Construction and Application of a Knowledge Graph

With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level’s ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.

A Cloud Solution for Service Oriented Workshop Management

Cloud manufacturing is a state-of-art networked manufacturing model with the idea and technologies of cloud computing to transform traditional production-oriented manufacturing into service-oriented manufacturing. This emerging model can make manufacturing resources, in a manner similar to traditional utilities such as water, gas and electricity, available (offered) over the internet as convenient, scalable, on-demand services to enterprises. The aim is to improve the sharing efficiency of manufacturing resources and reduce manufacturing costs in industries. In this paper, the current research of cloud manufacturing is summarized, including relevant theories, technologies and applications. A cloud solution for workshop management is proposed from a service perspective, along with its architecture and business process. The methodologies, including manufacturing resource virtualization and workshop sensor network configuration are developed to support heterogeneous data integration and effective collaboration among services in cloud. A case study is demonstrated and discussed to validate the proposed cloud service system.

Semantically Enhanced Web Service for Global Supply Chain Disruption Management

The recent coronavirus pandemic has now unleashed a global supply chain crisis across a huge number of organizations, stemming from a lack of understanding and flexibility of the multiple layers of their global supply chains and a lack of diversification in their sourcing strategies. One of the technical options to mitigate the pandemic is to automate business processes by which heterogeneous data integration is encouraged. The convergence of Semantic Web with service-oriented computing is manifested by Semantic Web services technology. It addresses the major challenge of automated, interoperable, and meaningful coordination of web service composition in industrial applications – such as apparel business. Automatic service composition may dramatically improve the development efficiency of web service applications. This chapter proposes an approach to automatically process semantic service composition (SSC) using description logics (DLs) to provide well-defined semantics. Also, this chapter explains the role of ontologies in the architecture of the Semantic Web.

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.