Deep neural network(DNN) services are now widely used in industrial defect detection applications. With the increasing number of pre-trained model services on MaaS platforms like HuggingFace and inside smart enterprises, fine-tuning or directly applying DNN services has become a new solution for building intelligent applications. However, selecting appropriate services for tasks with various industrial requirements is also challenging work. Existing DNN model recommendation systems typically categorize models based on a limited set of task types or leverage the training data similarities. However, they fail to reflect the DNN service's native transferability and dynamic ability in the specific industrial scenario (i.e., pragmatics). In this paper, we introduce DSR, a novel pragmatic-information-model-based DNN service recommendation approach, designed to retrieve the most suitable services by incorporating information across the scene of industrial tasks and the ability of services. Through graph convolutional networks, DSR embeds the pragmatic information model of services into unified vectors and applies a regression model for usefulness-oriented recommendation towards specific industrial tasks. Additionally, we established a benchmark dataset with hundreds of customized tasks derived from public datasets with open-source services, on which we evaluate DSR compared to existing methodologies, including ImageDataset2Vec, AutoMRM, and TransferGraph. Our results demonstrate DSR's superior performance in terms of accuracy, efficiency, and generality. We also conduct a case study on an industrial surface defect detection scenario, which illustrates the feasibility of the system.

An important task in IoT application scenarios is to perform synergy reasoning on the phenomenal data and events of strong temporal semantics and complex correlations characteristics, with the help of associated knowledge and rules. However, current reasoning methods suffer from difficult rule representation with poor readability, lack of temporal semantics, and high reasoning complexity and inefficiency. To address these problems, this paper proposes parallel collaborative reasoning approaches based on DatalogMTL. Firstly, a series of collaborative access control mechanisms are designed for the concurrent conflict problems. Then, the rule-level parallel and fact-level parallel reasoning methods are presented based on materialization algorithm respectively. In this paper, we take experiments on two relative datasets and verify that our approaches greatly improve the reasoning efficiency and have good scalability in IoT scenarios.

Tracking the process of remote task execution is critical to timely process analysis by collecting the evidence of correct execution or failure, which generates a process digital twin (DT) for remote supervision. Generally, it will encounter the challenge of constrained communication, high overhead, and high traceability demand, leading to the efficient remote process tracking issue. Existing approaches can address the issue by monitoring or simulating remote task execution. Nevertheless, they do not provide a cost-effective solution, especially when unexpected situation occurs. Thus, we proposed a new cloud-edge collaboration framework for process DT generation. It addresses the efficient remote process tracking issue with a real-virtual collaborative process tracking (RVCPT) approach. The approach contains three patterns of real-virtual collaboration for tracking the entire process of task execution with a coevolution pattern, identifying unexpected situations with a discrimination pattern, and generating a process DT with a real-virtual fusion pattern. This approach can minimize tracking overhead, and meanwhile maintains high traceability, which maximizes the overall cost-effectiveness. With prototype development, case study and experimental evaluation show the applicability and performance advantage of the new cloud-edge collaboration framework in remote supervision.

The three-dimensional (3D) curved plate manufacturing is performed by constructing surfaces corresponding to the shape of the curved plate for multi-point forming. However, in the manufacturing process, the rebound restricts the forming accuracy, and the currently adopted rebound control methods cannot predict the rebound amount accurately. Meanwhile, the process involves multi-role collaboration and multiple data conversions and comparisons. These problems lead to a high degree of manual dependence, which affects manufacturing efficiency and accuracy. To address the above problems, this paper proposes a collaborative platform for the intelligent manufacturing of curved plates based on graph matching. Firstly, this paper establishes information models covering the whole process of curved plate manufacturing and forms a unified topology graph model. Then, the intelligent generation method of processing parameters based on graph matching is proposed, which realizes similar case recommendation and case-based processing parameters generation. Finally, we design and develop a collaboration platform based on micro-service architecture to support efficient collaboration among various departments and roles. In this paper, we use sail-shaped curved plates as a case of processing parameters generation and verify that this intelligent method can improve the accuracy of rebound control by comparison with related work, which shows that our method can be effectively applied to curved plate manufacturing.

Since 2020, the COVID-19 pandemic severely disrupted regular off-line business activities. This unprecedented situation inspires the valuable research on facilitating off-line business under pandemics. In this article, we conceptualized the problem as travel management in pandemic (TMiP) and analyzed it from the technological perspective. Enabling travel in a pandemic not only needs a health certificate to prove that the traveler is safe but also entry/exit permissions from both the origin and the destination regions, determined by the local situation and measures. Thus, TMiP is related to technical, social, economic, and administrative factors. By conducting a review on the literature covering the health certificate technology, its adoption in practice, and the exchange system technology published during the COVID-19 pandemic, we learned about their usefulness and limitations in TMiP. Second, we analyzed the review outcomes to infer the six distinctive technical challenges of TMiP. Third, we analyzed the feasibility of referential solutions to these challenges and showed their applicability and limitations. Finally, we offered the perspectives on new TMiP solutions and concluded that they rely on adapting existing solutions, creating new ones, and integrating all of them. We also presented future research directions in a holistic view of TMiP technical solutions. Overall, the findings of the study will stimulate more research on a more coordinated, comprehensive, and intelligent TMiP solution. We also hope this article can help practitioners to restart economies in a pandemic.

One of the main goals of the Internet of Things(IoT) systems is to achieve intelligent interaction of IoT devices. Event prediction is one of the approaches to achieve intelligent interaction. Event logic graph can effectively represent the relationship between events and be used for event prediction. However, in IoT systems, the data generated by IoT devices are usually incomplete and there are complex relationships between events, which in turn affect the accuracy of event prediction in the event logic graph. To address the above problems, this paper proposes a scenario-aware event prediction approach based on event logic graph in IoT systems. First, a flexible paradigm is designed for recognizing events and scenarios in IoT devices. Then, a scenario collaboration-based event context extraction method is proposed for extracting event contexts with similar scenario attributes in the event logic graph. Finally, a scenario-based event prediction method is designed to predict the events that will occur subsequently. In this paper, we verify that our approach can improve the accuracy of event prediction through the case of driving, which shows that our approach in this paper can be effectively applied in IoT systems.

Recent trends in the Web of Things (WoT) have led to data explosion. Data lake (DL), as a flexible on-demand heterogeneous data management architecture, has become a feasible solution in data management. Metadata modeling for DLs is the key basis for smart analysis and processing. However, the varieties in structures and semantics of industrial WoT data hinder metadata modeling and maintenance. Moreover, the lack of textual descriptions and the semantics hidden in value streams make it hard to automatically construct semantic metadata. The dynamic nature of WoT requires on-time evolution on metadata. To overcome these challenges, we propose an automated bottom-up metadata generation approach for DL of WoT applications. Applying a data-driven framework, raw data are notated as linked data and self-organizing map-based online clustering is applied to real timely extract data characteristics. To recognize entities, concepts and relations, semantics-based entity discovery approach from short texts is proposed according to the feature of WoT data. The numerical analysis is performed to find the hidden relations from raw values. Full-dimensional metadata with rich semantic knowledge are finally built. Experiments on a real-world dataset are conducted to verify the effectiveness of methods and a case study on an energy WoT system is provided to demonstrate the feasibility of the approach.

Currently user authentication and identity monitoring are required in various collaborative computer supported systems, such as online assessments and examinations. However, existing authentication methods such as passwords checking are less reliable. In addition, identity monitoring is hard to be realized effectively and efficiently for applications based on collaborative architecture. In this paper, we leverage keystroke dynamics to explore biometrics security and propose a user authentication framework based on edge computing architecture to address these issues. To support both static and continuous authentications with high accuracy and efficiency, dynamically improved keystroke profiles, Gaussian model based anomaly detector and keystroke stream processing are designed in the framework. The feasibility and effectiveness of the framework are verified by three representative public data sets and a real-world case study. The results show that the authentication can proceeds efficiently to enable an undisturbed and secure environment for online examinations.

One of the primary goals of cyber-physical system is to deeply integrate cyberspace and the physical world to realize intelligent interaction of the system. Event prediction technique is a powerful means to fulfill this goal. Recently, a novel knowledge graph, the event graph, is widely studied in the field of event analysis due to its excellent ability in event relationship modeling. Therefore, this paper proposes constructing the event graph to model the sequential event evolution in the physical world for event prediction. To this end, the sequential event graph construction method and related event prediction mechanism for CPSs are proposed. First, a flexible and universal paradigm is designed to assist in extracting event instances from the data generated by physical devices. Then, an automatic event graph construction method based on frequent episode mining is proposed. Finally, the related prediction mechanism is designed, including the identification of contextual information a nd the prediction of subsequent events. A case study on car usage illustrates the feasibility of our approach. The flexibility and support for complexity are demonstrated by a comparative discussion.

While recent research in music generation has mostly focused on encoder decoder architectures and self-attention mechanisms, prominent advancements regarding GANs have not yet been incorporated for the creation of music. These include solutions for major challenges when training GANs, most importantly training instability. In this work, we aim to apply this new knowledge to music generation, in order to make it more efficient and enable the automatic creation of music of higher quality. We utilize the progressive approach towards GANs, and implement it to train on symbolic music data. For best results, we process this data to obtain a new dataset, which matches the progressive approach. To achieve this, we propose a new way of downsampling fit for musical data. We furthermore conduct a user study to evaluate our results, and compute an FID score of 12.30 as objective metric.

Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework.

With the transformation of Internet of Things to Web of Things (WoT), a variety of applications are required to deal with huge volumes of real-time and heterogeneous data. However, in most applications, due to the weak semantics of data itself and the loose combination with specific scenarios, it is sometimes difficult to depict the spatiotemporal feature of the scenario in an application only through the data. In this paper, a spatiotemporal semantic scenario meta-model-based configurable platform is proposed for the development of WoT applications to address this issue, based on the data configuration, event stream configuration, and service encapsulation, the entire WoT scenario can be depicted with an abstract data model and related rules, and the business process can be changed by redefining corresponding rules when requirements change. A case study is given to verify the feasibility of our platform. The result shows that the platform can provide background support for WoT applications in a promising way.

Driven by the automation technologies and health informatics of Industry 4.0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.

Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.

Information resource services are the key element for resource sharing in cloud manufacturing. Traditional resource service models focus on modeling the attributes, interfaces, and descriptions of the resources into resource information services. Such resource services are suitable for local environment but suffer semantic heterogeneities in open cloud environment. Recently, well-designed ontologies are applied in resource service models to unify the schema and eliminate the semantic heterogeneities among the services. However, the effectiveness of ontology-based models mainly depends on the expertise of the ontology experts in ontology designing. Moreover, it is difficult to catch the dynamic changes in the cloud once the ontology has been embedded. In this paper, a semantic model is presented for information resource service modeling that uses semantic links instead of ontologies. The model takes advantage of semantic links to enable automated integrating and distributed updating in resource service cloud. In the experiment, the model is applied on practical manufacturing resources from a wheel manufacturing company. The case study and experimental results show that the proposed model is suitable for modeling manufacturing resources into cloud services and enables the flexible and distributed manipulation on resource services in the cloud environment.

Emergency clinical decision making is a challenging issue in healthcare services, notably in the environment of complicated data processing. Effective and efficient clinical decision making highly depends on the sufficient information sharing of the involved working teams. However, emergency decision support systems are usually hard to be developed because that the problems of emergency decision are always unexpected and unstructured. This paper focuses on the developing of decision support system to coordinate actions carried out in emergency situations. A framework is proposed based on MDA (Model-Driven Architecture) approach and resource model to dynamically build decision support system when emergency events occur. The effectiveness of our method is discussed and verified in a case study of collaborative clinical decision making on traffic accident emergency rescuing. The result shows that the MDA approach combined with resource model has the potential to support information system evolution along with the emergency events.

Internet of Things (IoT) software is required not only to dispose of huge volumes of real-time and heterogeneous data, but also to support different complex applications for business purposes. Using an ontology approach, a Configurable Information Service Platform is proposed for the development of IoT-based application. Based on an abstract information model, information encapsulating, composing, discomposing, transferring, tracing, and interacting in Product Lifecycle Management could be carried out. Combining ontology and representational state transfer (REST)-ful service, the platform provides an information support base both for data integration and intelligent interaction. A case study is given to verify the platform. It is shown that the platform provides a promising way to realize IoT application in semantic level.

The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.

Online visual steering technology provides users a 3D cooperative working environment in the complex or multi-steps task. Due to the difference of role or authority, new demands have risen such as multi-view presentations and conflict solving mechanism for users in the process of cooperative work. Thus, a configurable architecture based on scene composition is proposed for visual steering. It mainly contains several points: (1) Configurable scene structure including geometry data, concept model, user profile, enabling scene composition to build multi-view for different uses in working environment. (2) Browser-based 3D scene rendering page using X3Dom. (3) Loose coupling model sources on web services. A demo is also provided to test our architecture and verified its usability. By comparing with other implement methods, this architecture provides more flexible customizability and expandability, together with light-weight web-based clients.

Integration and unified management of data scattering along the lifecycle chain is the primary problem to be solved in PLM field. In this paper, a framework based on resource meta-model is proposed to integrate and manage product lifecycle data. Firstly, the structure of this framework is presented. Then, the resource meta-model is defined and the functions, structure and generation method of the model are given in detail. To better control resource accessing operations, a resource accessing control mechanism is proposed to make sure that product lifecycle information in different stages is accessed by authorized roles in valid way. Finally, a case study is presented to demonstrate the application of our framework for PLM. The result shows that our proposed method could integrate and manage heterogeneous data during product lifecycle more flexibly.

Due to the complexity of describing and execute business requirements, it is difficult for enterprises to adapt to rapidly changing environment. Thus a Configurable Resource-Oriented Service Architecture (CROSA) is proposed to bridge business modeling in build time and services execution in run time seamlessly. Firstly, based on business model analysis, a business meta-model is built to act as a referred model to encapsulate enterprise information resources. Then these resources act as the basic description to generate services by means of service transformation. And WADL and BPEL are involved in this step. Next, referred to Model-View-Controller pattern of software development, IT elements including service, web pages and data sources are mapping to resources oriented service architecture. Lastly, a state space defined by a resource array is built as the control mechanism for services integration so as to build a completed IT system. And a prototype system is implemented to develop data-centre information system for verification. The approach provides a way to realize service cooperation in a more flexibly pattern.

Product lifecycle data is considered valuable resource to improve quality of product designing. However, at present, under the environment of E-manufacturing, product data is scattered across different companies during product lifecycle. Because enterprise information systems are heterogeneous, it becomes a challenging task managing product lifecycle data through the complete supply chain. In this paper, firstly, the product lifecycle management process is analyzed from the viewpoint of supply chain. Then, a data model is proposed to represent business activities related to product data generating. In the data model, all business activities are discomposed to three elements which are target, work flow and resource. All product data is composited by these three kinds of elements. Furthermore, in order to integrate heterogeneous data across supply chain, ontology is used to represent the proposed data model. Because that the ontology is abstracted from different companies in supply chain, multi-views modeling method is adopted to construct ontology collaboratively. Finally, based on the data model of ontology, a prototype of product lifecycle management is designed and implemented in the application of a manufacturing company. The case study shows that the ontology-based data model could support the integration of heterogeneous data during product lifecycle flexibly.

The recent years have witnessed a rapid development in virtual reality technology and computer aided design, which accompanied by a vast amount of 3D content springing up from various domains, has given rise to an emerging need for methods by which they can be efficiently annotated before being searched and retrieved. In this paper a new approach to semantic annotation on 3D content is proposed. In this approach, much of the manual work on annotation is replaced with a semi-automatic process based on prior gathering of domain expertise. The geometric properties of objects and their spatial relationships are first automatically extracted and associated with a general ontology, the result of which is then matched against a set of user-defined rules to create annotations represented in an application ontology. The approach is Web3D oriented so that it can take advantage of features of X3D format. It will be shown using a prototype system how this approach can succeed in accelerating the annotation process.

Nowadays many companies built enterprise level data warehouses (DW) for decision making support. However explosive data accumulated in distributed databases in company or across companies with the widely use of Computer Supported Cooperative Work in Design (CSCWD) technologies. Therefore it becomes a time costing task for engineers to construct the multidimensional model of data warehouse. This research presents an ontology approach to eliminate data source heterogeneity aiming to design the conceptual structure of data warehouse automatically. The proposed approach includes a domain ontology mete-data model, which consists of data, concept, ontology and resource repositories, to describe the semantic meaning of the data sources. The supply-driven and demand-driven methodologies are combined together to construct the concept model of DW. By supply-driven method, domain ontology system is extracted bottom-up from data sources; on the other hand, by demand-driven method, relationships of the concepts in the ontology system are extended top-down according to the business process. Furthermore, candidate of the data warehouse concept model is derived according to the relationships of the concepts in the ontology system. After discussing the process of data warehouse designing, a case study is given to show how our method is used in clinic domain. The result shows that ontology method could help users designing data warehouse more easily.

In order to gain the competitive advantage, more and more hospitals put their attention on determining and optimizing the standard clinical pathway. However, there are many abnormal instances in the event logs which disturb the effect of the process mining and the process analysis. Also, the prescription and the medical test items may have a large difference in the specific disease, where has the similar clinical pathways due to the multi-factor in the clinical pathways. In this paper, an abnormal process instances identification method (APIIM) is proposed. Given the event logs and the standard clinical pathway, the method classifies the instances based on the classification attributes and identifies the abnormal instances by the outlier detection technology. Moreover, a case study using the real data in one hospital is implemented and the result shows that the method is effective and efficient in discovering the abnormal process instances in the healthcare environment.

With rising accumulation of data and applications in enterprise IT environment, the business data management becomes increasingly important. But the data in the IT environment is usually organized by static modeling, which could not meet the business changes need. In our study the enterprise ontology is adopted as conception backbone to model for the business scenario, and the use of ontology evolution can reflect the business process changes. So we explore the data mode mapping and the modeling of enterprise ontology. Then the analysis of changes derived from application requirement is forced specially. Finally, the changes realization enables the whole evolution process to be implemented. The prototype system has been developed now in order to fulfill the application. It can help connect the data and business, and obtain business changes to direct the ontology evolution. As there are an increasing research and application tendency in the field of business data modeling and ontology evolution, our study pay more attention to the modeling, mapping and analysis between dynamic business changes and ontology.

Considering the low efficient of resource re-use for lack of consideration of user's role, team, design stage, and individuation in design activity, an agent-based interactive mechanism is proposed to build CSCW supported environment. Firstly, an Interactive Agent model is used to describe user feature in CSCW design activity. The definition of role, people preference, ability description as well as the definition of action, mental state, and other descriptions is formally represented in ABI agent structure. Then, based on analysis of design activity, a general design meta-resource (DMR) structure is constructed as the basic meta-unit to extract, organize, manage, and operate resources. Therefore, by combining user feature with design resource feature into resource matching calculation, design resources are accessed and pushed to the people with more pertinence. A practical resource-based system has been implemented for a jewel design enterprise. The result shows that the system makes design activity more effectively by pushing design resources with a high precision.

XML-based cnXML is an new e-business specification developed in China. cnXML defines the technical standard, data interchange protocol and the business processes of the B to B e-business. A primary objective of cnXML is to lower the barrier of entry to e-business, particularly with respect to small and medium -sized enterprises in China. In this paper, we gave a brief overview of the development of electronic business standards and compared the current e-business standards with traditional EDI. Then we put forward the rationales of design cnXML. Finally we investigate the characteristics and application of cnXML.