Federated recommender system is emerging as a new paradigm for providing personalized services while preserving user data privacy. Most existing personalized federated recommender systems predict the user's next item by discretely training user and item embeddings. However, this training approach overlooks the user's behavioral patterns, suffers from low interpretability, and requires a substantial amount of data and meticulous fine-tuning to achieve stable and accurate embeddings. To address these limitations, we propose Federated Context-Aware Personalized Recommendation (FedCAR), a novel framework that leverages users’ recent interactions as behavioral context to guide prediction. Instead of static user embeddings, FedCAR dynamically constructs context representations by aggregating and weighting recently interacted item embeddings. Additionally, we incorporate a contrastive learning strategy that enables the model to capture shared behavioral structures across clients while maintaining personalized preferences, enhancing both generalization and robustness in heterogeneous settings. Experiments on 5 benchmark datasets show that FedCAR consistently outperforms state-of-the-art methods and provides interpretable recommendations by explicitly modeling context dependencies.

Existing weakly supervised temporal action localization (WTAL) methods typically follow a decoupled classification-localization pipeline: segment-level classifiers are trained first, and their predictions are then aggregated to score proposals at inference. Under this training-inference discrepancy, proposal scoring at inference relies on an additional aggregation step, which can accumulate errors from noisy segment responses and thus undermine score reliability. Moreover, proposal scores are often directly used as confidence without explicit score-quality modeling or quality-aware evaluation, further contributing to pronounced score-quality misalignment and thus widening the classification-localization gap. To address proposal score-quality misalignment, we propose ACL-Net, a framework for proposal score calibration. At its core is a dual-axis Proposal-level Action Consistency Learning (PACL) paradigm, implemented through two complementary modules: (i) a Semantic Consistency Module (SCM) that refines proposal representations by maintaining fused class centers to enforce compact and robust same-class features; within SCM, a cross-modal consistency-driven Classification Enhancement Module (CEM) denoises the fused class centers to mitigate error accumulation under weak supervision; and (ii) a Process Consistency Module (PCM) that derives geometry-aware reference scores from relative temporal relations among overlapping proposals, guiding the model to assess proposal quality in terms of relative process completeness and improve score-quality alignment. By jointly modeling semantic and process consistency to calibrate proposal scores, ACL-Net markedly improves localization accuracy. On THUMOS14 and ActivityNet1.3, it achieves state-of-the-art performance with uniform and substantial gains across multiple established baselines, while markedly lowering the expected calibration error (ECE).

Sequential recommendation aims to derive insights from user interaction records and make predictions based on relationships between users and items. However, most existing approaches do not effectively integrate user intents and preferences, which limits their capability to capture user behavior patterns. Additionally, these methods often struggle with poor performance in sparse data scenarios. To address these challenges, we proposeEL4SR, a sequential recommendation approach by integrating intent understanding and preference learning.EL4SRsimultaneously learns user intents and preferences through a dual-channel recommendation module, modeling both item and popularity sequences to enable mutual learning that captures the combined effects of intent and preference. Moreover, we enhance intent learning through contrastive learning, improving adaptability in sparse data contexts. We design several augmentation operators to improve the performance and robustness ofEL4SR. Extensive experiments on MovieLens and Amazon demonstrate the performance of our proposed method across various scenarios.

Microservice-based systems often suffer from reliability issues due to their intricate interactions and expanding scale. With the rapid growth of observability techniques, various methods have been proposed to achieve failure diagnosis, including root cause localization and failure type identification, by leveraging diverse monitoring data such as logs, metrics, or traces. However, traditional failure diagnosis methods that use single-modal data can hardly cover all failure scenarios due to the restricted information. Several failure diagnosis methods have been recently proposed to integrate multimodal data based on deep learning. These methods, however, tend to combine modalities indiscriminately and treat them equally in failure diagnosis, ignoring the relationship between specific modalities and different diagnostic tasks. This oversight hinders the effective utilization of the unique advantages offered by each modality. To address the limitation, we propose TVDiag , a multimodal failure diagnosis framework for locating culprit microservice instances and identifying their failure types (e.g., Net-packets Corruption) in microservice-based systems. TVDiag employs task-oriented learning to enhance the potential advantages of each modality and establishes cross-modal associations based on contrastive learning to extract view-invariant failure information. Furthermore, we develop a graph-level data augmentation strategy that randomly inactivates the observability of some normal microservice instances to mitigate the shortage of training data. Experimental results on four datasets show that TVDiag outperforms the state-of-the-art methods in multimodal failure diagnosis by at least 20.16% and 3.08% in terms of \(HR@1\) and F1-score, respectively.

Next activity prediction, a critical subtask of Predictive Process Monitoring (PPM), aims to forecast the upcoming activity of an ongoing process instance based on event logs generated by process-aware information systems, thereby enabling businesses to proactively mitigate potential risks. While recent deep sequence models have shown strong predictive capabilities, they often fail to explicitly capture the intrinsic dependencies that govern the conditions and impacts of event occurrences in business processes. To address this limitation, we propose a novel Place-Transition Embedding (PTE) framework that captures these dependencies through Petri-net–inspired token consumption and generation semantics. PTE employs a deep embedding network to simulate the evolution of markings, i.e., how each observed event drives token dynamics over places over time under our resource interpretation. In addition, we introduce a temporal scoring mechanism that ranks candidate activities by jointly considering resource feasibility and temporal urgency, improving both interpretability and predictive accuracy. Extensive experiments on six real-world datasets show that PTE achieves state-of-the-art performance for next-activity prediction. Moreover, PTE generalizes robustly under sparse training conditions, highlighting its practical applicability in real-world scenarios with limited business data.

The rapid development of the internet has led to an exponential increase in the scale of computing, storage, networking, and service resources. Traditional monolithic architectures are increasingly insufficient for managing these complexities. In contrast, microservice architectures have emerged as the mainstream solution with their inherent flexibility in deployment and scalability. To ensure system reliability, modern microservice architectures rely heavily on observability data, including logs, metrics, and traces. However, challenges such as network instability, service instance restarts, and system overloads frequently lead to intermittent loss of metric data. These missing data points impede comprehensive assessments of system health, significantly threatening system stability and reliability. To address the above challenge, we propose an anomaly detection model, ADmM, which integrates logs, metrics, and traces. ADmM first extracts template-level and semantic-level features from multimodal inputs. Then, a multi-scale autoencoder module is applied to impute missing metrics. For anomaly detection, the model represents microservice dependencies as a directed acyclic graph and leverages a graph neural network to learn generative patterns from normal system behavior. By measuring the deviation between observed values and reconstructed values, ADmM assigns anomaly scores to identify anomalies. Experiments conducted on three open-source benchmarks demonstrate that ADmM outperforms state-of-the-art methods across multiple anomaly detection metrics. Notably, it achieves F1-Score improvements of 5.77%, 5.48%, and 2.16% in scenarios with 40% incomplete metrics.

Weakly-supervised temporal action localization (WTAL) aims to identify and localize action instances in untrimmed videos using only video-level labels. Existing methods typically rely on original features from frozen pre-trained encoders designed for trimmed action classification (TAC) tasks, which inevitably introduces task discrepancy. Additionally, these methods often overlook the importance of considering action consistency from multiple perspectives, specifically the consistency in action processes and action semantics, both of which are crucial for the model's understanding of actions. To address these issues, we propose a novel WTAL method based on similar modality enhancement and action consistency learning (SEAL). First, we construct global descriptors for each action category, and use the pseudo-labels generated based on these descriptors to guide the model in learning more consistent representations, thereby mitigating task discrepancy. Second, we design two types of losses to achieve action consistency learning: process consistency loss, which penalizes candidate proposals that deviate from the action center to ensure the completeness of the action process, and semantic consistency loss, which employs local descriptors to help proposals of the same action category (especially those with apparent semantic confusion) learn similar feature distributions. Extensive experiments on the THUMOS14 and ActivityNet datasets demonstrate the superior performance of the proposed method compared to state-of-the-art methods.

With the development of federated learning techniques and the increased need for user privacy protection, the federated recommendation has become a new recommendation paradigm. However, most existing works focus on user-level federated recommendation, leaving platform-level federated recommendation largely unexplored. A significant challenge in platform-level federated recommendation scenarios is severe label skew. Users behave in various ways on different platforms, bringing up the rating and item bias problem. In this work, we propose FREIB (Federated Recommendation with Explicitly Encoding Item Bias). The core idea is explicitly encoding item bias during federated learning, addressing the problem of fuzzy item bias, and achieving consistent representation in label skew scenarios. We achieve this by utilizing global knowledge guidance to model common rating patterns and by aligning feature prototypes to enhance item encoding at the same rating level. Extensive experiments conducted on three public datasets demonstrate the superiority of our method over several state-of-the-art approaches.

With the widespread adoption of microservice architectures, system complexity has increased significantly, making fault root cause localization a critical issue in system operations. Under resource constraints or load pressure, the monitoring metrics that reflect system status often exhibit incompleteness, negatively impacting operational accuracy and system stability. To address this challenge, we propose ReconRCA, which consists of an offline reconstruction stage and an online localization stage. During reconstruction, ReconRCA leverages historical data, along with spatio-temporal models and attention mechanisms, to reconstruct missing metrics. In the online stage, it captures both intra and inter-metric correlations of microservices to achieve precise root cause localization. Experiments show that ReconRCA outperforms existing methods with both incomplete and complete data, achieving average top-1 hit rates at 33.28% and 39.74%, respectively.

Predictive Process Monitoring (PPM) is a critical technology for analyzing log data to forecast future events in ongoing process traces, enabling early warnings of business risks and timely interventions. Traditional PPM methods primarily focus on predicting a subsequent activity based solely on activity sequences. However, such approaches often neglect the suffix variability reflected by branching behavior, where identical prefixes may lead to multiple potential outcomes. To address this limitation, we propose a two-stage PPM model based on enabled state filtering for coarse-to-fine activity prediction. In the first stage, the model identifies all enabled activities based on the activity prefix, enhancing flexibility to handle variable suffixes. In the second stage, the model further integrates multi-attribute trace information along with the candidate activity set, employing a context-aware attention mechanism tailored to these candidates for fine-grained prediction. The constraints of the first stage allow the second stage to focus on the possible activities and relevant attributes, effectively reducing the search space and mitigating overfitting. Experimental results on four real-world datasets show that our approach outperforms traditional methods, particularly with variable suffixes. Ablation studies further highlight the critical role of the enabled state filtering strategy in reducing overfitting when modeling multi-attribute information.

The Vision Language Model (VLM) excels in aligning vision and language representations, and prompt learning has emerged as a key technique for adapting such models to downstream tasks. However, the application of prompt learning with VLM in federated learning (FL) scenarios remains underexplored. This paper systematically investigates the behavioral differences between language prompt learning (LPT) and vision prompt learning (VPT) under data heterogeneity challenges, including label skew and domain shift. We conduct extensive experiments to evaluate the impact of various FL and prompt configurations, such as client scale, aggregation strategies, and prompt length, to assess the robustness of Federated Prompt Learning (FPL). Furthermore, we explore strategies for enhancing prompt learning in complex scenarios where label skew and domain shift coexist, including leveraging both prompt types when computational resources allow. Our findings offer practical insights into optimizing prompt learning in federated settings, contributing to the broader deployment of VLMs in privacy-preserving environments.

ABSTRACT Developers utilize issue tracking systems to track ideas, feedback, tasks, and bugs for projects in the open‐source software ecosystem of GitHub. In this context, extensive bug reports and feature requests are raised as issues that need to be resolved. This makes issue resolution prediction become more and more important in project management. To address this problem, this paper constructed a multiple feature set from the perspectives of project, issue, and developer, by combining static and dynamic features of issues. Then, we refine a feature set based on the feature's importance. Furthermore, we proposed a method to explore what features and how these features affect the prediction of issue resolution time. Experiments are conducted on a dataset of 46,735 resolved issues from 18 popular GitHub projects to validate the effectiveness of the refined feature set. The results show that our prediction method outperforms the baseline methods.

Recommending API sequences is crucial in software development, saving developers time and effort. While previous studies primarily focus on accuracy, often recommending popular APIs, they tend to overlook less frequent, or ”tail,” APIs. This oversight, often a result of limited historical data, consequently diminishes the diversity of recommender systems. In this article, we propose DDASR, a framework for recommending API sequences containing both popular and tail APIs. To accurately capture developer intent, we utilize recent Large Language Models for learning query representations. To gain a better understanding of tail APIs, DDASR clusters tail APIs with similar functionality and replaces them with cluster centers to produce a pseudo ground truth. Moreover, a loss function is defined based on learning-to-rank to achieve an equilibrium in accuracy and diversity due to the inherent tradeoff between them. To evaluate DDASR, we conduct extensive experiments on Java and Python open source datasets. Results demonstrate that DDASR significantly achieves the best diversity without sacrificing accuracy. Compared to seven state-of-the-art approaches, DDASR improves accuracy metrics BLEU, ROUGE, MAP, and NDCG and diversity metric coverage by 108.28%, 67.30%, 88.59%, and 45.83%, respectively, on the Java dataset, as well as 9.83%, 2.45%, 8.06%, and 8.03%, respectively, on the Python dataset.

Optical coherence tomography (OCT) emerged as a promising, non-invasive, real-time, high-resolution imaging technology for cervical cancer detection. The scarcity of high-quality annotated cervical OCT images severely hinders the predictive performance of deep learning models. In contrast, self-supervised learning (SSL) methods offer a viable solution to the above challenge. This study aims to develop a cost-effective SSL framework that efficiently classifies high-resolution cervical OCT images to meet the clinical "see-and-diagnose" requirement for cervical lesions. Therefore, we propose COVE, a novel SSL framework with masked autoencoders based on VMamba with linear complexity. To our knowledge, COVE is the first SSL framework using masked image modeling for VMamba to leverage large unlabeled cervical OCT datasets. It has two specific designs: (1) 2D-Selective-Scan for visible patches (VP-SS2D): the VMamba encoder’s core module processes only visible patches for efficient pre-training and addressing the inconsistency between pre-training and fine-tuning; (2) visible patch feature-preserving (VPFP): visible patch features in each decoder block are replaced with encoder-extracted features, decoupling the encoder’s feature extraction from the decoder’s pixel reconstruction tasks. Experimental results showed that COVE outperformed existing SSL frameworks in five-fold cross-validation on a 1,452-subject cervical OCT dataset from a multi-center study and two external validation sets from top Chinese hospitals. Additionally, COVE demonstrated the highest pre-training efficiency, with significantly faster speed than existing SSL frameworks.

Predictive process monitoring, a technique within the field of process mining, aims primarily to forecast the next execution activity of ongoing traces. This capability enables organizations to proactively discover anomalies and enhance intelligent decision-making in business processes. Existing predictive methods only consider the individual context of traces or the structural constraints of process models, failing to comprehensively describe the state of ongoing traces. Therefore, this paper proposes SGAP (Sequential Graph for next Activity Prediction), a predictive model that combines contextual information within traces and collaborative behaviors among traces. In SGAP, sequential graphs are constructed by transforming activities and traces from event logs into nodes and edges, which offers a flexible framework for understanding the impact of various trace contexts on predictions. Moreover, to adaptively handle the message passing across multiple and heterogeneous edges in sequential graphs, we devise a trace attention mechanism that considers different sequential patterns of edges. Experimental results conducted over six real-world datasets demonstrate the effectiveness of sequential graph modeling on trace contexts and highlight the significance of SGAP in forecasting the next activities.

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing state-of-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.

Abstract The phenomenon of crossover cooperation and convergence among services has gained increasing attention in the modern service industry. Service boundaries have been expansively stretched into other domains rather than limited to their original domains to achieve value creation, fostering the emergence of crossover services. Consequently, a complex service ecosystem takes shape. However, there is a lack of the convergence‐evolution mechanism of crossover services for the adaptive transformation of service providers' businesses in this context. To address this problem, this paper proposes population‐based and community‐based convergence‐evolution patterns from the ecological perspective. Based on the analysis of these evolution patterns and the driven force of service evolution, we propose an ecology‐oriented evolution analysis method. Furthermore, we devise an automated tool to support the evolution design of crossover service ecosystems. Case studies and evaluation experiments show the feasibility and effectiveness of our proposed method and the corresponding tool.

Edge computing, a recently developed computing paradigm, seeks to extend cloud computing by providing users minimal latency. In a mobile edge computing (MEC) environment, edge servers are placed close to edge users to offer computing resources, and the coverage of adjacent edge servers may partially overlap. Because of the restricted resource and coverage of each edge server, edge user allocation (EUA), i.e., determining the optimal way to allocate users to different servers in the overlapping area, has emerged as a major challenge in edge computing. Despite the NP-hardness of obtaining an optimal solution, it is possible to evaluate the quality of a solution in a short amount of time with given metrics. Consequently, deep reinforcement learning (DRL) can be used to solve EUA by attempting numerous allocations and optimizing the allocation strategy depending on the rewards of those allocations. In this study, we propose the Dual-sequence Attention Model (DSAM) as the DRL agent, which encodes users using self-attention mechanisms and directly outputs the probability of matching between users and servers using an attention-based pointer mechanism, enabling the selection of the most suitable server for each user. Experimental results show that our method outperforms the baseline approaches in terms of allocated users, required servers, and resource utilization, and its running speed meets real-time requirements.

Autoscaling is critical for ensuring optimal performance and resource utilization in cloud applications with dynamic workloads. However, traditional autoscaling technologies are typically no longer applicable in microservice-based applications due to the diverse workload patterns and complex interactions between microservices. Specifically, the propagation of performance anomalies through interactions leads to a high number of abnormal microservices, making it difficult to identify the root performance bottlenecks (PBs) and formulate appropriate scaling strategies. In addition, to balance resource consumption and performance, the existing mainstream approaches based on online optimization algorithms require multiple iterations, leading to oscillation and elevating the likelihood of performance degradation. To tackle these issues, we propose PBScaler, a bottleneck-aware autoscaling framework designed to prevent performance degradation in a microservice-based application. The key insight of PBScaler is to locate the PBs. Thus, we propose TopoRank, a novel random walk algorithm based on the topological potential to reduce unnecessary scaling. By integrating TopoRank with an offline performance-aware optimization algorithm, PBScaler optimizes replica management without disrupting the online application. Comprehensive experiments demonstrate that PBScaler outperforms existing state-of-the-art approaches in mitigating performance issues while conserving resources efficiently.

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.

In edge computing, service providers aim to provide users with low latency, high reliability, and more secure services by deploying services to edge servers close to users. However, scheduling service requests in edge computing scenarios is challenging because the edge service nodes typically have limited resources. This limitation makes it difficult to simultaneously optimize multiple objectives, including resource utilization, total running time, and average waiting time. Existing methods are insufficient to provide intelligent scheduling services that consider multiple optimization objectives, resulting in unsatisfactory scheduling results. To address this problem, we propose PSNet, a deep reinforcement learning-based Pointer Scoring Network, for edge task scheduling. PSNet integrates multiple pointer networks to optimize multiple objectives and obtain final scheduling results. Experiments carried out on three publicly available real-world datasets show that our method can effectively solve the multiobjective optimization problem on edge service requests, with an 11% improvement over several state-of-the-art methods.

As an emerging computing paradigm, mobile edge computing (MEC) is receiving growing attention. In MEC, user requests on software applications are firstly sent to edge servers for processing, which can significantly reduce the latency compared with sending to cloud centers. Furthermore, a software application adopting the popular microservice architecture usually contains multiple intercommunicating microservices. This suggests that the software used by a moving user will invoke different microservices on different locations. However, a microservice request may fail as no corresponding microservice is deployed on nearby edge servers due to resource limitation and coverage limitation. Moreover, if the user is moving at high speed, the user may leave the coverage of the edge server before receiving a response. To address this issue, we propose a microservice pre-deployment approach by integrating mobility prediction and service composition. Our work aims to improve the success rate of both request and response for multi-users while reducing the resource cost of pre-deployment. Three groups of experiments demonstrate that our approach can significantly improve the performance of microservice pre-deployment compared with several baseline approaches.

Recently, it has become a trend for developers to build applications using the microservice architecture.The functionality of each application is divided into multiple independent microservices, which are interconnected to others.With the emergence of cloud-native technologies, such as Docker and Kubernetes, developers can achieve a consistent and scalable delivery for complex software applications.However, it is challenging to diagnose performance issues in microservices due to the complex runtime environments and the numerous metrics.In this paper, we propose a novel root cause analysis approach named AAMR.AAMR firstly constructs a service dependency graph based on real-time metrics.Next, it updates the anomaly weight of each microservice automatically.Finally, a PageRankbased random walk is applied for further ranking root causes, i.e., ranking potential problematic services.Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several state-of-the-art methods.

Abstract Crossover services involve deep convergence of services in different domains. Requirements analysis of crossover services often requires the collaboration of engineers in different domains and various organizations. The industry has a demand for sophisticated requirements engineering (RE) methods to address challenges brought by crossover and convergence. However, many methods proposed by researchers have not been widely used in the industry, and the reason is that these methods provide technological solutions to the problems and challenges that may not be considered accurate or even real by practitioners in the crossover services industry. A great effort is needed to explore what needs, expectations, and constraints that RE approaches must satisfy and promote the adoption of novel RE approaches in the crossover services industry. The authors have conducted an industrial study to gain an in‐depth understanding of practitioners' needs concerning RE research and method development. The study involved qualitative interviews as well as questionnaires to collect data. The objective of this study is to investigate issues, challenges, and practices of crossover services RE from practitioners' perspectives and to reveal potential guidelines for researchers and tools developers. Findings from five aspects of RE methods are reported and some research directions for future crossover services RE research are provided.

In recent years, more and more developers have been building applications based on the cloud-native architecture. Container and microservice are two essential components in the cloud-native architecture. Container technologies like Docker and Kubernetes can help developers achieve a consistent and scalable delivery for complex software applications. On the other hand, microservice technologies can facilitate the division of complex applications into multiple functionality-independent and composable components, which further increases the flexibility of applications. With the support of cloud computing platforms, cloud-native applications will be easier to manage and maintain, together with higher scalability. However, it is challenging to identify performance issues on microservices due to the complex runtime environments and the numerous monitoring metrics. Towards this issue, this paper proposes a novel root cause analysis approach. Our approach firstly constructs a service dependency graph based on the metrics collected in real time. Next, the anomaly weight of each microservice is automatically updated by extending the mRank algorithm. Finally, a PageRank-based random walk is adopted to rank root causes further, i.e. to rank potential problematic services. Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several baseline methods.

Quality of Service (QoS) prediction is a hot topic in services computing, which has been extensively investigated in the past decade. Many approaches have been proposed to predict unknown QoS values of Web services according to their historical invocation records. These methods usually analyze each individual QoS data as a basic unit while ignoring the intrinsic characteristics of these time-series QoS data. In an extremely dynamic environment, how to capture the intrinsic and time-varying characteristics of QoS data from a finer-grained perspective becomes an essential issue to achieve accurate prediction. In this paper, we propose a hybrid QoS prediction approach by combining the Empirical Mode Decomposition (EMD) and the multivariate LSTM (Long Short-Term Memory) model. Our approach aims to capture the potential information in the historical sequence and perform accurate QoS forecasting. Experiments conducted on two realworld datasets show that our approach outperforms several state-of-the-art methods in QoS prediction performance.

With the increasingly complex requirements of users in modern society, it is difficult to meet the complex goals of users by services from a single domain, and thus crossover service convergence, which aims to provide deep service integration and innovation across boundaries of industries, organizations, and value chains becomes an urgent need. Goal convergence is the first step to guide the process of crossover service convergence. However, there is currently no systematic theory and method to address this problem. In this paper, we propose a method to support goal convergence of crossover services. Firstly, two goal models from different domains are converged according to new user requirements. We then detect inconsistencies of goal models and resolve potential conflicts. We have developed a goal model convergence tool to help requirements analysts perform goal convergence in a visualized way. A case study is presented to show the feasibility of the proposed method.

With the rapid development of service-oriented computing, a large number of software applications have been developed based on the services computing framework. It is well known that software engineering is a knowledge-intensive activity, and thus the effective management of service-related knowledge facilitates service-oriented software development. Although many methodologies have been proposed for service-oriented knowledge management, little attention has been paid to mining knowledge (especially domain-specific functionalities) from service resources. To address this issue, we propose an approach to mine domain knowledge on service goals (i.e., service functionalities) from textual descriptions of services. The approach consists of two components: service goal extraction from textual service descriptions based on linguistic analysis and domain service goal construction that merges semantically similar service goals within a domain. The effectiveness of the proposed approach is validated by a series of experiments conducted on a real-world dataset crawled from the ProgrammableWeb.

Summary Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold‐start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user‐service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold‐start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F‐Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.

Quality of Service (QoS) prediction is an important task in services computing, which has been extensively investigated in the past decade. Many time-aware QoS prediction approaches have been proposed and achieved encouraging prediction performance. However, they did not provide effective model updating mechanisms, and thus have to periodically retrain the whole models to deal with the newly coming data. How to timely update the prediction model to precisely predict missing QoS values of candidate services becomes an urgent issue. In this paper, we propose a novel personalized LSTM based matrix factorization approach for online QoS prediction. Our approach can capture the dynamic latent representations of multiple users and services, and the prediction model can be timely updated to deal with the new data. Experiments conducted on a real-world dataset show that our approach outperforms several state-of-the-art approaches in online prediction performance.

With the broad adoption of service-oriented architecture, many software systems have been developed by composing loosely-coupled Web services. Service discovery, a critical step of building service-based systems (SBSs), aims to find a set of candidate services for each functional task to be performed by an SBS. The keyword-based search technology adopted by existing service registries is insufficient to retrieve semantically similar services for queries. Although many semantics-aware service discovery approaches have been proposed, they are hard to apply in practice due to the difficulties in ontology construction and semantic annotation. This paper aims to help service requesters (e.g., SBS designers) obtain relevant services accurately with a keyword query by exploiting domain knowledge about service functionalities (i.e., service goals) mined from textual descriptions of services. We firstly extract service goals from services’ textual descriptions using an NLP-based method and cluster service goals by measuring their semantic similarities. A query expansion approach is then proposed to help service requesters refine initial queries by recommending similar service goals. Finally, we develop a hybrid service discovery approach by integrating goal-based matching with two practical approaches: keyword-based and topic model-based. Experiments conducted on a real-world dataset show the effectiveness of our approach.

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

In the era of service-oriented software engineering (SOSE), service clustering is used to organize Web services, and it can help to enhance the efficiency and accuracy of service discovery. In order to improve the efficiency and accuracy of service clustering, this paper uses the self-join operation in relational database (RDB) to realize Web service clustering. Based on storing service information, it does the self-join operation towards the Input, Output, Precondition, Effect (IOPE) tables of Web services, which can enhance the efficiency of computing services similarity. The semantic reasoning relationship between concepts and the concept status path are used to do the calculation, which can improve the calculation accuracy. Finally, we use experiments to validate the effectiveness of the proposed methods.

Recommender systems have been studied comprehensively in both academic and industrial fields over the past decade. As user interests can be affected by context at any time and any place in mobile scenarios, rich context information becomes more and more important for personalized context-aware recommendations. Although existing context-aware recommender systems can make context-aware recommendations to some extent, they suffer several inherent weaknesses: (1) Users’ context-aware interests are not modeled realistically, which reduces the recommendation quality; (2) Current context-aware recommender systems ignore trust relations among users. Trust relations are actually context-aware and associated with certain aspects (i.e., categories of items) in mobile scenarios. In this article, we define a term role to model common context-aware interests among a group of users. We propose an efficient role mining algorithm to mine roles from a “user-context-behavior” matrix, and a role-based trust model to calculate context-aware trust value between two users. During online recommendation, given a user u in a context c , an efficient weighted set similarity query (WSSQ) algorithm is designed to build u ’s role-based trust network in context c . Finally, we make recommendations to u based on u ’s role-based trust network by considering both context-aware roles and trust relations. Extensive experiments demonstrate that our recommendation approach outperforms the state-of-the-art methods in both effectiveness and efficiency.

With the rapid development of SOA (Service Oriented Architecture), an increasing number of Web services have been published on the Internet. How to recommend suitable Web services to users becomes a challenging problem. Existing Web services recommendation approaches based on collaborative filtering mainly focus on QoS (Quality of Service) prediction. Recommending services based on users' ratings on services are seldom reported since such explicit feedback data is difficult to collect. In this paper, we report a dataset of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. In addition, time is becoming an increasingly important factor in recommenders since time effects influence users' preferences to a large extent. Based on the collected dataset, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.

Recent years have witnessed a rapid growth in using Web services for data publishing and sharing among organizations. To improve the efficiency of software development and economize on human and material resources, service reuse is viewed as a powerful means which will not only reuse atomic services, but also reuse arbitrary granularities of Service Process Fragments (SPFs). However, effectively reusing arbitrary granularities of SPFs has not been solved yet. In this paper, we propose a novel method of SPF reuse, named SCKY, based on the Cocke-Kasami-Younger (CKY) algorithm. We first present an extended CKY to do SPF-query. Then we address how to do SPF-query by a probability CKY, i.e. return a SPF with maximum emergence probability. Finally, we explore the QoS Query of SPF. Through a set of experiments, the effectiveness and robustness of our approach are evaluated, where the dataset is constructed by the Web Service Challenge Testset Generator1 (CTG).

With the increasing number of Web services available on the Internet, how to recommend Web services to interested users effectively and efficiently remains to be a big challenge. At present, collaborative filtering (CF) is the most widely used technique in the design of recommender systems to handle information overload. For Web services, however, it is difficult for user to collect personalized QoS (Quality of Service)data and other explicit feedbacks such as ratings. In most cases, only a part of the implicit feedbacks (e.g., watchlist) is available in service registry. In this paper, we leverage implicit feedback from user's watchlist to build a CF-based recommender system for Web service. Our main contribution is to transform implicit feedbacks into explicit ratings to improve the accuracy of service recommendation. More specifically, we first construct binary user-service rating matrix according to the implicit feedback from the watchlist. Then, temporal and tag preference are combined into the original rating matrix to generate a more accurate pseudo rating matrix, which can reflect users' different preference on services in their own watchlists. Finally, we use traditional user-based CF method to produce a personalized service recommendation list with corresponding pseudo ratings. Moreover, the empirical experiments based on ProgrammableWeb show that compared with traditional log-based CF method, the recommender system with temporal and tag preference is more accurate and precise.

Service reuse aims at improving the efficiency of software development and providing common functionalities which are not linked to any particular business process. However, the existing service reuse methods are confined to the reuse of atomic services or processes encapsulated as stand-alone services. How to reuse arbitrary granularities of Service Process Fragment (SPF) is a challenging problem with great application value. This paper presents a novel Variable Granularities Index (VGI) based on SSM-Tree on service processes. VGI could realize the unified index on both atomic and composite services and maximize reuse of them. To verify the feasibility and effectiveness, we construct a sample dataset which contains 500 thousand processes and 127 million atomic services based on the Web Service Challenge Testset Generator (CTG). The experimental results show an effective and efficient approach for SPF query.

As one foundational technology of cloud computing, services computing is playing a critical role to enable provisioning of software as a service (SaaS). However, how to effectively and efficiently discover proper available services from the cloud of resources remains a big challenge. This paper reports our continuous efforts on semantic services discovery. We extend the Support Vector Machine (SVM)-based text clustering technique in the context of service-oriented categorization in a service repository, and propose an iterative process to incrementally enrich domain ontology. A popular Web 2.0 mashup platform is used as a testbed; and preliminary evaluation results are reported.

Currently there exist many kinds of semantic Web Service models on the internet. They are heterogeneous so that it is hard to understand and interoperate each other. In this paper, we study several mainstream semantic Web Service models, and extract meta-models for each from the perspective of semantic Web Service discovery. Based on this, we propose the universal meta-model for semantic Web Service registration within the background of meta-model framework for interoperability (MFI, ISO/IEC19763). Some cases are studied and infrastructure supporting our work is demonstrated. The work in the paper may be regarded as an extension to UDDI on service semantics facet, and has been proposed to ISO as ISO/IEC 19763 Part 7.

With the development of web service applications, how to realize on-demand services selection according to user's personal requirements is a hot research work in modern times. WS-CDL (Web Services Choreography Description Language) is a W3C candidate recommendation for the description of peer-to-peer collaborations for the participants in web services interaction. However, the goal of the service choreography is lacking and we can't understand the information exchange from the whole point. This paper extends WS-CDL from the aspect of goal that services to implement, and this can lay the foundation of on-demand service selection. Finally, the feasibility of the proposed method is validated through a case study.

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