Boyi Xu

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.

An IoT-Oriented Data Storage Framework in Cloud Computing Platform

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 frameworks effectiveness.

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.

IoT-Based Big Data Storage Systems in Cloud Computing : Perspectives and Challenges

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.

The design of an m-Health monitoring system based on a cloud computing platform

Compared to traditional medical services provided within hospitals, m-Health monitoring systems (MHMSs) face more challenges in personalised health data processing. To achieve personalised and high-quality health monitoring by means of new technologies, such as mobile network and cloud computing, in this paper, a framework of an m-Health monitoring system based on a cloud computing platform (Cloud-MHMS) is designed to implement pervasive health monitoring. Furthermore, the modules of the framework, which are Cloud Storage and Multiple Tenants Access Control Layer, Healthcare Data Annotation Layer, and Healthcare Data Analysis Layer, are discussed. In the data storage layer, a multiple tenant access method is designed to protect patient privacy. In the data annotation layer, linked open data are adopted to augment health data interoperability semantically. In the data analysis layer, the process mining algorithm and similarity calculating method are implemented to support personalised treatment plan selection. These three modules cooperate to implement the core functions in the process of health monitoring, which are data storage, data processing, and data analysis. Finally, we study the application of our architecture in the monitoring of antimicrobial drug usage to demonstrate the usability of our method in personal healthcare analysis.

A Domain Ontology Approach in the ETL Process of Data Warehousing

Extract-Transform-Loading (ETL) tools integrate data from source side to target in building data warehouse. However data structure and semantic heterogeneity exits widely in the enterprise information systems. On the purpose of eliminate data heterogeneity so as to construct data warehouse, this paper introduces domain ontology into ETL process of finding the data sources, defining the rules of data transformation, and eliminating the heterogeneity. In this method, the domain ontology is embedded in the metadata of the data warehouse. Hence, the data record could be mapped from data bases to ontology classes of Web Ontology Language (OWL). As result, the accessing of information resources could be done more efficiently. The method is testing in a hospital data warehouse project, and the result shows that ontology method plays an important role in the process of data integration by providing common descriptions of the concepts and relationships of data items, and medical domain ontology in the ETL process is of practical feasibility.

Model-Driven Development Patterns for Mobile Services in Cloud of Things

Cloud of Things (CoT) is an integration of Internet of Things (IoT) and cloud computing for intelligent and smart application especially in mobile environment. Model Driven Architecture (MDA) is used to develop Software as a Service (SaaS) so as to facilitate mobile application development by relieving developers from technical details. However, traditional service composition or mashup are somewhat unavailable due to complex relations and heterogeneous deployed environments. For the purpose of building cloud-enabled mobile applications in a configurable and adaptive way, Model-Driven Development Patterns based on semantic reasoning mechanism are provided towards CoT application development. Firstly, a meta-model covering both multi-view business elements and service components are provided for model transformation. Then, based on formal representation of models, three patterns from different tiers of Model-View-Controller (MVC) framework are used to transform business models into service component system so as to configure cloud services rapidly. Lastly, a related software platform is also provided for verification. The result shows that the platform is applicable for rapid system development by means of various service integration patterns.

An automatic method of data warehouses multi-dimension modeling for distributed information systems

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.

A method of demand-driven and data-centric Web service configuration for flexible business process implementation

ABSTRACT Facing the rapidly changing business environments, implementation of flexible business process is crucial, but difficult especially in data-intensive application areas. This study aims to provide scalable and easily accessible information resources to leverage business process management. In this article, with a resource-oriented approach, enterprise data resources are represented as data-centric Web services, grouped on-demand of business requirement and configured dynamically to adapt to changing business processes. First, a configurable architecture CIRPA involving information resource pool is proposed to act as a scalable and dynamic platform to virtualise enterprise information resources as data-centric Web services. By exposing data-centric resources as REST services in larger granularities, tenant-isolated information resources could be accessed in business process execution. Second, dynamic information resource pool is designed to fulfil configurable and on-demand data accessing in business process execution. CIRPA also isolates transaction data from business process while supporting diverse business processes composition. Finally, a case study of using our method in logistics application shows that CIRPA provides an enhanced performance both in static service encapsulation and dynamic service execution in cloud computing environment.

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.