Cheng Xie

Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

IoT-Based Configurable Information Service Platform for Product Lifecycle Management

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.

A framework of emergency clinical decision support system based on MDA and resource model

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.

Instance-Driven Ontology Evolution Mechanism towards Enterprise Data Management

with the wide application of information systems, more and more enterprises adopt ontology as conceptual backbone for business data management to take advantage of knowledge base and semantic web technology. However, dynamic business motivates modifying existed ontology according to the continuous changes, which is hard to be done manually due to the growing size of ontology. Thus automatic method is needed for ontology evolution. In this paper, an instance-driven ontology evolution approach is proposed to cope with the dynamic changes in ontology evolution. The proposed approach suggests changing directions to users to control the process of evolution. Then the changes of ontology are formalized with elementary and composite changes which are considered as the basis of evolution. Moreover, the identification of changes is realized by instances analyzing. Finally, the approach is tested in a hospital database. The result shows instance-driven ontology evolution is an efficient method to cope with data conception changes in enterprise data management.

A Linked Data Based Decision Support System for Cancer Treatment

Cancer treatment is a complex process that needs experienced doctors and solid medical knowledge. Different cancer treatment methods work for different patient characteristics. Sometimes it is hard for doctors to specify the treatment for certain patient because it is difficult to get useful information to support treatment decision making from diverse clinic data source. In order to improve the effectiveness of information searching from complicated data environment for cancer therapy, we propose a linked data based system for cancer treatment methods selecting to help doctors in the process of cancer treatment. This system incorporates hospital inner data and open data in life science field combined with Linked Data model. On this basis, a cancer treatment selection algorithm is proposed to find similar cases from historical cases. Finally, a protocol system is implemented to show the usability of our method in the applications for intelligent medical supporting.

An Ontology Approach for Manufacturing Enterprise Data Warehouses Development

Data warehouse (DW) is a powerful and useful technology for decision making in manufacturing enterprises. Because that the operational data often comes from distributed units for manufacturing enterprises, there exits an urgent need to study on the methods of integrating heterogonous data in data warehouse. In This paper, an ontology approach is proposed to eliminate data source heterogeneity. The approach is based on the exploitation of the application of domain ontology methods in data warehouse design, representing the semantic meanings of the data by ontology at database level and pushing the data as data resources to manufacturing units at data warehouse access level. The foundation of our approach is a meta-data model which consists of data, concept, ontology and resource repositories. The model is used in a shipbuilding enterprise data warehouse development project. The result shows that with the guide of the meta-data model, our ontology approach could eliminate the data heterogeneity.

Dynamic Weight-Based Individual Similarity Calculation for Information Searching in Social Computing

In the social computing environment, the complete information about an individual is usually distributed in heterogeneous social networks, which are presented as linked data. Synthetically recognizing and integrating these distributed and heterogeneous data for efficiently information searching is an important but challenging work. In this paper, a dynamic weight (DW)-based similarity calculation is proposed to recognize and integrate similar individuals from distributed data environments. First, each link of an individual is weighted by applying DW. Then, a semantic similarity metric is proposed to combine the DW into similarity calculation. Then, a searching system framework for a similarity-based individual is designed and tested in real-world data sets. Finally, massive experiments are conducted both in benchmark and real-world social community data sets. The results show that our approach can produce a good result in similar individual searching in social networks. In addition, it performs significantly better than the existing state-of-the-art approaches in similar individual searching.

Transitional Resource Meta-model: Generating Restful Service to Implement Complex Activity

Service-oriented architecture is widely adopted in the development of web information system. The construction of web service, however, is challenging, since it should not only meet up with intensively changing requirements but also be capable of handling complex activity where multiple entities are involved. The traditional RESTful service is restricted in implementing complex activity since it is single-resource-oriented. Using SOAP services to implement is possible but time-consuming, thus not adaptive to the sensitive requirements change. Therefore, a Transitional Resource Meta-model (TRM) is proposed in this paper to generate the RESTful service with the capability of executing complex activity in a flexible and fast way. Our proposed model functions on describing the complex activity by using a state transfer sequence for multiple entities as well as generating the service interface and controlling the execution of the service. A case study is given to represent the construction process and generation results of TRM, and a comparison with REST-based architecture and SOAP-based architecture is provided at the end to show the advantages of TRM approach.

Profiling similarity links in Linked Open Data

Usually the content of the dataset published as LOD is rather unknown and data publishers have to deal with the challenge of interlinking new knowledge with existing datasets. Although there exist tools to facilitate data interlinking, they use prior knowledge about the datasets to be interlinked. In this paper we present a framework to profile the quality of owl:sameAs property in the Linked Open Data cloud and automatically discover new similarity links giving a similarity score for all the instances without prior knowledge about the properties used. Experimental results demonstrate the usefulness and effectiveness of the framework to automatically generate new links between two or more similar instances.

Utilizing semantic information from linked open data in web service clustering

With web services growing and accumulating in application area, service discovery has become a hot issue for service composition and service management. Service clustering provides a promising way to split the whole searching space into small regions so as to minimize the discovery time effectively. However, semantic information is a critical element during the whole disposing process. Current industrialized Web Service Description Language (WSDL) does not contain enough information for service description. Therefore, a service clustering method has been proposed, which enhances original WSDL document with semantic information by means of Linked Open Data (LOD). Experiment based on real service data has been performed, and comparison with similar methods has also been provided to demonstrate the effectiveness of the method. It is shown that utilizing semantic information from LOD enhances the precision of service clustering. And it forms a sound base for further comprehensive processing with semantic information.