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From Big Data to Big Service
Abstract
Big service-the convergence and collaboration of big data systems-presents a solid solution to the challenges brought by big data.
... Also, services have become increasingly interconnected to facilitate transboundary collaboration for creating and delivering unique new values to customers. Many researchers have focused on this new phenomenon and introduced various new terms for it, such as "Internet of Services" [1], "Big Services" [2], and "Crossover Services" [3]. All of these terms are used to describe the complicated service ecosystem phenomena but with different theoretical focuses. ...
... We use the cross-entropy loss function for this module. 2 can be calculated as follows: ...
... In addition to MSEM specifications, a data-driven approach for MSEM construction is introduced. This method overcomes the shortcomings of traditional methods in building large-scale service ecosystems in two ways: (1) Numerous news corpora are continuously collected, and service events are extracted from these massive unstructured texts so that rich real-world data can be used; (2) high-quality open source KGs and external data sources are also utilized to enrich MSEM with more information. ...
Services are flourishing drastically both on the Internet and in the real world. In addition, services have become much more interconnected to facilitate transboundary business collaboration to create and deliver distinct new values to customers. Various service ecosystems come into being and are increasingly becoming a focus in both research and practice. However, due to the lack of widely recognized service ecosystem models and sufficient real data for constructing such models, existing studies on service ecosystems are limited to a very narrow scope and cannot effectively guide the design, optimization, and evolution of service ecosystems. In this paper, we first propose a multilayer network-based service ecosystem model (MSEM), which covers a variety of service-related elements, including stakeholders, channels, functional and nonfunctional features, and domains, and more importantly, structural and evolutionary relations between them. “Events” are introduced to describe the triggers of service ecosystem evolution. Then, we propose a data-driven approach for constructing MSEM from public media news and external data sources. Experiments conducted on real news corpora show that compared with other approaches, our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.
... The rise of big data on cloud computing environments has led to the emergence of big services as collections of collaborating and interrelated services for handling and dealing with big data (see Fig. 3). This kind of services is considered "big" in terms of functionalities and data processing capabilities, as well as their ability to execute across, not only different layers, but also different domains [3]. ...
... In [23], big services are defined as online services that manage and access a huge amount of data. While in [3], big services are seen as an aggregation of several cross-domain services that access and process a large amount of data. Authors, in [24] and [25], define big services as a massive ambiguous series of services centered on big data. ...
... Xu et al. [3] proposed a generic reference architecture for big service (see Fig. 4). This architecture has three main layers (local services, domain-oriented services, and demand-oriented service solutions) and two additional layers (the cloud infrastructure at the bottom and the client business at the top). ...
Over the last years, cloud computing has emerged as a natural choice to host, manage, and provide various kinds of virtualized resources (e.g., software, business processes, databases, platforms, mobile and social applications, etc.) as on-demand services. This “servicelization” across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. This latter consists of the encapsulation, abstraction and the processing of big data, allowing then to hide their complexity. However, this promising approach still lacks management facilities and tools. Indeed, due to the highly dynamic and uncertain nature of their hosting cloud environments, big services together with their accessed data need continuous management operations, so that to maintain a moderate state and high quality of their execution. In this context, frameworks for designing, composing, executing and managing big services become a major need. The purpose of this paper is to provide an understanding of the new emerging big service model from the lifecycle management phases’ point of view. We also study the role of big data frameworks and multi-cloud strategies in the provisioning of big services. A research road map on this topic will be summarized at the end of this paper.
... In addition, cloud computing as a natural environment for hosting and processing big data is considered as one of the most powerful modern technologies to process complex and massive computing tasks, and to allow its users to benefit from various types of virtualised services. With the synergy between cloud computing and big data, a new type of service called big service has emerged to allow users process and deal with a huge volume of data (Xu et al., 2015). This heterogeneous and large-scale service ecosystem is a combination of a huge number of various services, including cloud, mobile, web and mashup services, which are hosted on multiple cloud availability zones (Xu et al., 2015). ...
... With the synergy between cloud computing and big data, a new type of service called big service has emerged to allow users process and deal with a huge volume of data (Xu et al., 2015). This heterogeneous and large-scale service ecosystem is a combination of a huge number of various services, including cloud, mobile, web and mashup services, which are hosted on multiple cloud availability zones (Xu et al., 2015). Big services have received a particular attention due to the increasing number of generated data that need to be abstracted and processed by big services, which helps hiding their complexity and heterogeneity. ...
... Like these latter, big services are composed by filtering the available ones based on their functional, QoS, and contextual capabilities. However, given the seven characteristics of big services (complexity, massiveness, heterogeneity, value, customer focus, credibility, and convergence) (Xu et al., 2015), the big service composition process must be refined by considering additional constraints. We give as examples, the provenance and the quality (QoD) of data sources consumed by the composed big service, the adopted acquisition model and provider's policies, the cross-domain correlations between services and between data sources, etc. ...
... Under the dual effect of the continuous evolution of the software ecosystem and the rapid development of the modern service industry, software ecosystems have gradually evolved into service ecosystems. In 2016, Wu and Deng first used crossover service to describe the cross-border integration service model in the modern service industry, which is featured by crossover, convergence, and complexity [38,39] . Xu et al. [40,41] proposed the concept of "big service", coping with the challenges brought about by big data through the integration and collaboration of a large number of services in multiple fields. ...
... For example, as the service provider in the same group, Taobao wants users to spend more money on online shopping, whereas Alipay wants users to save money and deposit more money in it. How to solve these contradictions and weigh various value elements to create a greater value is the challenge that performance analysis faces [39] . ...
With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development.
... Hereby, the actors and resources in the value chain need to be integrated to meet the massive individualized needs (Xu et al., 2018). Cloud computing provides the access mode of service resources, while big service provides the construction method of complex services (Xu et al., 2015). The PSS is a relatively new field, and theories such as cloud computing and big services can be used to enrich its resource access modes and service compositions, while the use of resources is inseparable from each participant in the operation system of the PSS. ...
... The massive amount of data generated by the current development of technology is both a challenge and an opportunity for enterprises. The collaborative and integrated ecosystem named big service can more effectively address these challenges (Xu et al., 2015). ...
With the evolution of product service systems, as well as the proposal and wide application of cloud computing and big services, more and more manufacturing enterprises are turning from being product oriented to service oriented. A difficult aspect is that with the growth of customer scale and the personalization of customer service needs, service providers cannot rely on their own resources to fulfill services. Moreover, meeting the needs of customers often requires a complete and complex service. Therefore, the service provider has to provide cross-enterprise collaborative services and coordinate the resources of all participants in the product service system to complete the services together. This research proposes a novel resource allocation method for product service systems that adopt the bilateral resource integration service mode and considers the service process life cycle. Based on the process mining techniques, this method extracts knowledge from the execution event log of the service process stored in the enterprise information system, constructs the resource allocation problem model, and gives the process mining-based resource allocation algorithm (PMRA). We use an air conditioner repair service as a case to verify the method proposed in this study. The contribution of this study is to propose a new method of resource allocation for cross-enterprise product−service processes based on process mining techniques, which takes into account empirical knowledge from historical data and can provide a new idea for service optimization of product service systems.
... The service needs of users using Internet-based services tend to be more coarse-grained and more personalized, and it is difficult for a single service to satisfy the vast range of user needs, which typically requires integration of multiple services [3]. In addition, services on the Internet are not entirely independent and are closely connected [4]. ...
... With the explosive growth of information on the web, web pages accommodate a large amount of unstructured information [28]. Unlike ordinary text, web pages contain textual information and other significant factors, e.g., structural fea- • Web Text: the semantic information of the entity • Web page structure Position: the label structure features of the extracted text position ['html', 'head', 'body', 'div', 'div', 'div',…, div', 'div', 'ul', 'li', 'div', 'a' ] • Title of the page: the semantic information of the title of the page where the target entity is located title • The Keywords of the web page: the semantic information of the keywords in the web page name = "keywords" • Web Context: semantic information about the context in which the text is located, or some important information that displays features near the text According to the fields in some service participant web data, e.g., Xiaomi Youpin, 3 Taobao, and Ctrip, and the types of service participants involved, the initial manually constructed example of the tag libraries for the service participant roles and types is described as Fig. 5. ...
Various emerging IT technologies are widely used in the service industry. Thus, an increasing number of new service models have also emerged, including the Internet of Services (IoS). The IoS supports network-based service collaboration and transactions among various service participants from different domains and different organizations, and it is expected to deliver the maximum service value to all stakeholders. To describe the cross-domain, cross-organization, and cross-value chain characteristics of the IoS from a value perspective and support subsequent analysis of the value network and optimization of the IoS, this paper proposes a semi-automatic modeling method for a IoS-oriented value network based on external public data. We first propose an intelligent domain entity recognition algorithm based on multidimensional web data to help value network modelers realize effective and efficient recognition of service participants. Then, based on external news data, an intelligent domain relationship extraction algorithm that combines the Bert + BiLSTM + CRF model with the LightGBM model is proposed to effectively and efficiently identify the value exchange relationships among service participants, thereby forming an IoS-oriented value network model (IVN). Finally, to extend the cross-domain semantics of the IVN and support analysis of the IVN, we present a domain-specific value chain extraction algorithm based on typical patterns to complete the cross-domain semantic annotation of the IVN. The effectiveness and efficiency of the proposed methods and algorithms are validated through experimental analysis and a case study, which can be of great help in IVN modeling.
... Accelerated by phenomenal development, several newly emerged concepts such as Big Service [32], Internet of Services (IoS) [15], and increasingly more Web services have been published onto the Internet recently. Such Web services and various objects (e.g., Web service providers and users) related to them gradually form a knowledge network, i.e., a Web service ecosystem [11,28], through a variety of complex correlations. ...
Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.
... Los gobiernos a lo largo y ancho del mundo han adoptado una gran diversidad de nuevas tecnologías para mejorar la calidad de sus servicios, incrementar la eficiencia organizacional y generar valor público (Criado y Gil-García, 2019;Gil-García et al., 2014;Valle-Cruz, 2019). Los esfuerzos realizados comienzan desde la puesta en marcha de portales de gobierno y estrategias de redes sociales para acercarse al ciudadano, hasta la implementación de servicios digitales transaccionales, como el pago de impuestos y la atención ciudadana basada en técnicas de inteligencia artificial, como chatbots y la explotación de macrodatos (Big Data) (Löfgren y Webster, 2020;Xu et al., 2015). Es así como las formas organizacionales tradicionales en los gobiernos han cambiado y se han adoptado artefactos tecnológicos que potencian la eficiencia en el gobierno digital (Fernández y Rainey, 2006;Seneviratne, 1999). ...
Las tecnologías emergentes tienen el potencial de transformar la administración pública de una forma inimaginada. En este sentido, este documento se enfoca en analizar a las tecnologías emergentes en gobiernos locales usando la metodología PRISMA. Las preguntas que guían la investigación son: ¿cuáles son las tecnologías emergentes utilizadas por los gobiernos locales? Y ¿cuáles son los retos y consecuencias del uso de las tecnologías emergentes en los gobiernos locales? Los hallazgos muestran tres tipos de tecnologías emergentes: 1) Básicas (como la tecnología móvil, la Web 2.0, las páginas web y las TIC), 2) De vanguardia (como Blockchain, inteligencia artificial, macrodatos (Big Data) e Internet de las cosas) y 3) Específicas y aplicadas a las ciudades inteligentes, agricultura urbana, conciencia ambiental y telesalud. Aunque la implementación de tecnologías emergentes puede resultar en beneficios para el sector público, uno de los retos consiste en acortar la brecha entre desarrolladores de tecnología y tomadores de decisiones. Asimismo, la inescrutable condición de algunos algoritmos y la capacidad de vigilancia masiva de algunas tecnologías emergentes amenazan la libertad de las sociedades y pueden deshumanizar algunos procesos en el sector público.
... Services have become much more interconnected and have formed various service ecosystems. A variety of terms, such as "Internet of Services," [26] "Smart Planet," 1 and "Big Services" [31] have been used to distinguish between service ecosystems with different theoretical focuses. Service ecosystems keep evolving over time, which is exhibited in the forms of the emergence, prosperity, and decline of individual services and their relations. ...
Services are flourishing dramatically, and continuously increasing interactions among them are resulting in a new phenomenon called “service ecosystems,” which has become a focus of academia and industry. Driven by technology innovation, changes in regulations, and changes in the competitive strategies of individual businesses, service ecosystems are constantly evolving. Service ecosystem evolution analysis is an emerging research problem of great significance. By analyzing service ecosystem evolution history, common evolution patterns can be identified, underlying driving forces can be discovered, and future evolution trends can be predicated so that service providers can adjust their competitive strategies in a timely manner to adapt to evolution trends. In this paper, a framework for identifying service ecosystem evolution patterns from the service community perspective is presented. First, following the approaches of community detection and community evolution analysis, time-series community evolution traces are identified from historical service ecosystem evolution, and a service community evolution prediction model is trained in accordance with such traces. Second, the prediction model is explained to show how different factors affect the evolution of service communities. Finally, an approach for assisting service providers in making business decisions is presented according to interpretable prediction results and prior domain knowledge. Experiments on a real-world dataset showed that this work can indeed provide business-level insights on service ecosystem evolution. Additionally, all the data and source code have been made fully open-source for service ecosystem researchers.
... The products range from NoSQL databases (e.g., "MongoDB", "Cassandra"), to parallel processing structures (e.g. "Hadoop", "Spark", "Storm"), workflow management and execution architectures (e.g., "Apache Oozie", "Azkaban", "Luigi"), and visualization frameworks (like "Tableau", "FusionCharts", "Sisense") [46,47]. To select the best suitable product based on the requirement is often difficult and also the configuration and deployment of all the products is a tedious task. ...
Current era is witnessing data explosion being generated from a wide range of resources including RFID (Radio-frequency identification), sensors, web logs, social media, IoT (Internet of Things) devices and many more. Pace at which data is being generated routinely in all the task performed by us has overwhelmed the proficiency and working of present infrastructure and analytical solutions available. Data has become the driving force of economy and has been treated as an asset for an organization. It contains truth or facts that can be interpreted and manipulated to gain insight for knowledge discovery. To excel out in competition enterprises are escalating their big data projects for knowledge discovery to gain valuable insights. These projects require scalable architectures for storage and data processing. Data-centric technologies are gaining impetus which can be provisioned as service to the organizations. Cloud computing is an effective and promising solution for refined analytical application. Cloud computing model supports resources to be provisioned as service. Herein paper we examine the requirements for provisioning Big Data Knowledge Discovery as a service. In addition, we explore the prevalent big data frameworks accessible and provisioned as a service via cloud. We also explore the state-of-the- art progress in this arena with open challenges and research prospects.
... With the rapid development of the Internet, user requirements have become increasingly complex. It has become a trend for service providers to connect more external applications to their own platforms to satisfy these increasingly complex user requirements [39,40,19]. For example, WeChat and Alipay achieved this by Mini-program; Microsoft released Office 19 to enable users to access Word, Pow-erPoint and Excel in one app. ...
The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.
... Big services are defined as the interrelation of virtualized/physical cross-domain services to deal with a large volume of data. Big services' characteristics include massiveness, heterogeneity, complexity, value, customer focus, credibility and convergence [96]. Some of these latter are beyond the capacity of traditional placement schemes, which have been mainly designed for specific cloud service models. ...
Cloud computing is largely adopted by the current computing industry. Not only users can benefit from cloud scalability, but also businesses are more and more attracted by its flexibility. In addition, the number of offered cloud services (e.g., SaaS, BPaaS, mobile services, etc.) is continuously growing. This raises a question about how to effectively arrange and place them in the cloud, in order to offer high-performance services. Indeed, companies’ and providers’ benefits are strongly related to the optimal placement and management of cloud services, together with their related data. This produces various challenges, including the heterogeneity and dynamicity of hosting cloud zones, the cloud/service -specific placement constraints, etc. Recent cloud service placement approaches have dealt with these issues through different techniques, and by fixing various criteria to optimize. Moreover, researchers have considered other specificities, like the cloud environment type, the deployment model and the placement mode. This paper provides a comprehensive survey on service placement schemes in the cloud. We also identify the current challenges for different cloud service models and environments, and we provide our future directions.
... Zhang et al. 18 developed a mapreduce-based improved discrete particle swarm optimization (PSO) for QoS-aware large-scale service composition. Xu et al. 1 presented a novel approach based on mixed integer programming for QoS-aware Big service composition. Wu et al. 19 discussed an MapReduce-based skyline approach for QoS-aware web service composition. ...
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.
... The inability to store and analyze the newly generated and existing uncollected massive volume and variety of data is another reason for the failure of SFA systems [18]. However, BDA extracts customer opinions on products, services, and organizations by mining customer data from all possible sources, e.g., social media data, sensor network data, transactional data, and survey data, for decision making and has the ability to analyze massive amounts and varieties of data [19,20]. Therefore, based on prior literature, this study recognizes that BDA can overcome some shortcomings of SFA, which is the motivation to do this research. ...
In this era of technology development, every business wants to equip its salesforce with a sustainable salesforce automation system to improve sales performance and customer relationship management (CRM) capabilities. This study investigates the impact of big data analytics (BDA) on CRM capabilities and the sales performance of pharmaceutical organizations. A research model was tested based on 416 valid responses collected from pharmaceutical companies through a structured questionnaire. Structural equation model-ing (SEM) was employed using Smart-PLS3 to confirm the contribution of BDA to improving CRM capabilities and sales performance. The study finds that individual characteristics such as self-efficacy, playfulness, and social norms, along with organizational characteristics such as voluntariness, user involvement, user participation, and management support, are positive predictors of salesforce perception of BDA. This positive perception of BDA increased the person-technology fit in the salesforce, which ultimately increased the CRM capabilities and sales performance.
... It can also be viewed as networked services or systems across the real and virtual worlds over the Internet. In the IoS, services -as encapsulated functional entities containing interaction processes by service providers and customers -are redistributed, virtualised and converged over the Internet to meet the requirements of and create value for customers (Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S., 2015). An example of IoS is cloud computing. ...
Industry 4.0 is for most companies and especially for small and medium sized enterprises (SMEs) one of the major challenges after the wave of lean management. The aim of this chapter is to provide a methodological guidance for the practical use of the Industry 4.0 vision and principles in production system design in the specific context of SMEs. Based on the analysis of literature, a procedure model for the target-oriented introduction of Industry 4.0 principles in SMEs is proposed. A first practical evaluation of the approach is carried out based on two industrial case studies. The experiences made in the industrial cases show that Industry 4.0 is not limited to the application in large enterprises but is very suitable also for SME. This chapter contributes, with its case-study-based methodology, to the existing sparse knowledge on the introduction of Industry 4.0 in SME production systems.
... More and more software services are developed and deployed on the Internet, along with a huge number of virtualized services that connect real-world physical service resources. Services from multiple domains, multiple networks, and multiple worlds are converged as a huge complicated service network or ecosystem, which can be called as "Internet of Services (IoS)" [1] or "Big Service" [2]. IoS presents a paradigm in which everything is available as a service on the Internet. ...
Various types of services, such as web APIs, IoT services, O2O services, and many others, have flooded on the Internet. Interconnections among these services have resulted in a new phenomenon called "Internet of Services" (IoS). By IoS,people don't need to request multiple services by themselves to fulfill their daily requirements, but it is an IoS platform that is responsible for constructing integrated solutions for them. Since user requirements (URs) are usually coarse-grained and transboundary, IoS platforms have to integrate services from multiple domains to fulfill the requirements. Considering there are too many available services in IoS, a big challenge is how to look for a tradeoff between the construction efficiency and the precision of final solutions. For this challenge, we introduce a framework and a platform for transboundary user requirement oriented solution design in IoS. The main idea is to make use of domain priori knowledge derived from the commonness and similarities among massive historical URs and among historical integrated service solutions(ISSs). Priori knowledge is classified into three types: requirement patterns (RPs), service patterns (SPs), and probabilistic matching matrix (PMM) between RPs and SPs. A UR is modeled in the form of an intention tree (ITree) along with a set of constraints on intention nodes, and then optimal RPs are selected to cover the I-Tree as much as possible. By taking advantage of the PMM, a set of SPs are filtered out and composed together to form the final ISS. Finally, the design of a platform supporting the above process is introduced.
... Several works have been published to define how to structure and share data produced in a smart city [14]. Smart urban traffic ecosystems are identified in [15] as an example of a "big service", "evolved from the collection of collaborating, interrelated services for handling and dealing with big data". By collecting suitable sensor data and defining appropriate data exploitation strategies, it is possible to empower both citizens and decision-makers to improve our quality of life. ...
Modern cities face pressing problems with transportation systems including, but notlimited to, traffic congestion, safety, health, and pollution. To tackle them, public administrationshave implemented roadside infrastructures such as cameras and sensors to collect data aboutenvironmental and traffic conditions. In the case of traffic sensor data not only the real-time dataare essential, but also historical values need to be preserved and published. When real-time andhistorical data of smart cities become available, everyone can join an evidence-based debate on thecity’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) projectseeks to understand how traffic affects urban air quality. The project develops a platform to providereal-time and predicted values on air quality in several cities in Europe, encompassing tasks suchas the deployment of low-cost air quality sensors, data collection and integration, modeling andprediction, the publication of open data, and the development of applications for end-users andpublic administrations. This paper explicitly focuses on the modeling and semantic annotation oftraffic data. We present the tools and techniques used in the project and validate our strategies fordata modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain).An experimental evaluation shows that our approach to publish Linked Data is effective.data management; semantics; sensor data; data integration; data annotation; traffic insmart cities
... Trans-boundary services have become an important requirement and feature of the development of modern service industry. Xu et al. [9] proposed the concept of "big service" to express transboundary services, and pointed out the characteristics that big services should have. Wu S14 or S15.out: address a a a a address S16.in: delivery information (IIT_4) S17.out: instruction S11.in: usage ...
With the aging of the population and the development of modern service industries, the health service ecosystem (HSE) is beginning to emerge. As a new form of healthcare industry in the Internet era, HSE has its inherent “social cyber” complexity: the source of healthcare service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between diverse supply and personalized requirements for aged persons. In order to meet this challenge, it is necessary to conduct the trans-boundary cooperation and integration between service chains in different domains, including business convergence, interface convergence and value convergence. This task is very difficult and there is currently no common method. In order to change such a situation, this paper proposes an interactive trans-boundary convergence method for health service ecosystem based on micro-service architecture. Firstly, the method confirms the value of service convergence, and then uses this as a driving force to achieve business process integration of different service chains. Then, the business coupling between different service chains is converted into asynchronous data communication to achieve interface convergence. Finally, the value assessment of service convergence is given to realize the value convergence. This method has been verified in the construction of National healthcare service platform for the elderly in China. The results demonstrate that our method has a substantial promise.
... As described above, in order to realize business improvement and new services, the need for utilizing various data held by various business systems in railway companies is expanding [3], [4]. In order to solve the above, there is a growing expectation that the use of IoT technology and big data, which has been increasing in recent years, has been particularly active [5], [6]. However, because each railway company's business systems such as transportation, maintenance, and sales have been individually developed, it is difficult to utilize various data transversely. ...
Recently in the railway field and the industry field, it becomes more necessary to use data effectively in order to store and succeed to fields' know-how by information technology and in order to plan for further increase in business efficiency. However, there is a large amount of various data from plural different railway business systems, so it is difficult to utilize these data transversely. In order to solve the above problems, we propose a data utilization platform, especially including platform architecture, data relation generating/visualizing functions with data relation network model, and analysis components that can be reused in plural applications, for the railway field as the first instance.
In the era of a more advanced and intelligent Internet, the highly sophisticated service-oriented internet provides users with a diverse array of similar services. Accurate Quality of Service (QoS) prediction plays a pivotal role in helping users choose the optimal service from a multitude of available options. Traditional federated learning models offer a secure method for multiple clients to collaborate on QoS predictions. However, these models still employ a uniform approach that overlooks the unique requirements of individual clients. In order to meet the different needs of a wide range of customers for models, we propose an innovative personalized federated learning framework with layer-wised and neighbor-based aggregation for QoS prediction (pFedLN). In the proposed framework, we consider the privacy and functional disparities among layers in neural network models and employ diverse aggregation strategies for layers serving different functions. In addition, the similarity between neighbors will be taken into account during the aggregation process. This results in the creation of personalized models for each client that better align with their specific requirements. Sufficient experiments are conducted on a real-world dataset and the results indicate that our approach have a clear advantage in improving the effectiveness of personalization compared to existing approaches.
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.
Over the last few years, big data have emerged as a paradigm for processing and analyzing a large volume of data. Coupled with other paradigms, such as cloud computing, service computing, and Internet of Things, big data processing takes advantage of the underlying cloud infrastructure, which allows hosting and managing massive amounts of data, while service computing allows to process and deliver various data sources as on‐demand services. This synergy between multiple paradigms has led to the emergence of big services , as a cross‐domain, large‐scale, and big data‐centric service model. Apart from the adaptation issues (e.g., need of high reaction to changes) inherited from other service models, the massiveness and heterogeneity of big services add a new factor of complexity to the way such a large‐scale service ecosystem is managed in case of execution deviations. Indeed, big services are often subject to frequent deviations at both the functional (e.g., service failure, QoS degradation, and IoT resource unavailability) and data (e.g., data source unavailability or access restrictions) levels. Handling these execution problems is beyond the capacity of traditional web/cloud service management tools, and the majority of big service approaches have targeted specific management operations, such as selection and composition. To maintain a moderate state and high quality of their cross‐domain execution, big services should be continuously monitored and managed in a scalable and autonomous way. To cope with the absence of self‐management frameworks for large‐scale services, the goal of this work is to design an autonomic management solution that takes the whole control of big services in an autonomous and distributed lifecycle process. We combine autonomic computing and big data processing paradigms to endow big services with self‐ * and parallel processing capabilities. The proposed management framework takes advantage of the well‐known MapReduce programming model and Apache Spark and manages big service's related data using knowledge graph technology . We also define a scalable embedding model that allows processing and learning latent big service knowledge in a distributed manner. Finally, a cooperative decision mechanism is defined to trigger non‐conflicting management policies in response to the captured deviations of the running big service. Big services' management tasks (monitoring, embedding, and decision), as well as the core modules (autonomic managers' controller, embedding module, and coordinator), are implemented on top of Apache Spark as MapReduce jobs, while the processed data are represented as resilient distributed dataset (RDD) structures. To exploit the shared information exchanged between the workers and the master node (coordinator), and for further resolution of conflicts between management policies, we endowed the proposed framework with a lightweight communication mechanism that allows transferring useful knowledge between the running map‐reduce tasks and filtering inappropriate intermediate data (e.g., conflicting actions). The experimental results proved the increased quality of embeddings and the high performance of autonomic managers in a parallel and cooperative setting, thanks to the shared knowledge.
In recent years, the concept of Big Service has been proposed to describe the complex characteristics of cross-world, cross-domain, and cross-network services. The main goal of Big Service is to provide on-demand services to users. Accurately providing services on demand requires a full understanding of the characteristics of service participants. Portrait technology is a useful method for describing various attributes, behaviors, and preferences of service participants to provide personalized services. However, existing methods often overlook the macro spatio-temporal connections between service providers and their customers. To address this issue, this paper proposes a service portrait construction approach based on demand-service capacity spatio-temporal matching. The approach focuses on the characteristics of both customers and service providers and provides a method for constructing demand and service capacity distribution models. Additionally, the approach includes a method for analyzing the supply and demand matching condition. We illustrate the proposed approach through a ride-hailing service case. Analyzing the characteristics of service participants and the status of service provision from the perspective of time and space enables providers to allocate resources reasonably and achieve precise services.KeywordsService portraitSupply and demand matchingSptio-temporal characteristics
Recent years have witnessed the emergence of big services , as a large‐scale big data‐centric service model, that resulted from the synergy between powerful computing paradigms (big data processing, service and cloud computing, Internet of Things, etc.). Big services are seen as a heterogeneous combination of physical and virtualized domain‐specific resources, with a huge volume of data and complex functionalities, all encapsulated and offered as services. This complexity of big services (composition units' heterogeneity, cross‐domain orientation, data massiveness), coupled with other environmental factors (cloud dynamicity, providers' policies, customer requirements) makes their management tasks beyond humans' capability. Therefore, endowing big service ecosystems with self‐adaptive behavior is a natural solution. To achieve this goal, this article models big services as autonomic computing systems , and structures their behavioral aspects (functional behavior, quality of service/data levels, management policies) as a multi‐view knowledge graph . To infer useful knowledge (e.g., conflicts between policies) for the autonomic big service's management tasks, we process the big service's knowledge graph (BSKG) via a graph neural network ‐based graph embedding model. This latter is reinforced by an incremental learning method, that helps capturing the big services' frequent changes (e.g., QoS deviations, service failures, new policies), and drives autonomic managers to continuously update and enrich their knowledge w.r.t. the managed big service's current state. Finally, a flexible decision mechanism explores the BSKG structure and the latent knowledge, to locate and trigger the appropriate management policies, according to the big service's produced events.
The concepts “Big Service” and “Internet of Services (IoS)” arise along with the flourish of Internet-based service paradigm and services computing technologies. Business functionalities belonging to different organizations, regions or domains are encapsulated as services and publicized to outside world, and these services are further aggregated by public platforms, then complicated and flexible collaborations among services could be constructed for the fulfillment of personalized customer demands. Big Service and IoS have drastically fostered a new approach for business innovation and transformation of various types of industries. In this paper, we introduce the fundamental concepts and reference architecture of Big Service and IoS, then summarize the corresponding roadmap of business innovation and transformation, and analyze the intrinsic drivers of such roadmap. Referential development and execution environment of Big Service and IoS is presented, along with several candidate technological architecture for companies to adopt in different application scenarios.
With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.
Big services have recently emerged as a solution to process, encapsulate and offer huge volumes of data as a service. However, its management operations are beyond the ability of human administrators, due to several challenges including big services’ large-scale nature and complexity, the heterogeneity of its components, the dynamicity and uncertainty of its hosting cloud environments. To cope with these challenges, we endow big services with self-* capabilities and we propose an autonomic computing architecture for big services. We also take advantage of two recent technologies called knowledge graphs and multi-view learning, to represent the managed big service’s information (service descriptions, services’ and data sources’ quality levels, management policies) as a heterogeneous information network. Finally, a decision mechanism to select and trigger the appropriate management policies is defined and validated through a set of experiments.
Benefiting from the pay-as-you-go business model, cloud-based software applications are becoming more and more popular. A composite cloud system can be constructed by integrating existing component cloud services available over the internet as its system components. In order to fulfill the service-level agreements (SLAs), as well as users' quality of experience (QoE), a stable execution of the constructed system is desirable in the long term. To achieve this goal, system components at high risk of failing must be identified and fault-tolerated. This is extremely challenging in the dynamic cloud environment that host the component cloud services. However, existing approaches are constrained by their lack of modeling and analysis of system components' fluctuating reliability time series. This paper proposes PARS, a perturbation-aware approach, for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services' historical reliability time series. Then, it calculates the reliability sensitivity of the component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. Based on PARS, we propose a proactive adaptation approach for constructing and operating composite cloud systems. The results of experiments demonstrate the effectiveness and efficiency of the proposed approaches.
Service Internet is a complex, networked, and comprehensive service system formed by a large number of service units in different networks through a highly cooperative relationship. It can effectively and accurately create value for service stakeholders, and provide basic theoretical support for service application in many different situations. Nevertheless, there is a lack of systematic research on the overall dynamic evolution of service Internet, which leads to the lack of relevant mathematical models and theoretical guidance on the control and optimization for the overall service Internet. Therefore, considering the system characteristics such as cooperation and competition in service internet, this paper puts forward an overall evolution model of service Internet based on the logistic growth model, and then makes a theoretical analysis of its life cycle and evolution path. Finally, we use real-world electronic technology industry cluster data to verify the proposed model. Experiment results show that our model can better reflect and predict the evolution trend of service Internet.
The papers in this special section focus on data centric big data services. As an overwhelming amount of data is generated at a faster rate every day from all sources, and applications such as cloud services, the Internet of Things (IoT), social network services and intelligent terminals, it has become more urgent than ever to design, deploy and provision services more wisely so that the provisioned services could support effective acquisition, storage, transformation, process, management and utilization of such data. Manipulating and getting the most out of the Big Data can bring unprecedented value and new opportunities that are critical to business success. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, and improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of the Big Data. Datacentric big service is an inevitable evolution of services with the emergence of big data in the last decade.
Over the last years, big data has emerged as a new paradigm for the processing and analysis of massive volumes of data. Big data processing has been combined with service and cloud computing, leading to a new class of services called “Big Services”. In this new model, services can be seen as an abstract layer that hides the complexity of the processed big data. To meet users' complex and heterogeneous needs in the era of big data, service reuse is a natural and efficient means that helps orchestrating available services' operations, to provide customer on-demand big services. However different from traditional Web service composition, composing big services refers to the reuse of, not only existing high-quality services, but also high-quality data sources, while taking into account their security constraints (e.g., data provenance, threat level and data leakage). Moreover, composing heterogeneous and large-scale data-centric services faces several challenges, apart from security risks, such as the big services' high execution time and the incompatibility between providers' policies across multiple domains and clouds. Aiming to solve the above issues, we propose a scalable approach for big service composition, which considers not only the quality of reused services (QoS), but also the quality of their consumed data sources (QoD). Since the correct representation of big services requirements is the first step towards an effective composition, we first propose a quality model for big services and we quantify the data breaches using L-Severity metrics. Then to facilitate processing and mining big services' related information during composition, we exploit the strong mathematical foundation of fuzzy Relational Concept Analysis (fuzzy RCA) to build the big services' repository as a lattice family. We also used fuzzy RCA to cluster services and data sources based on various criteria, including their quality levels, their domains, and the relationships between them. Finally, we define algorithms that parse the lattice family to select and compose high-quality and secure big services in a parallel fashion. The proposed method, which is implemented on top of Spark big data framework, is compared with two existing approaches, and experimental studies proved the effectiveness of our big service composition approach in terms of QoD-aware composition, scalability, and security breaches.
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