, this repository also exploits semantic Web technology to provide an intuitive visualized browser for convenient service exploring.

Context-aware Web services are currently emerging as an important technology for building innovative context-aware Web applications. Unfortunately, context-aware Web services are still difficult to build. This paper describes Smart Adelaide Guide, a context-aware Web application developed by ContextServ platform, a research project sponsored by Australian Research Council. ContextServ adopts model-driven development where a UML based modeling language---ContextUML---is used to model Web services and its context-awareness features. The platform offers a set of visual editing and automation tools for rapid generating and deploying context-aware Web services.

Service composition has become an important paradigm for building distributed applications and e-business processes. While effort has been reported to verify a posteriori whether a given composition such as a BPEL schema satisfies the predefined behavioural properties, little effort has been made to utilise the properties to assist the designer in developing a correct service composition in the first place. This paper reports our first attempt towards this goal by presenting a framework and associated techniques to provide automated guidance to the designer during the composition design process. The guidance can be suggestions on the next valid steps in the business process, identifications of missing/misplaced steps, and/or propositions for inserting, deleting or reordering activities. The guidance is provided based on the temporal business rules which state the temporal/sequential relationships between business activities.