Crowdsourced delivery platforms (e.g., Meituan, Uber Eats, DoorDash) have become vital infrastructure in urban logistics, yet their competitive order-grabbing mechanisms often lead to strategy homogenization, inefficiency, and income inequality. This paper presents ARDE (Adaptive Regulation via Dual-layer Evolution), an evolutionary governance framework that integrates individual reinforcement learning with adaptive platform-level regulation. The outer agent dynamically generates governance signals based on system diagnostics (strategy entropy, Gini coefficient, completion rate), while inner agents employ Diffusion Q-Learning guided by a language-model-driven reward shaping module to promote fairness and strategy diversity. Experiments on real-world datasets show that ARDE achieves stable diversity (0.997 ± 0.184), reduces inequality (Gini change 1.3%), and maintains high efficiency. Further comparison (ARDE-PPO vs. MAPPO) confirms that its advantages stem from explicit hierarchical governance rather than algorithmic coincidence. Overall, ARDE offers a scalable and interpretable paradigm for reconciling individual rationality with collective welfare in gig economies and other multi-agent socio-technical systems.

Edge service monitoring is essential for ensuring the robustness and efficiency of service executions, where predictive monitoring enables proactive detection of potential service violations. Current approaches for predictive monitoring, which mostly adoptSignalTemporalLogic (STL) specifications for requirements representation and evaluation, primarily focus on deterministic signals, and thus, may lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper proposesBayesianSTL(BSTL), an extension ofSTLthat enables probabilistic reasoning over stochastic signals. Specifically,BayesianNeuralNetworks (BNNs) are employed to generate sequences of posterior probability distributions, offering more comprehensive predictive insights compared to traditional point- or interval-based methods with deterministic sequential predictions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, aBSTL-based predictive monitoring framework is developed, where a service constraint is formally specified by aBSTLformula and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of aBSTLformula are rigorously estimated. Extensive experiments on publicly available datasets demonstrate thatBSTLoutperforms baseline techniques in terms of expressiveness, robustness, and applicability.

Metaverse service is a product of the convergence between Metaverse and service systems, designed to address service-related challenges within Metaverse. With the rise of large language models (LLMs), agents are employed to represent service entities, facilitating various service events and interactions in Metaverse service ecosystem. However, existing LLM-based agents exhibit critical limitations in emotional state integration, failing to emulate the bounded rationality required for bridging virtual-world services with real-world services, such as emotion measurement, emotional state evolution and emotional decision. This paper proposes an explainable emotion alignment framework for LLM-based agents in Metaverse Service Ecosystem. It aims to integrate factual factors into the decision-making loop of LLM-based agents, systematically demonstrating how to achieve more relational emotion alignment for these agents. Finally, a simulation experiment in the Offline-to-Offline food delivery scenario is conducted to evaluate the effectiveness of this framework, obtaining more realistic social emergence.

With the rise of service computing, cloud computing, and IoT, service ecosystems are becoming increasingly complex. The intricate interactions among intelligent agents make abnormal emergence analysis challenging, as traditional causal methods focus on individual trajectories. Large language models offer new possibilities for Agent-Based Modeling (ABM) through Chain-of-Thought (CoT) reasoning to reveal agent intentions. However, existing approaches remain limited to microscopic and static analysis. This paper introduces a framework: Emergence Analysis based on Multi-Agent Intention (EAMI), which enables dynamic and interpretable emergence analysis. EAMI first employs a dual-perspective thought track mechanism, where an Inspector Agent and an Analysis Agent extract agent intentions under bounded and perfect rationality. Then, k-means clustering identifies phase transition points in group intentions, followed by a Intention Temporal Emergence diagram for dynamic analysis. The experiments validate EAMI in complex online-to-offline (O2O) service system and the Stanford AI Town experiment, with ablation studies confirming its effectiveness, generalizability, and efficiency. This framework provides a novel paradigm for abnormal emergence and causal analysis in service ecosystems. The code is available at https://anonymous.4open.science/r/EAMI-B085.

Edge service monitoring is crucial for ensuring the robustness and reliability of service executions. Predictive monitoring, in particular, enables proactive detection of potential service violations. Existing predictive monitoring approaches, often leveraging Signal Temporal Logic (STL) for requirement specification, primarily focus on deterministic signals, and thus, lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper introduces Bayesian STL (BSTL), an extension of STL that enables probabilistic reasoning over stochastic signals. Specifically, Bayesian Neural Networks (BNNs) are utilized to transform deterministic sequential predictions into sequences of posterior probability distributions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, a BSTL-based predictive monitoring framework is developed, wherein service constraints are formally specified by BSTL formulae and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of BSTL formulae are rigorously estimated. Extensive experiments on publicly available datasets demonstrate that BSTL outperforms baseline techniques in expressiveness, robustness, and applicability.

As the social environment is growing more complex and collaboration is deepening, factors affecting the healthy development of service ecosystem are constantly changing and diverse, making its governance a crucial research issue. Applying the scenario analysis method and conducting scenario rehearsals by constructing an experimental system before managers make decisions, losses caused by wrong decisions can be largely avoided. However, it relies on predefined rules to construct scenarios and faces challenges such as limited information, a large number of influencing factors, and the difficulty of measuring social elements. These challenges limit the quality and efficiency of generating social and uncertain scenarios for the service ecosystem. Therefore, we propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents that autonomously optimize experimental schemes to construct an experimental system and generate high quality scenarios. Specifically, the Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions. These agents work in coordination, with the PA adjusting the experimental scheme in real time by perceiving the states of each agent and these generating scenarios. Experiments on the ProgrammableWeb dataset illustrate our method generates more accurate scenarios more efficiently, and innovatively provides an effective way for service ecosystem governance related experimental system construction.

Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that characterizes CoT reasoning through the dual lenses of sufficiency and necessity. Incorporating causal Probability of Sufficiency and Necessity allows us not only to determine which steps are logically sufficient or necessary to the prediction outcome, but also to quantify their actual influence on the final reasoning outcome under different intervention scenarios, thereby enabling the automated addition of missing steps and the pruning of redundant ones. Extensive experimental results on various mathematical and commonsense reasoning benchmarks confirm substantial improvements in reasoning efficiency and reduced token usage without sacrificing accuracy. Our work provides a promising direction for improving LLM reasoning performance and cost-effectiveness. The code will be publicly available upon acceptance at: https://anonymous.4open.science/r/causalmath-1CEF.

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favorite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort1to find a trade-off solution among user-side fairness, provider-side fairness, and personalized recommendations utility. Previous works habitually treat this issue as a knapsack problem, incorporating both-side fairness as constraints. In this paper, we adopt a novel perspective, treating each recommendation list as a runway rather than a knapsack. In this perspective, each item on the runway gains a velocity and runs within a specific time, achieving re-ranking for both-side fairness. Meanwhile, we ensure the Minimum Utility Guarantee for personalized recommendations by designing a Binary Search approach. This can provide more reliable recommendations compared to the conventional greedy strategy based on the knapsack problem. We further broaden the applicability of FairSort, designing two versions for online and offline recommendation scenarios. Theoretical analysis and extensive experiments on real-world datasets indicate that FairSort can ensure more reliable personalized recommendations while considering fairness for both the provider and user.

Anomaly detection has emerged as one of the core research topics to support workflow applications across various domains. To differentiate anomalies from underlying normal patterns of workflows, Graph Neural Networks (GNNs) models have been introduced. These models leverage time series data to construct graph structures, in order to explicitly capture task dependencies among industrial Internet of Things (IoT) devices, and thus, to identify deviations from predicted behaviours as anomalies. However, existing forecasting-based anomaly detection methods may not accurately detect certain anomalies, since they have seldom considered valuable information uncovered by historical sensory data, but presented as domain knowledge. To address this limitation, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Based on which, a knowledge-enhanced graph attention-based forecasting network is developed to predict the future behaviours of IoT devices. Anomalies, such as those caused by cyber-attacks in workflows, are detected by analyzing deviations from these predicted behaviours in conjunction with domain-specific knowledge. A case study is presented, along with extensive experiments conducted on publicly available datasets. Evaluation results demonstrate that KeAD outperforms the state-of-the-art techniques in terms of anomaly detection accuracy.

The increasing prevalence of smart services in daily life drives the rapid emergence of service ecosystems across various domains such as E-commerce, cloud manufacturing, and crowdsourcing. Evaluating the utility of these ecosystems is challenging due to complex characteristics like diverse crowd intelligence, cascading service network effects, and the interplay of individual interests. To address these challenges, this study introduces an innovative utility evaluation model that integrates individual and systemic factors with multidimensional metrics, reconciling micro and macro perspectives. This model effectively addresses potential conflicts between individual and systemic benefits, supporting the continuous learning and evolution of agents within service ecosystems. It employs value entropy to precisely model and interpret complex nonlinear emergence phenomena. The model's universal framework offers high customizability and broad applicability, facilitating specific adaptations across different service ecosystem scenarios. Additionally, a visual multi-agent system simulation tool has been developed to adjust agent attributes and cooperative topologies, allowing for the observation of system responses. Empirical validation confirms the model's accuracy and provides a mechanistic explanation for emergence phenomena in human societies. The findings demonstrate the model's high coordination and effectiveness in managing both linear and nonlinear characteristics, presenting a powerful and flexible tool for utility evaluation in service ecosystems. Our code is available at https://github.com/yxn9191/value_entropy.

The movement of mobile robots in a known environment can be viewed as a Multi-Agent Path Finding (MAPF) problem. MAPF is influenced by various factors such as the application scenarios, the scale and attributes of the agents. However, MAPF assumes agents to be abstract homogeneous nodes, without considering differences in agent attributes. Therefore, this paper proposes a general method to improve the abstract MAPF algorithms into the specific Multi-Attribute Heterogeneous MAPF (MAH-MAPF) algorithms. The method consists of three steps: 1) Define the MAH-MAPF problem based on the attributes of the robots in the application scenario. 2) Based on the agent attributes and definition, transform the MAPF algorithm into MAH-MAPF algorithm by modifying the homogeneous abstract agent assumption. 3) Reduce path conflicts caused by agent attributes to improve the performance of the MAH-MAPF algorithm. The experimental results indicate that the method proposed in this paper is effective and general.

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

The governance of service ecosystem needs to balance efficiency and fairness to promote the sustainable development of the system, making it an important topic. However, service entities in the service ecosystem have autonomy and engage in dynamic game with governance strategies, leading to governance challenges. To address this issue, we model the governance process as a repeated sequential game between the governance algorithm and service entities, and propose a novel two-level learning algorithm. This algorithm considers the learning evolution of service entities and the co-evolutionary of the governance algorithm, using reinforcement learning to learn effective governance strategies while also considering the response function of service entities to balance efficiency and fairness. Combining the ideas of bilevel optimization and online learning, this algorithm effectively balances exploration (understanding the service entities’ responses function) and exploitation (choosing efficient actions). We apply the algorithm to a classic service ecosystem, the ride-hailing service system, and empirically demonstrate its effectiveness in the governance task of order dispatching.

With the increasing prevalence of smart services in our daily lives, service ecosystems are rapidly emerging across various domains, including E-commerce, cloud manufacturing, and crowdsourcing, among others. However, evaluating the utility of service ecosystems presents several challenges due to their complex characteristics, including the diversity in crowd intelligence, the cascading effects of service networks, and the interest game among individuals. Current evaluation methods are limited in providing a comprehensive system-wide view, highlighting various shortcomings. To address these challenges, this article introduces a multidimensional and integrated model for evaluating service utility, comprising three key components: (1) The individual utility model is built to facilitate the transition from specific scenarios to general evaluations. (2) Value entropy is introduced to assess the impact of nonlinear emergence on system utility. (3) The system utility model is used to enable the integration of both linear and nonlinear factors within the system. Furthermore, a crowdsourcing service platform is taken as an example to verify the effectiveness of the proposed model. The results show that the proposed model is effective in providing new means and ideas for evaluating and analyzing service ecosystems.

Data service marketplaces are pivotal elements of an intelligent society as they bridge user requirements and the realization of value from data services. Ensuring the efficient circulation of data services and driving industrial chains to create value within data service marketplaces has become a core issue in developing intelligent societies, posing an urgent concern for researchers and governments. Within the context of service ecosystems, individual data services can no longer meet the requirements of users and organizations thus moving towards convergence, which brings new governance challenges both economically and technically. This paper comprehensively explores the governance of data service marketplaces by combining insights from service ecosystems and data service value. It begins by introducing data service marketplaces. Data servitization and standardized workflows are driving the massive expansion of data services marketplaces. Then it analyzes the complexity of marketplaces under service ecosystems. Furthermore, the governance issues and research themes of data service marketplaces were elaborated at macro, medium, and micro scales. This paper provides new ideas for the value creation and development of data service marketplaces.

Abstract Mobile application service ecosystems (MASEs) are highly complex systems that involve multiple factors influencing value creation. However, current research on the impact of structural holes (SHs) as an organizational characteristic on value creation in MASE is still insufficient. To address this research gap, this paper proposes a value creation model for MASE and investigates the impact of SH on the value creation of MASE. Moreover, this paper investigates the correlation between SH and diversity, which can facilitate regulating the value creation process in MASE. Finally, we construct a computational experiment, comparing and analyzing how changes in SH affect the value creation of MASE and the correlation between SH and diversity. The findings of this study can be used to induce the evolution of MASE and promote its value maximization.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Governance strategies related to platform ecosystems have become a vital issue for developing a smart society, attracting governments’ and practitioners’ attention. Under the consensus of “service as a commodity” and “platform as market,” service providers, platforms, services, and various supply demand matching methods form new supply processes. These elements are continuously and uncertainly changing during supply demand matching, which makes platform ecosystems constantly evolving. However, when multiple supply demand matching methods coexist such as service composition and crossover fusion, dynamic service supply and matching cause dilemmas in the platform ecosystem governance. To this end, this article proposes a model for platform ecosystem evolution with four dynamics: 1) dynamics between ISPs (services) and platforms; 2) dynamics between users and platforms; 3) dynamics among services; and 4) dynamics between services and demands. The model considers multiple supply demand matching methods and considers both fully online services and incompletely online services. Then, according to the market operation law, we design six evaluation indexes such as demand matching rate, service diversity, and market concentration to evaluate the efficiency of the platform market. Finally, a computational experiment system is established to simulate the dynamic supply and matching processes. The experimental results show that reducing the cost of service release can increase the amount of demand and the diversity of services, and the monopoly of digital platforms is a natural trend to improve the efficiency of supply and demand. The model provides a reference for the governance of platform ecosystems and lays a foundation for further research on the value cocreation mechanism of platform ecosystems.

Service recommendations are crucial for developers to create mashups such as mobile applications, workflows, e-business solutions, etc. Existing methods based on collaborative filtering or content analysis are manual and cannot automatically acquire services that align with the requirements of mashup creation. A possible solution to automatically acquiring necessary services for mashups is the seq2seq (sequence to sequence) generation model, which has demonstrated promising performance in automatic text and program code generation. However, two main challenges must be tackled in service acquisition based on the seq2seq model. First, the seq2seq model can only acquire a set of services without inter-service dependencies, but such dependencies are crucial in the generation of sequences for services. Second, external knowledge must be leveraged to recommend services more accurately that fulfill developers' requirements, such as similar historical user requirements and combining mashup category information, due to the incomplete description of user requirements. To tackle these challenges, this paper proposes GSR (Generation ofServiceRecommendations), an approach that can automatically acquire services based on user requirements. Specifically, GSR employs reinforcement learning to learn the inter-dependencies among services and integrate dependencies into service recommendations. To further improve the quality of the acquired services, GSR retrieves relevant user requirements based on BERT (Bidirectional Encoder Representation from Transformers) to help identify potential services. Experiment results conducted on real-world datasets show the superior performance of GSR. Compared with the existing recommendation approaches, the precision metric is increased by up to 1.99x, and the recall metric is increased by up to 12%.

Edge service monitoring is essential to guarantee the healthy of service compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, but they are inadequate for that of inter- and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to the execution context. To address these challenges, this paper formulates the problem of edge service monitoring as the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, an adaptive monitoring mechanism is developed, where constraints are converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results show that CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.

Social recommendations play a crucial role in providing personalized services to users by leveraging social relationships and user sessions. Despite recent advancements, it still faces challenges in dealing with social inconsistency and the loss of critical semantic information in user-service interactions. To overcome these problems, an Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users’ fresh interests. Specifically, the graph structure features on user-service interactions and the correlations between users and different sequences are extracted by the graph contrastive learning module. Then, social consistency sampling based on the graph convolutional network is adopted to filter out noise information effectively. Finally, time-sliced representations on the dual side (user, service) are integrated to capture users’ evolving interests by employing gated recurrent units. Comprehensive experiments on three datasets demonstrate the proposed model consistently outperforms the representative baseline methods in various evaluation metrics. EGCLSR facilitates the recommendation of services that fulfill instant requirements within dynamically evolving user interests.

The conventional crowdsourcing system is dependent on a centralized platform, which grants the platform owner undue authority to manipulate the operation of the system for unethical profits. In this paper, the crowdsourcing system is revolutionized in a decentralized manner with Blockchain as a Service (BaaS). All crowdsourcing operations are implemented with smart contracts deployed on blockchain. Requesters invoke these contracts to publish tasks, while workers invoke them to submit solutions. Notably, the operation of assigning tasks is regarded as a crowdsourcing task to be performed by assigners. Multiple assigners compute respective task assignment schemes in an off-chain manner, and then submit the schemes to blockchain for competition. Performance evaluations show that not only the operating efficiency of the crowdsourcing system is improved, but also the adverse consequences of the system being maliciously manipulated are avoided. The proposed decentralized crowdsourcing system is anticipated to restructure the business model of the conventional crowdsourcing industry.

With the advent of mobile internet, Online-to-Offline (O2O) home services have emerged, such as home healthcare and repair services, greatly facilitating our lives. Customers book services through online platforms, and workers provide the requested services at the customers’ homes offline. However, each time workers travel to customers’ homes, they incur opportunity cost, leading to increased cost for home services. In this paper, we propose to bundle several O2O home service requests close to each other and match them with a worker. Therefore, requests that are in a bundle can split the worker’s opportunity cost. Specifically, we formalize the request bundling problem for O2O home services, which aims to minimize the overall cost of completing all requests while satisfying the time and Quality of Service(QoS) constraints. We present three Bi-layer Greedy request bundling(BiG) algorithms to solve it, including BiG-LEV, BiG-DIST, and BiG-COST. Besides, a cost accounting method based on Shapley value is designed to calculate the actual cost of each service for in-depth analysis. Finally, we illustrate a case of bundling requests for home healthcare services and compare the performance of the three algorithms.

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

With the applicability of edge intelligence in various domains, anomaly detection, which aims to identify unusual and infrequent circumstances, is regarded as a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Generally, sensory data are gathered at the network edge and completely transmitted to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of anomaly. Considering the occurrence infrequency of anomalies, this strategy may transmit relatively huge volume of sensory data, which may reflect a healthy situation indeed, to the cloud. To mitigate this problem, this article proposes an accurate anomaly detection mechanism with energy efficiency in three-tier IoT–edge–cloud collaborative networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge networks, the edge node applies the marching squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A sensory data filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are transmitted to the cloud and, thus, the network traffic is decreased significantly. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are determined by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered for boundary refinement. Extensive experiments are conducted on an air quality hazardous gas data set from Toward Data Science, and evaluation results show that our technique outperforms the state-of-the-art counterparts in boundary accuracy and energy consumption.

The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API functions and API quality performances (e.g., popularity, compatibility, and diversity) simultaneously. These decrease the mashup development success rate and the mashup developers’ satisfaction significantly. In view of these challenges, a novel web APIs’ recommendation method named the popularity-aware and diverse method of web API compositions’ recommendation (PD-WACR) is proposed in this article. In concrete, we model web APIs’ functions, popularity, and compatibility with an API correlation graph. Afterward, correlation graph-based web APIs’ recommendation is performed with popularity and compatibility guarantee. Moreover, a top-$k$strategy is adopted in the recommendation process, so as to diversify the final recommended web APIs’ results. Finally, massive experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com. Experimental comparisons with related methods show the advantages and innovations of the proposed PD-WACR method.

The Internet of Things (IoT) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiableIoTservices. Considering the fact that the capacities ofIoTdevices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieveIoTservice monitoring at runtime. Specifically, we formulate the problem ofIoTservice monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based onSignalTemporalLogic (STL), where a novel accumulativerobustnessmetric is proposed, denotedAccumulativeSTL(ASTL), to emphasize the robust satisfaction over the entire time domain. Thereafter,IoTservice monitoring is converted toASTLformulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that ourASTLperforms better than the state-of-the-art's techniques with more robust satisfaction.

With the development of ICT (information and communications technology) and service economy, service ecosystem is emerging in a lot of fields, including E-commerce, O2O(Online To Offline) life service, healthcare service, cloud manufacturing, and so on. As a complex socio-technical system, the evolution of service ecosystem is the joint result of the interaction of the three heterogeneous networks, including social network, service network and value network. Under such circumstances, the traditional SOA (Service Oriented Architecture)-based analysis model is powerless. As a result, how to analyze the laws behind the evolution of service ecosystem is still a serious challenge in the field. This paper proposes a value oriented analysis framework (VOA) of service ecosystem, which can use value as a clue to describe the interaction of the three heterogeneous networks. In addition, a computational experiment system is established to verify the effectiveness of the VOA framework, which stimulates the effect of different intervention strategies on service ecosystem. The result shows that our analysis framework can provide new means and ideas for the analysis of service ecosystem.

With the recent adoption of edge computing,Internet ofThings (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting,IoTdevices are typically encapsulated asIoTservices to encode their functionalities, and their collaboration is achieved throughIoTservice composition. Due to the continuous resource occupancy, release, and consumption ofIoTdevices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certainIoTservices may significantly downgrade in theirQuality-of-Services (QoS). To guarantee the compatibility of compositions withQoSvariations, this article proposes an adaptive composition mechanism leveragingComputationTreeLogic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it toCTLformulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted byCTLsemantics during the execution of compositions. Besides, we construct aQoSDependencyGraph (QoSDG) to captureQoSvariations, and achieve adaptive composition with dynamicQoSsatisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higherQoSdependencies ranging from 0.3$\%$to 27.8$\%$.

Recommendation systems play a crucial part in helping users efficiently obtain information based on users’ current preferences and discover their individual needs, but the existing works are deficient in terms of the evolution of users’ interests. In this paper, Graph Embedding with Service and User information (GESU) model is proposed to address the limitations of capturing users’ fresh interests. Graph-structured data derived from time-varying session sequences are captured via gated graph neural networks. Then, the evolving influence of different services for users is obtained through a multi-head module. At the same time, a graph attention network is applied to predict users’ fresh consumption preferences by selecting representative friends to characterize user information. Extensive experiments on three datasets show that the proposed model outperforms state-of-the-art methods consistently on various evaluation metrics. GESU provides a means to recommend services that meet current requirements in an environment where users’ interests evolve dynamically.

The incompleteness of android documentation causes the lack of permission semantics, which in turn leads to permission misuse and threatens android system security. Previous studies have used API information to supplement permission semantics and recommended permissions, but they still lack sufficient contextual information, e.g. category and user review grades. To solve this problem, a novel Knowledge Graph based Convolutional Propagation Model (KGCP) is proposed for apps permission recommendation. In KGCP, we construct a knowledge graph (KG) to model the contextual information corresponding to apps and permissions. In order to regularize the representations of items, KGCP utilizes KG embedding technique to preserve its intrinsic structure while embedding entities and relationships into a continuous vector space. Focused on apps permission recommendation, KGCP learns the representation of entity through graph convolutional networks, which recursively aggregates information about neighbors in KG to mine potential preferences for app entities over permission entities. Experimental results show that KGCP improves by 16.7% over state-of-the-art apps permission recommendation methods, thus helps developers find the suitable permissions faster and more accurately.

With the service-oriented encapsulation of Internet of Things (IoT) devices, IoT services, which are functionally compatible and non-functionally satisfiable, are composed to support domain applications. The execution of IoT services may last for a relatively long time duration in which their capacities may vary significantly. In this setting, whether or not, and to what extent, certain constraints specified upon certain IoT services can always be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to conduct IoT service monitoring at runtime. Specifically, the requirement of IoT service monitoring is formulated as a constraint satisfaction problem. Specification-based monitoring is developed leveraging Signal Temporal Logic (STL), where a novel accumulative robustness metric, denoted Accumulative STL (ASTL), is proposed to emphasize the robust satisfaction over the entire time domain. Hence, an ASTL-based mechanism is proposed to support IoT service monitoring, where prescribed constraints are converted to ASTL formulae, and interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets. Experimental results show that ASTL performs better than the state-of-the-art techniques with more robust satisfaction.

The edge–cloud collaboration network is promising to support complex requirements with temporal constraints, where a requirement can be achieved through the composition of computation-demanding and delay-sensitive services. In this setting, most services should be optimally configured at the network edge, in order to decrease service response latency and reducing network resource consumption. To address this challenge, this article proposes an optimal service configuration mechanism, where temporal constraints between services are mined from event logs through our temporal interval discovery mechanism. Service configuration is formulated as a constrained multiobjective optimization problem, which is solved by our improved nondominated sorting geneticalgorithm II. Extensive experiments are conducted, and evaluation results demonstrate that our approach can find the close-to-optimal service configuration in comparison with the state-of-the-art techniques in terms of delay sensitivity and energy efficiency, especially when edge nodes can co-host a relatively large number of services.

Computational experiments are an important method for carrying out the quantitative analysis of complex systems and play a major role in mapping the real world to the virtual world. However, the flexibility of computational experiments leads to arbitrary modeling processes and unconvincing results, which greatly hinder the large-scale application of this method. In this context, the verification of computational models has become an urgent problem in this field. Currently, model evaluation is still in its infancy and the existing evaluation methods are not mature enough. Thus, we took epidemic models as the research object and proposed a capability maturity evaluation framework for computational models of artificial society. The framework differs from previous assessment methods that focus on the validity of results, but instead provides a comprehensive evaluation from two perspectives: 1) evaluation of the model itself—by comparing the expectation with the final implementation, we can obtain whether the model meets the expectation and 2) comparison between different models—by evaluating the implementation process of each model and comparing the results, we can identify more mature models. The implementation of the model is evaluated from input, process, and output. Further, specific analyses and evaluations are conducted for several representative COVID-19 models to verify the validity of this evaluation framework. The results of the case study show that the proposed evaluation framework can help decision-makers identify more mature and referential models, and point out the directions where modelers can improve their models.

Computational experiments have emerged as a new method for quantitative analysis of complex social systems. It has been applied to many interdisciplinary research fields, such as economics, finance, and epidemiology. Though the representation form of computational experiments is relatively flexible, the real system is more complex. Therefore, it is important to seek a balance between the flexibility of computational modeling and the credibility of conclusion. This article proposes a customized design framework for computational experiment models, so as to meet the diverse application demands of computational experiments in different fields. Finally, this article outlines some typical applications of computational experiments to provide a roadmap for its rapid development and widespread application.

Edge computing promises to facilitate the collaboration of smart sensors at the network edge, in order to satisfy the delay constraints of certain requests, and decrease the transmission of large-volume sensory data from the edge to the cloud. Generally, the functionalities provided by smart sensors are encapsulated as services, and the satisfaction of certain requests is reduced to the composition of services configured upon smart sensors in edge networks. Considering the dynamics and nonpredictability of incoming requests, an adaptive and online service configuration mechanism is essential, especially when various temporal constraints are prescribed by requests and satisfied by configured services. In this article, we formulate this problem in terms of a continuous-time Markov decision process model based on the state–action–reward mechanism. A temporal-difference learning approach is developed to optimize the service configuration while taking long-term delay sensitivity and energy efficiency into consideration. Extensive experiments are conducted, and evaluation results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configuration, and improving the temporal satisfaction of user requests.

As a new form of manufacturing in the industrial Internet era, cloud manufacturing service ecosystem (CMSE) can meet complex customization needs through a dynamic collaborative network between cloud manufacturing services. The source of cloud manufacturing service is social, and such sociality aggravates the uncertainty and dynamics of CMSE. This poses new challenges to the analysis of CMSE's evolution. The existing model social learning evolution (SLE) model only analyzes manufacturing service ecosystem from the perspective of individuals and lacks research on the organizational structure among individuals. In this article, we propose the improved model (SLE2) from a system perspective, which reconstructed the three layers of the SLE model: the individual layer describes the learning and evolution characteristics of service agents; the organization layer describes the competition and cooperation among service agents; and the social layer describes the value-driven social network operation mode. Finally, the article verifies that the SLE2 model is effective through computational experimental results.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

Trans-boundary has become not only a trend but also an important requirement and feature of Modern Service Industry. In Internet era, crossover service not only changes people's daily life, but also has a profound impact on the business model of traditional industry. However, because of the complexity and convergence of crossover service, it is difficult to analyze and identify whether crossover services are valuable and how to achieve them. In order to solve the problems, the analysis framework of crossover service is proposed from the perspective of value creation and realization, which includes four main parts: value assessment, data analysis, capability analysis, and execution path. Based on the proposed method, the New Retail in China is given as a case study to clarify the implementation process of crossover service. Furthermore, JD (self-employed mode) and Alibaba (platform mode) are taken as examples to explain how to migrate from different e-commerce models to the New Retail model. By introducing computational experiment, the performance of different execution paths can be evaluated. The comparison of experimental results with the reality shows that the proposed method can provide a roadmap for the implementation of crossover service.

With the rise of crossover services, service providers usually cooperate with each other on different platforms to expand their service value. However, in cross-platform cooperation, insufficient understanding and malicious competition between different platforms would lead to inaccurate trust establishment and unreliable trust recommendations. In this paper, we propose a trust management framework of cross-platform based on blockchain to establish, store and recommend trust securely for cross-platform cooperation. Firstly, we take into account the contextual background information to enhance interaction and understanding between platforms to achieve accurate trust establishment. Secondly, the trust recommendation algorithm is written into the blockchain in the form of smart contracts, which can ensure the security of trust recommendation. Finally, experiments are used to demonstrate the superiority and reliability of the framework.

Trust prediction is gaining significant interest since it could reduce the burden of user decision-makings effectively in various social activities. Existing works on trust prediction mainly based on trust networks, however, usually give little consideration to data sparsity and temporal continuity of user behavior. In order to solve these problems, we propose a comprehensive deep MemTrust model for trust prediction. With this model, we introduce a embedding layer to extend the feature space and alleviate the distinctive information oblivion caused by data sparsity. In addition, Long Short-Term Memory(LSTM) network is utilized to extract overall time series features through the multiple time slices of user features. Finally, the trust is estimated by pairwise time series features of users. Extensive experiments are validated on two real datasets, which demonstrate that the proposed model has superior performance compared with representative baseline approaches.

With the development of Online to Offline (O2O) model and the rapid growth of service types and numbers, service matching algorithms have become the key in connecting users and services. The traditional service matching algorithms lack consideration for the limited resources of O2O services, leading to the Matthew effect more seriously. In this context, how to alleviate the Matthew effect through the optimization of matching algorithms has become an urgent problem in this field. Based on this, this paper proposes an adaptive optimization algorithm of O2O service matching to achieve the balance of supply and demand by optimizing supply, thus alleviating the Matthew effect. In addition, a computational experiment system is constructed to verify the effect of different matching algorithms on alleviating the Matthew effect. The result shows that our proposed algorithm can provide new means and ideas for alleviating the Matthew effect.

The collaboration of contiguous Internet of Things (IoT) devices is envisioned to satisfy complex applications which are beyond the capacity of single devices. The functionalities of IoT devices are encapsulated as IoT services, and their collaboration is implemented in terms of IoT service composition. Considering the capacity occupancy, release, and consumption caused by the implementation of IoT services, their composition is challenging in capacity-dynamically fluctuating IoT networks. This paper proposes a dynamic IoT service composition mechanism with inter-service dependencies adopted to capture the dynamic changes of IoT devices, and this change is specified by various Quality-of-Service factors. IoT service composition is formalized under Computation Tree Logic specification with certain composite structures and dynamic dependencies, and this composition is formally achieved by an optimized model checking method. Extensive experiments are conducted on publicly available datasets, and evaluation results show that our technique outperforms the state-of-the-art's approaches in relevant performance metrics.

The prosperous development of Internet services such as O2O, IoT, and Web API has brought new vitality to service commercial platforms. However, these services involve online and offline business, which are widely diverse without a unified design and development standard. In addition, Internet services update frequently, which leads to repeat releases on commercial platforms. Therefore, in this paper, we present a generic method to rapidly release Internet services on commercial platforms. The method uses a highly abstract metamodel to express service business extensively and realizes service functions by executing metamodel objects. This method has wide versatility. Meanwhile, it extends the DevOps theory to solve the frequent changes of service functions during use after the release. Finally, we verified the usability of this method in the elderly healthcare domain.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. As shown in Fig.1 , the value creation of service ecosystem consists of three elements: Input, Output, and Operation. Input means customers’ value demands, which drives the constant evolution of service ecosystem. Output means the value created by the service ecosystem in a certain period of time. Operation means the value creation ability of service ecosystem.

An energy-efficient anomaly detection is fundamental to maintain a healthy status of domain applications in edge-cloud collaboration networks. Generally, various kinds of multimodal sensory data capture heterogeneous attributes, where a certain attribute, called the primary one, may be more significant in detecting certain anomaly. This observation drives us to propose a novel energy-efficient anomaly detection mechanism, where attributes sensed by multimodal smart things (msts) are categorized as primary and secondary ones according to their relevance with the characteristic of this anomaly. This technique includes two steps: 1) an initial anomaly detection in single edge networks. Edge nodes associated with the primary attribute adopt a lightweight object detection model to initially detect the potential occurrence of this anomaly. Certain edge networks are determined where an anomaly is suspected and 2) an anomaly refinement with multimodal and multiattribute smart things in marginal edge networks. The cloud identifies and issues a specific query request to gather anomaly-aware sensory data from smart things with secondary attributes, for refining the detection accuracy of this anomaly, where an adaptive weighted fusion model is developed to analyze sensory data coupling of msts. The experimental results show that this technique performs better than the state of the art on the reduction of energy consumption and query time.

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users’ experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

With the development of cloud manufacturing technology, Manufacturing Service Ecosystem (MSE) is emerging as a typical complex cyber-social system. On the one hand, service strategy (cyber layer) drives the evolution of manufacturing community (social layer); on the other hand, the initial conditions of manufacturing community (social layer) affect the performance of service strategy. In order to promote the evolution of MSE in the expected direction, it is necessary to clarify the loop feedback mechanism between heterogeneous networks. However, how to analyze and intervene in the possible evolution directions of MSE has become a serious challenge in the field. In order to face this challenge, this paper proposes a parallel system theory-based research framework to study the evolution and controlling of MSE. First, the corresponding digital system of MSE is constructed from the perspective of supply and demand matching. Second, the specific computational experiment is executed to present the effect of different service strategies (cyber layer) and different initial conditions (social layer) on the evolution of MSE. Furthermore, the comparison of experiment results with real data verifies the credibility of the proposed approach. It demonstrates that our approach can provide a new way for analyzing the complexity of MSE.

IoT search engines have attracted increasing attention from both academia and industry, since they are capable of crawling heterogeneous data sources in highly dynamic environment. To process tens of thousands of spatial-temporal-keyword queries per second, query efficiency and communication cost in IoT search engines become critical issues. To address these challenges, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighboring activity regions, is proposed in this paper. Thereafter, frequent query results can be achieved quickly leveraging the spatial-temporal-keyword filtering index of n-hop neighbor regions through modeling keywords relevance and uncertain traveling time. Besides, we adopt STK-tree proposed previously to directly answer non-frequent queries. Extensive experiments on real-life dataset demonstrate that our method outperforms the state-of-the-art's techniques in terms of the reduction of the query time and the number of transmitted messages.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.

With the Internet of Things (IoT) becoming the infrastructure to support domain applications, IoT search engines have attracted increasing attention from users, industry, and research community, since they are capable of crawling heterogeneous data sources in a highly dynamic environment. IoT search engines have to be able to process tens of thousands of spatial-time-keyword queries per second, making query throughput a critical issue. To achieve this heavy workload, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n -hop neighbor activity regions, is first proposed in this article. With our design, the frequent query result can be gained quickly from the spatial-time-keyword filtering index of n -hop neighbor regions by modeling keywords relevance and uncertain traveling time. In addition, we use STK-tree proposed previously to directly answer nonfrequent queries. Extensive experiments on real-life and synthetic data sets demonstrate that our proposed method outperforms the state-of-the-art approaches with respect to query time and message number.

Crossover cooperation and fusion between services is becoming very common in the modern service industry. Crossover service fusion can create value that cannot be provided by single-domain services, thus achieving the value-emergence effect of 1+1>2. However, semantic inconsistencies in business and interface make crossover service fusion difficult and time-consuming. This paper proposes an interactive crossover service fusion approach based on microservice architecture to enable smooth and rapid integration of domain services. This approach takes service fusion requirements as the driving force to detect business inconsistencies between the services to be fused, and carries out business process reengineering by human-computer interaction. Then, semantic inconsistencies in service interface matching are detected and solved by splitting and completing parameter concepts to obtain the service fusion design scheme. Finally, the implementation scheme based on microservice architecture transforms the business coupling between domain services into asynchronous data communication, which facilitates the crossover fusion of complex business services. The elderly healthcare application is used to demonstrate and validate our approach.

In the context of big data, cloud storage services provide users with the ideal data storage service. But external store of data causes cloud storage service providers gain control of the data. Therefore, our work should consider how to ensure the privacy of data and maintain the integrity of data when enjoying convenient services. This paper builds a blockchain-based solution through research on cloud storage service model and blockchain technology. And related protocols are built on the solution-based architecture. In our solution, the decentralized model solves the single point of trust problem in the traditional data auditing service model by collective trust. A public agreement enables auditors to efficiently build proof of data integrity without touching data. The protocol allows users to trace the history of their data, and examine whether the owner of the data guarantees the privacy of the data in an after-the-fact audit. In addition, our work constructed the prototype system of the scheme and revealed the effectiveness of the scheme through system testing.

Cloud computing, which is claimed to provide plentiful storage, computational, and other resources, has become a promising platform to support resource-intensive applications. Due to the wide adoption of smart things to support domain applications and considering the delay-sensitivity of certain requests and limited network capacity compared with huge data packets to be transmitted, the quality of experience (QoE) may be hard to be satisfied when requests are solely supported by cloud computing. In this setting, edge computing has become an infrastructure to facilitate request satisfaction at the network edge. This article proposes a mechanism to optimize the collaboration of heterogeneous edge servers with certain QoE constraints. Specifically, concurrent requests, which are usually represented in terms of SQL queries, are rewritten as atomic queries, and these atomic queries are optimally assigned to edge servers through adopting an algorithm inspired by the minimum spanning tree, where QoE factors, including the delay, size of data packets, and number of operators, are considered. Evaluation results indicate that the proposed mechanism can effectively improve the QoE of requests compared with the state-of-the-art's mechanisms.

As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between supply-side and demand-side. In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing. Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena. In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model. Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing. The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework. The results demonstrate that our method has a substantial promise.

O2O (online to offline) service recommendation is a typical context-aware service application, which needs to provide the most suitable services to customers in time according to their user profile and current context. By means of composing various data sources, different O2O service recommendation strategies can be customized, which may lead to great performance difference. Incorrect or non-real-time service recommendation would not work well and even cause negatives consequences. As a result, how to evaluate the performance of different O2O service recommendation strategies and select the most suitable one has become a key problem in the field. Due to the diversity and the variability of context events, as well as the economic, legal, and ethical impact, it is difficult or even impossible for traditional methods to realize comprehensive evaluation of various service strategies. Based on the background, this paper proposes a computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O2O service strategies, modeling of experiment system, and execution of experiment evaluation. As a case study, the method was applied to Food O2O service. Three kinds of service strategies were compared respectively under two different market environments. Experiment results show that the proposed evaluation method is effective.

Transboundary has become not only a trend but also an important requirement and feature of modern service industry development. In the Internet era, transboundary service not only changed people's daily life, but also had a profound impact on the business model of traditional industry. China has proposed the “Internet+” development plan, hoping to use the Internet for the transformation of traditional industries, including retail, manufacturing, catering, agriculture, finance, and health care. However, the transboundary impact of Internet on the operating model of traditional industry is decided by a combination of a variety of factors. Currently, the appropriate assessment model and analysis methods are absent in this field. Based on this, the impact evaluation method of Internet (service bridge) is proposed from the prospect of supply and demand matching, which includes three main parts: the capability model of supply side, the characteristic model of demand side, and the service bridge model. Based on the proposed method, the corresponding computational experiment system is built to evaluate the impact of Internet mode in different industries. Finally, this paper verifies the method with actual cases, and compares the transboundary impact of Internet in different daily consumption industries (online to offline in beauty service and take-out food service industry). The results showed that the “service bridge” method can introduce a new idea for the transboundary impact evaluation of Internet, which can provide some decision support to the reconstruction of demand value chain in some traditional industries.

In the framework of Industry 4.0, collaborative manufacturing across different supply chains is one of the most important business models. In order to avoid the uneven distribution of service requirements among service providers (i.e., non-equalization phenomenon), a lot of service strategies with different characteristics can be taken as candidate solutions to adjust the matching between service providers and service consumers. Based on the background, how to identify the application conditions of various service strategies in complex environment has become a serious challenge in the field. To solve this problem, the computational experiment-based evaluation method is proposed in this paper, including customization of service strategy, construction of experiment system, and experiment analysis of service strategy. In this paper, three possible service strategies are built to deal with the non-equalization phenomenon, i.e., non-equalization strategy, equalization strategy, collaborative equalization strategy. Experiment results show that: collaborative equalization strategy can effectively enhance the service utilization rate and reduce the completion time in short supply environment; equalization strategy is the optimal one in oversupply market environment. This case study can show that the proposed method is feasible and the result is satisfactory.