Recently, the care of chronic disease has become a worldwide challenge. Although the healthcare domain has begun to adopt new ICT technologies such as the Internet of Things and cloud computing, it is still in its initial phase. In this chapter, the authors conduct a small-scale two-step case study of diabetes care in Blekinge, Sweden. The first step is intended to acquire insight into the actual care situation to improve the original conceptual model. In the second step, we validate the feasibility of the improved model working in real healthcare. The results show that the Internet of Things and cloud computing is acceptable by both healthcare providers and recipients for home-based chronic diseases healthcare with little concerns of security and privacy in the future. In our hybrid cloud model, hospitals and primary healthcare centers could continue using their own exciting databases as private clouds. The best approach for home-based healthcare data is to collect it by connecting the different devices directly and storing and processing them in public clouds. To realize this model in the future, we suggest that a professional IT healthcare education team should be created to support both healthcare providers and recipients. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Health data integration enables a collaborative utilization of data across different systems. It not only provides a comprehensive view of a patient’s health but can also potentially cope with challenges faced by the current healthcare system. In this literature review, we investigated the existing work on heterogeneous health data integration as well as the methods of utilizing the integrated health data. Our search was narrowed down to 32 articles for analysis. The integration approaches in the reviewed articles were classified into three classifications, and the utilization approaches were classified into five classifications. The topic of health data integration is still under debate and problems are far from being resolved. This review suggests the need for a more efficient way to invoke the various services for aggregating health data, as well as a more effective way to integrate the aggregated health data for supporting collaborative utilization. We have found that the combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges based on our analysis of the review result.

Effective and convenient self-management of health requires collaborative utilization of health data from different services provided by healthcare providers, consumer-facing products and even open data on the Web. Although health data interoperability standards include Fast Healthcare Interoperability Resources (FHIR) have been developed and promoted, it is impossible for all the different categories of services to adopt in the near future. The objective of this study aims to apply Semantic Web technologies to integrate the health data from heterogeneously built services. We present an Web Ontology Language (OWL)-based ontology that models together health data from FHIR standard implemented services, normal Web services and Linked Data. It works on the resource integration layer of the presented layered integration architecture. An example use case that demonstrates how this method integrates the health data into a linked semantic health resource graph with the proposed ontology is presented.

The vast amount of Web services brings the problem of discovering desired services for composition and orchestration. The syntactic service matching methods based on the classical set theory have a difficulty to capture the imprecise information. Therefore, an approximate service matching approach based on fuzzy control is explored in this paper. A service description matching model to the OpenAPI specification, which is the most widely used standard for describing the defacto REST Web services, is proposed to realize the fuzzy service matching with the fuzzy inference method developed by Mamdani and Assilian. An evaluation shows that the fuzzy service matching approach performed slightly better than the weighted approach in the setting context.

Various health Web services host a huge amount of health data about patients. The heterogeneity of the services hinders the collaborative utilization of these health data, which can provide a valuable support for the self-management of chronic diseases. The combination of REST Web services and Semantic Web technologies has proven to be a viable approach to address the problem. This paper proposes a method to add semantic annotations to the REST Web services. The service descriptions and the resource representations with semantic annotations can be transformed into a resource graph. It integrates health data from different services, and can link to the health-domain ontologies and Linked Open Health Data to support health management and imaginative applications. The feasibility of out method is demonstrated by realizing with OpenAPI service description and JSON-LD representation in an example use case.

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

The utilization of Web services is becoming a human labor consuming work as the rapid growth of Web. The semantic annotated service description can support more automatic ways on tasks such as service discovery, invocation and composition. But the adoption of existed Semantic Web Services solutions is hindering by their overly complexity and high expertise demand. In this paper we propose a highly lightweight and non-intrusive method to enrich the REST Web service resources with semantic annotations to support a more autonomous Web service utilization and generic client service interaction. It is achieved by turning the service description into a semantic resource graph represented in RDF, with the added tag based semantic annotation and a small vocabulary. The method is implemented with the popular OpenAPI service description format, and illustrated by a simple use case example.

Knowledge sharing has become an important issue that challenges the efficient healthcare delivery in eHealth system. It also rises as one of the mostdemanding applications with reference to dynamic interactivities among various healthcare actors (e.g. doctors, nurses, patients, relatives of patients). Inthis paper, we suggest an activity theory based ontology model to represent various healthcare actors. The goal of the suggested model is to enhance inte-ractivities among these healthcare actors for conducting more efficient knowledge sharing, which helps to design eHealth system. To validate the feasibilityof suggested ontology model, three typical use cases are further studied. A questionnaire based survey is carried out and the corresponding survey resultsare reported, together with the detailed discussions.

A huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. © 2017, Strojarski Facultet. All rights reserved.

The internet based social network has been applied to serve many social functions, such as democratic decision making, knowledge sharing, educa‐ tion, and healthcare. In this paper, we provide a prototype of virtual community designed for home-based chronic diseases healthcare. We studied the concept “community” from the activity theory model in order to design the prototype with a solid theoretical base. Then we conducted a questionnaire from healthcare recipients and interviewed healthcare providers to gather the requirements for the design of the community. With some user stories we described the requirements as use cases for our design and a conceptual prototype is built based on the requirements. This virtual community servers as a shared platform for all the stakeholders who are engaged in the healthcare activity. With this shared community platform, the interoperability problems of current healthcare systems can be moderated