Ensemble Temporal Prediction Model-as-a-Service (ETP-MaaS) has become crucial in fields like financial modeling and cloud monitoring. Existing solutions fail to co-optimally address a two-fold challenge of dynamic collaboration and heterogeneity, treating models as independent entities and employing simplistic worker allocation rules. However, at the model level, data volatility means that optimal performance requires identifying and weighting constantly shifting subgroups of base models, not just individual ones; at the system level, these model groups must be efficiently mapped to a pool of heterogeneous and dynamically available workers. To this end, we introduce WIET, an efficient ETP-MaaS system that co-optimizes model weighting and worker allocation. For adaptive weighting, WIET identifies evolving group behaviors among base models and propose a novel group temporal locality-enhanced weighting method. Additionally, WIET develops an efficient, multi-dimensional worker allocation method powered by hybrid heuristic optimization, effectively reducing bottlenecks and resource waste. Experiments show WIET consistently outperforms state-of-the-art methods in terms of accuracy, latency, and resource usage across various workloads and tasks.

The combination of edge and cloud brings new opportunities and challenges to the data placement problem. Expanding from cloud to edge allows data to be placed closer to user, which relieves bandwidth pressure on cloud and reduces latency. Factors such as varying request preferences and regional correlations are becoming increasingly evident. However, they are largely ignored by existing methods. To this end, we combine the distribution of user requests and server location to construct a spatial distribution vector of the request and propose a data placement strategy based on it. Within edge-cloud environment, the proposed strategy can adapt to different request distributions and place data in a targeted manner. Considering the impact of data volume, we assign different weights for placing data on different servers and update them dynamically in response to request fulfillment. To address the imbalance in requests and data volume in network traffic composition, we analyze the similarity of some data request distribution through the vectors, and co-place the data with high similarity to satisfy user demands. Through experiments on real base station distributions and Foursquare dataset, our proposed method reduces the latency by 17.98% to 38.72% and the log ratio of total cost ranges from 0.13 to 0.30 compared to SOTA algorithms.

The scaler and scheduler of serverless system are the two cornerstones that ensure service quality and efficiency. However, existing scalers and schedulers are constrained by static thresholds, scaling latency, and single-dimensional optimization, making them difficult to agilely respond to dynamic workloads of functions with different characteristics. This paper proposes a game theory-based scaler and a dual-layer optimization scheduler to enhance the resource management and task allocation capabilities of serverless systems. In the scaler, we introduce the Hawkes process to quantify the “temperature” of function as an indicator of their instantaneous invocation rate. By combining dynamic thresholds and continuous monitoring, this scaler enables that scaling operations no longer lag behind changes of function instances and can even warm up beforehand. For scheduler, we refer to bin-packing strategies to optimize the distribution of containers and reduce resource fragmentation. A new concept of “CPU starvation degree” is introduced to denote the degree of CPU contention during function execution, ensuring that function requests are efficiently scheduled. Experimental analysis on ServerlessBench and Alibaba clusterdata indicates that compared to classical and state-of-the-art scalers and schedulers, the proposed scaler and scheduler achieve at least a 149% improvement in the Quality-Price Ratio, which represents the trade-off between performance and cost.

Owing to remarkable advances in 5G and IoT, Mobile Edge Computing (MEC) has been extensively applied to meet the high latency requirements of computation-intensive applications. This paper explores the optimal resource management in MEC involving multiple competitive edge computing servers (ECSs), and presents a Competition-aware differential Pricing and resource Allocation method (ComPA). First, we propose a differential pricing mechanism that jointly analyzes the computing capability and resource usage rate to comprehensively quantify each MU’s use of ECS resources and give a differential per-second price, alleviating the resource underutilization in traditional schemes. Then, to co-optimize ECSs and MUs and ensure all ECSs have a fair chance to increase revenue, a differential pricing-based hierarchy game is developed. Specifically, ECSs and MUs play a Stackelberg game where ECSs price for higher profits and MUs subsequently offload at least-cost. Meanwhile, a non-cooperative game among competing ECSs is formulated. We design a price renewal algorithm that incorporates choice probabilities to find a suboptimal solution iteratively, offering ECSs the most competitive final pricing. MU’s optimal offloading decision is finally derived through convex optimization. Extensive experiments validate the notable superiority of ComPA over other advanced solutions in boosting ECS revenue and MU experience.

Edge storage offers low-latency services to users. However, due to strained edge resources and high costs, enterprises must choose the data that most warrant placement at the edge and place it in the right location. In practice, data exhibit temporal and spatial properties, and variability, which have a significant impact on their placement, but have been largely ignored in research. To address this, we introduce the concept of data temperature, which considers data characteristics over time and space. To consider the influence of spatial relevance among different regions for placing data, inspired by PageRank, we present a model using data temperature to assess the regional value of data, which effectively leverages collaboration within the edge storage system. We also propose a regional value-based algorithm (RVA) that minimizes cost while meeting user response time requirements. By taking into account the correlation between regions, the RVA can achieve lower latency than current methods when creating an equal or even smaller number of replicas. Experimental results validate the efficacy of the proposed method in terms of latency, success rate, and cost efficiency.

Obtaining high-quality service function vectors and aggregating neighborhood features in service association graph are prevalent methods for Mashup service clustering. However, existing methods often focus on enhancing the service functional feature extraction while overlooking distinctions among different services when creating service function vectors. Additionally, neighborhood feature aggregation is typically considered within a single association graph, lacking contrast optimization of different association features. To address these challenges, we propose a novel framework, MSCCL (Mashup Service Clustering with Contrastive Learning). MSCCL consists of two core components: a service function vector generation module and a neighborhood feature aggregation module. Contrastive learning is employed to enhance vector quality and optimize feature aggregation in both modules. We present a service clustering method within MSSCL that combines techniques from BERT (Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks). Compared to state-of-the-art methods, this approach reduces DBI by 2.03% to 12.58%, while enhancing SC, NMI, and Purity by 2.24% to 15.47%, 3.34% to 11.39%, and 2.58% to 13.65%, respectively. Furthermore, the experiments demonstrate that the popular models for service function vector generation and neighborhood feature aggregation can all be integrated into MSSCL. After being integrated into MSSCL, the clustering performance of these models was significantly improved, highlighting the effectiveness and generalizability of MSSCL.

How to efficiently deploying the service components of a data-intensive application on cloud and edge servers to minimize its latency is one of the main challenges for service providers. Most existing studies consider either service deployment or data placement, rather than their joint optimization. This work considers the driving relationship between data and services in a heterogeneous environment including remote cloud and nearby edge servers, and aims to obtain a desired data placement and service deployment scheme while meeting user requirements for service quality. Firstly, we formulate the problem and decouple data placement from service deployment by polynomial reduction. Then, a priority-based data placement strategy is proposed, which can generate a data placement scheme. After that, the original problem is transformed into a classical assignment problem, and a service deployment strategy based on an improved Hungarian algorithm is proposed to obtain a service deployment scheme. Then, a dynamic adjustment strategy based on response weight is proposed to dynamically adjust the data placement and service deployment scheme in order to reduce response latency, and obtain the final scheme. Finally, a series of comparative experiments were conducted, pitting our algorithms against several baseline and SOTA algorithms. The results show that the proposed algorithms, in comparison to other algorithms, is capable of generating superior data placement and service deployment schemes to significantly reduce response latency.

Serverless computing is a transformative technology that simplifies the management of underlying infrastructure. Deploying serverless computing in edge environment enhances application execution efficiency compared to centralized cloud. In serverless edge computing, efficient scheduling is critical for optimizing the performance of application execution. Applications are typically modeled as Directed Acyclic Graphs (DAGs), where the complex dependencies between functions and the distinct characteristics of each function present challenges to scheduling. Moreover, the constraints of edge resources further complicate scheduling, impeding the enhancement of Quality of Service (QoS) for users. In this paper, we propose an effective function scheduling strategy aimed at improving the performance of applications execution. Specifically, we achieve this by considering the topological structure of the DAG, the dependencies between functions, and the resource requirements. Based on these insights, we develop a new method for function prioritization and propose a corresponding function deployment strategy. Furthermore, we design a data transmission method to reduce latency during data transmission between functions with dependencies. Experimental evaluation using Alibaba Cluster Trace Data shows that the proposed method achieves superior performance in terms of cost-efficiency compared to existing algorithms.

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

Cloud-edge collaboration is an emerging approach that combines cloud computing and edge computing. This combination holds great potential for enhancing system efficiency and reliability, as well as mitigating processing costs and latency. Existing research focuses on task scheduling and service deployment in cloud or edge environments, aiming to achieve different optimization goals. However, traditional deployment methods are no longer suitable for the current used cloud-edge collaboration model, which does not consider the heterogeneity of the cloud-edge, the constrained relationship between cost and latency, and the distribution of user access. To this end, this paper proposes a service deployment scheme to minimize latency in a heterogeneous cloud-edge environment. The scheme first introduces a cloud-edge pre-distribution mechanism (ACEP) to reasonably divide the services among the cloud or edge servers, and then employs a global heuristic latency optimization algorithm (GLOA) and a cost allocation strategy (CACS) to find the best deployment strategy. The effectiveness of our proposed method is evaluated by comparing it with existing algorithms, which shows its superiority in terms of latency.

The sampling method for real-time and high-speed changing streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate streaming data valuation.The SDSLA (Streaming Data Drilling Sampling Method Under Limited Access) sampling method based on mineral drilling exploration can streaming data valuation containing many discrete data in real-time, but when the range of discrete data in streaming data is irregular, it has low sampling accuracy for discrete data.Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS (Streaming Data Dynamic Drilling Sampling).This method takes well as the analysis unit dynamically changes the size and position of the well, and accurately predicts the position and range of discrete data.A new model SDVEM (Streaming Data Value Evaluation Model), is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions.Experiments show that the method proposed in the paper uses neural network training and testing with a small sampling rate to obtain accuracy, recall, and F1 scores above 90%, which is higher than that of the SDSLA algorithm.In summary, the SDDS sampling method is beneficial to the training neural network models and evaluating the value characteristics of streaming data, which has essential research significance in big data valuation.

The big data sampling method for real-time and high-speed streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate evaluation of the value characteristics of streaming data. The SDSLA sampling method based on mineral drilling exploration can evaluate the valuable information of streaming data containing many discrete data in real-time, but when the range of discrete data is irregular, it has low sampling accuracy for discrete data. Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS, which takes well as the analysis unit, dynamically changes the size and position of the well, and accurately locates the position and range of discrete data. A new model SDVEM is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions. Experiments show that compared with the SDSLA algorithm, the sample sampled by the SDDS algorithm has higher evaluation accuracy, and the probability distribution of the sample is closer to the original streaming data, with the AOCV indicator being nearly 10% higher. In addition, the SDDS algorithm can achieve over 90% accuracy, recall, and F1 score for training and testing neural networks with small sampling rates, all of which are higher than the SDSLA algorithm. In summary, the SDDS algorithm not only accurately evaluates the value characteristics of streaming data but also facilitates the training of neural network models, which has important research significance in big data estimation.

With the development of multiaccess edge computing (also called mobile-edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired Quality of Service (QoS). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to limited resources, heterogeneous servers, and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies of the deployment problem in edge environment consider such property. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work first builds a redundant service deployment model under a heterogeneous edge environment and defines it as a multiobjective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and the K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.

As an increasingly mature business model, crowdsourcing, especially spatial crowdsourcing, has played an important role in data collection, disaster response, urban planning and other fields. However, the rapid growth of user scale and massive data collected inevitably brings serious challenges to computing and storage resources. The emergence of cloud computing provides an opportunity to handle such challenges. Its nearly unlimited resource provision capability can provide reliable services for different crowdsourcing applications. Nevertheless, considering the risks of privacy leakage and vendor lock-in using only a single cloud, as well as the additional restrictions caused by the wide geographical distribution of data and associations among workers, the use of multi-cloud seems to be a better choice. In this article, we define a problem to find an effective data placement scheme for spatial crowdsourcing in multi-cloud environment to achieve the cost-effectiveness and minimal latency. We take full account of the interval pricing strategy. Then we analyze the geographical distribution characteristics of data centers through a clustering algorithm, and propose an effective data initialization strategy. Finally, we use a genetic algorithm to further optimize the results. Through experiments on real-world data from cloud providers, the efficiency and effectiveness of our proposed method is verified. Compared with some existing algorithms, the proposed method can significantly reduce the system cost and latency, among which the cost reduction is up to 150 times and the latency reduction is up to twice.

Efficiently deploying the service components of data-intensive applications on edge or cloud servers to minimize latency is one of the main challenges faced by the cloudedge environment. Most existing studies consider either service deployment or data placement, rather than the joint optimization of them. To this end, this work considers the driving relationship between data and services in a heterogeneous environment including remote cloud and nearby edge servers, and aims to obtain a satisfactory data placement and service deployment scheme while ensuring the QoS for users. Firstly, we formulate the desired problem and decouple data placement from service deployment by polynomial reduction. Then, a priority-based data placement strategy (PDPS) is proposed, which can generate a data placement scheme. After that, the original problem is reduced to a classical assignment problem, and a service deployment strategy based on an improved Hungarian algorithm (HA-SDS) is proposed to obtain a service deployment scheme. The effectiveness of our proposed method are evaluated by ablation and comparative experiments, which performs better than other existing algorithms.

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

Sparsity and cold start problem are two classic problems of collaborative filtering. To alleviate these issues, researchers usually add side information to the recommendation models to boost the performance. In this paper, we propose a multi-task learning approach for recommendation based on knowledge graph (KGeRec), which takes recommendation as the main task and the knowledge graph as an auxiliary task to provide side information for recommendation. To fully capture the correlation information between these two tasks, a feature interaction layer (FlU) based on cross networks is designed to share features between them. Besides, a side information embedding layer (SIE) is also designed in the recommendation task to exploit more feature information. We apply KGeRec to three public datasets about movie, book, and music. Experimental results show that the proposed KGeRec outperforms the state-of-the-art approaches (+2.2% in AUC, +2.6% in Accuracy, +2.5% and in F1-score, compared to the maximum value in Type I models; +1.3% in AUC, +0.8% in Accuracy, and +2% in F1-score, compared to the maximum value in Type II models) and it performs well in sparse datasets. We also validate the effectiveness of knowledge graphs in improving recommendation performance.

In e-commerce platforms, a user's next behavior will be affected by his long-term constant interests and short-term temporal needs. Such information is usually hidden in the users' historical online behavior data, so how to capture long-term and short-term patterns becomes the key to design better recommendation models or algorithms. Current mainstream methods such as Markov chain, convolutional neural network, and recurrent neural network cannot well express the mixed dynamic characteristics. In this paper, we propose an attention-based deep neural network (ADNNet) to solve the problem. In ADNNet, a convolutional neural network is used to extract the short-term patterns in the behavior sequences, and a gated recurrent unit is used to mine the long-term patterns in the behavior sequences. The attention mechanism is adopted to help the network automatically learn the best fusion coefficient of these two patterns. Our experimental result on four real public datasets (+0.69% in Hit Ratio and +3.49% in MRR) shows the superiority of our proposed ADNNet compared with other state-of-the-art methods.

With the development of cloud computing, the cloud market is becoming more and more complicated. There are many cloud providers and different cloud instance types, which brings users some confusion when they select cloud instance types. In order to solve the cloud instance type selection problem in multi-cloud environment, a Cloud Instance Type Selection Algorithm based on Genetic Algorithm (CITSA-GA) is proposed. CITSA-GA mainly includes two-dimensional encoding with the constraint between adjacent genes, selection operation adopting the elite retention strategy and the roulette strategy, crossover operation using the first fit strategy, and mutation operation with mutation bounds. We perform some experiments to prove the effectiveness of the proposed CITSA-GA.

Correlative approaches have attempted to cluster web services based on either the explicit information contained in service descriptions or functionality semantic features extracted by probabilistic topic models. However, the implicit contextual information of service descriptions is ignored and has yet to be properly explored and leveraged. To this end, we propose a novel framework with deep neural network, called DeepWSC, which combines the advantages of recurrent neural network and convolutional neural network to cluster web services through automatic feature extraction. The experimental results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

With the rapid development of Internet finance, the volume of online transactions increases gradually, but the risk of exposure is increasing, and fraud is emerging. Because of the characteristics of online transaction, such as large volume, high frequency and fast update speed. In addition, online transaction data has the problems of unbalanced positive and negative sample and sparse timing of transaction data. Most of the existing methods to solve data imbalance are sampled, but this method will change the dataset’s distribution, which is not conducive to improving the generalization ability of the model. There are some timing characteristics of online transaction data, and the common fraud detection model does not take the problem into account in the design of the model. Based on the problems, this paper puts forward the siamese neural network structure based on CNN and LSTM, uses the siamese neural network structure to solve the problem of sample imbalance in online transaction and uses the LSTM structure to make model memory user's transaction information, in order to better detect the fraudulent transaction. The model presented in this paper is verified in real B2C transaction data, and its precision and recall reach about 95% and 96%, respectively.

In large-scale network service systems, the phenomenon of instantaneous gathering of a large number of users can cause system abnormality, whenever the load imposed by the user behaviors does not match the system load. This paper proposes a behavior reconstruction model for large-scale network service systems integrated with Petri net reconstruction methodology, for the purpose of achieving load balancing in the system under increasing number of users. Based on the features of the user interaction behavior sequence, the behavioral load balancing model defines a user behavior membership function. Then, a random fuzzy Petri net with delay is presented to control the user behavior reconstruction. Experiments conducted by considering various changes in the number of user behaviors and their distribution in unit time demonstrate that the proposed methodology can effectively trigger the reconstructed model to balance the system load when the system load exceeds the defined warning point.

Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements the reorganization of raw transaction features to form different convolutional patterns. Its significance is that different feature combinations entering the convolution kernel will produce different derivative features. The advantage of this model lies in taking low dimensional and nonderivative online transaction data as the input. The whole network consists of a feature sequencing layer, four convolutional layers and pooling layers, and a fully connected layer. Verifying with online transaction data from a commercial bank, the experimental results show that the model achieves excellent fraud detection performance without derivative features. And its precision can be stabilized at around 91% and recall can be stabilized at around 94%, which increased by 26% and 2%, respectively, comparing with the existing CNN for fraud detection.

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate.

Summary In the mobile environment, users will use mobile devices to enjoy different services, such as surfing the Internet, shopping online, or paying bills. And the network latency will greatly affect the performance of services, which influences the users using experience at the same time. Many service selection methods have treated network latency as QoS, but they do not consider the issue of user's mobility that resulting inconstantly changing of latency when existing information transmission between the user and services in mobile environment. So it is meaningful to take it into consideration to select globally optimal service to solve the practical problem. In this paper, we consider the user's movement, estimate the network latency by Euclidean distance, and propose a service composition algorithm in mobile environment to provide the best service scheme with the basic idea of dynamic programming. Finally, we make experiments by changing different factors and obtain lower latency compared with the traditional method; reflecting service composition algorithm in mobile environment can better adapt to the user's mobility and uncertain times of interaction between the user and services. Copyright © 2016 John Wiley & Sons, Ltd.

By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.

The number of web services has grown drastically. Then how to manage them efficiently in a service repository is an important issue to address. Given a special field, there often exists an efficient data structure for a class of objects, e.g., the Google' Bigtable is very suitable for webpages' storage and management. Based on the theory of the equivalence relations and quotient sets, this work proposes a multilevel index model for large-scale service repositories, which can be used to reduce the execution time of service discovery and composition. Its novel use of keys as inspired by the key in relational database can effectively remove the redundancy of the commonly-used inverted index. Its four function-based operations are for the first time proposed to manage and maintain services in a repository. The experiments validate that the proposed model is more efficient than the existing structures, i.e., sequential and inverted index ones.

An exploratory search may be driven by a user's curiosity or desire for specific information. When users investigate unfamiliar fields, they may want to learn more about a particular subject area to increase their knowledge rather than solve a specific problem. This work proposes a topic-oriented exploratory search method that provides browse guidance to users. It allows them to discover new associations and knowledge, and helps them find their interested information and knowledge. Since an exploratory search needs to judge the ability to discover new knowledge, the existing commonly used metrics fail to capture it. This paper thus defines a new set of criteria containing clarity, relevance, novelty, and diversity to analyze the effectiveness of an exploratory search. Experiments are designed to compare results from the proposed method and Google's “search related to ....” The results show that the proposed one is more suitable for learning new associations and discovering new knowledge with highly likely relevance to a query. This work concludes that it is more suitable than Google for an exploratory search.

Internet data are heterogeneous, redundant, disordered, and exponentially growing. Finding the right information from them becomes an ever-challenging issue. Existing technologies such as inverted index and keyword matching can list user webpage matching with given search keywords. They cannot recognize potential relations among webpages to meet some rising user needs, e.g., exploratory search and personalized search. We propose an indexing network model that organizes information in webpages at three levels: words, webpages, and categories, thereby leading to a semantic association graph. Words are used as the description of webpages and categories. Webpage classification is used to gather similar webpages together. Hyperlinks imply the wisdom of the webpage creator, which can help us generate semantic relations among categories. With a clear organizational structure, an indexing network can provide support for many important applications including intelligent information retrieval, recommendation and decision support. In order to provide access to interfaces for the proposed indexing network, an indexing network algebra is defined. Finally, to validate the proposed model, an indexing network is generated based on 30 million webpages and its structure is analyzed. We also give methods to achieve “browsing navigation” and “personalized search” based on the generated network. Results reveal that the use of an indexing network can greatly facilitate exploratory information retrieval and personalized search.

The creation of value-added services by automatic composition of existing ones is gaining significant momentum as the potential silver bullet in service-oriented computing. A large number of composition methods have been proposed, and most of them are based on the matching of input and output parameters of services only. However, most services in the real world are not universally applicable, and some applicable conditions or restrictions are imposed on them by their providers. Such constraints have a great impact on service composition, but have been largely ignored by the existing methods. In this paper, they are discussed and defined, and a simple formal expression is adopted to describe them. Two novel concepts, called service intension and service extension, are presented, which allow one to divide the basic elements of a web service definition into two parts. Consequently, their use allows us to propose a constraint-aware service composition method in which service constraints are well taken care. The proposed solution includes a graph search-based algorithm and two novel preprocessing methods. A publicly available test set from ICEBE05 is used to evaluate and analyze the proposed methodology.

Owing to their rapid development, services are increasing rapidly in quantity. The consequence is that there are so many services that share the same or similar functions. Therefore, it is important to select a credible and optimal service. Reputation as one of the important parameters of services plays a significant role in the decision support for service selection. This paper proposes a novel two-phase method to calculate service reputation. The first phase uses a dynamic weight formula to calculate reputation such that it can reflect the latest tendency of a service. The second one uses an olfactory response formula to mitigate the negative effect of unfair ratings. Some experiments are conducted and the results validate the effectiveness of the proposed method.

The use of information technology and management systems for the betterment of health care is more and more important and popular. However, existing efforts mainly focus on informatization of hospitals or medical institutions within the organizations, and few are directly oriented to the patients, their families, and other ordinary people. The strong demand for various medical and public health care services from customers calls for the creation of powerful individual-oriented personalized health care service systems. Service computing and related technologies can greatly help one in fulfilling this task. In this paper, we present PHISP: a Public-oriented Health care Information Service Platform, which is based on such technologies. It can support numerous health care tasks, provide individuals with many intelligent and personalized services, and support basic remote health care and guardianship. In order to realize the personalized customization and active recommendation of intelligent services for individuals, several key techniques for service composition are integrated, which can support branch and parallel control structures in the process models of composite services and are highlighted in this paper.

With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to improve the efficiency of service discovery and composition is important. This paper proposes three categorization rules to classify services for a large scale repository to form a relational taxonomy. The service retrieve scope can be drastically narrowed by this taxonomy. Therefore, the efficiency of service discovery and service composition can be greatly improved. We evaluate and compare the performance of the proposed method and other related ones via a publicly available test set, ICEBE05. The experimental results validate the effectiveness and high efficiency of the proposed one.

The information technology has been recognized as one of the most important means to improve health care and curb its ever-increasing cost. However, existing efforts mainly focus on informatization of hospitals or medical institutions within organizations, and few are directly oriented to individuals. The strong demand for various health services from customers calls for the creation of powerful individual-oriented personalized health care service systems. Web service composition (WSC) and related technologies can greatly help one build such systems. This paper aims to present a newly developed platform called a Public oriented Health care Information Service Platform (PHISP) and several novel WSC techniques that are used to build it. Among them include WSC techniques that can well support branch and parallel structures.