Fenglin Bu

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.

Linked Semantic Model for Information Resource Service Toward Cloud Manufacturing

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.

A Hybrid User Profile Model for Personalized Recommender System with Linked Open Data

In order to better enhance user experience on the web, varies applications such as search engines have integrated with recommender systems. Users will get some relevant items recommended when browsing the result page. However, building such recommendation services needs a large amount of item information, which makes it hard to start a new recommender service. Due to the development of Semantic Web and Linked Data, a vast amount of RDF data can be accessed via the Internet and naturally used as a knowledge base for recommender systems. In this paper, we study modeling user profiles in semantic environment and building a personalized recommender system exclusively on Linked Open Data. First, we define two concepts related to user browsing history and propose a hybrid user profile model (Hay-UPM). Second, we design a generic and personalized recommender system to utilize the semantic information between the items and user profile model to make recommendations. Finally, we conduct our experiment on the movie dataset of DBpedia and MovieLens. The result shows Hy-UPMhas a better Mean Reciprocal Rank performance compared with other recommendation methods.

A Business-driven Methodology for Service-Oriented Information System Development

In order to build a flexible and configurable software architecture to realize enterprise business requirements agilely for distributing environment, a methodology called BSAD (Business-driven Service-oriented Application Development) is proposed to bridge business models in build time and application development in runtime seamlessly. From the five stages of business modeling, service transformation, Process modeling, resource mapping and system implementation, a running application could be developed. And a software supported platform is also built to develop information systems based on BSAD. The research indicates a new way to develop enterprise service-oriented information system in a more flexible and configurable way.

Rules and Implementation for Generating Ontology from Relational Database

On the purpose of generating ontologies from data sources on the practical application consideration, some rules on mapping relational database to ontologies are proposed. For the massive data stored in information systems, Master data management is brought in to make classification of data. Firstly, map the relation schema information into the ontology as concepts. Secondly, achieve the attributes, and map them to the properties in the ontology. Thirdly, analyze the constraints in databases, and modify the property constraints as needed in ontology. Then, extract the records into the ontology as individuals of concepts. At last, an implementation of generating ontologies from Mysql data source using the rules above is recommended as a case study of these rules. The result shows the rules are reasonable and complete in generating ontologies, and the method could be applied to the practical application.

A Four-Layer Flexible Spatial Data Framework towards IoT Application

Huge amounts of spatial documents have been generated every day in IoT (internet of things) applications. Both spatial and textual or other information of complicated structure should be taken into account when providing services. Therefore, it is crucial to store and process these data efficiently in IoT applications. In this paper, we propose a flexible four-layer framework for spatial data towards IoT applications, which decouples the spatial and content information and organizes the whole space with a pyramid structure. The four layers of the framework are geography node, application node, logical node and storage node that are related with the accurate spatial information, content information with rich semantic, flexible space organization and efficient storage with hybrid database respectively. Among them, logical node is the center which associates the other nodes, and provides an efficient query processing method using the pyramid structure. We apply the four-layer framework in the Smarter City Charging Pile System and construct the four kinds of nodes to realize the function that recommends the best matching charging piles for electric vehicle drivers taking both distance and pile state into consideration. The compare discussion shows that our framework has high flexibility, supports rich semantic and is more acceptable in practical IoT applications.

A Process Mining Based Service Composition Approach for Mobile Information Systems

Due to the growing trend in applying big data and cloud computing technologies in information systems, it is becoming an important issue to handle the connection between large scale of data and the associated business processes in the Internet of Everything (IoE) environment. Service composition as a widely used phase in system development has some limits when the complexity of relationship among data increases. Considering the expanding scale and the variety of devices in mobile information systems, a process mining based service composition approach is proposed in this paper in order to improve the adaptiveness and efficiency of compositions. Firstly, a preprocessing is conducted to extract existing service execution information from server-side logs. Then process mining algorithms are applied to discover the overall event sequence with preprocessed data. After that, a scene-based service composition is applied to aggregate scene information and relocate services of the system. Finally, a case study that applied the work in mobile medical application proves that the approach is practical and valuable in improving service composition adaptiveness and efficiency.

Personas in O2O Mobile Healthcare: A Method for Identifying and Creating User Groups

Persona is a useful technique to obtain a group of target users that share common behavioral characteristics. However, persona analysis in mobile healthcare system is particularly difficult due to the complexity, ad hoc and multi-scenario nature of healthcare processes. In this paper, a personas method is proposed for the identification and creation of user groups in mobile healthcare. The approach utilizes sequence alignment, clustering technique to identify user groups. Then based on the behaviors of the user groups, we perfect the personas by constructing a decision tree of multiple user attributes. The approach is demonstrated in a case study of mobile medical appointment. The result shows that the method overcomes some drawbacks of current methods, and it is less subjective, more efficient, and less reliant on specialized expertise.

A Bottom-Up Approach for BOM Generation and Its Application in Mass Customization

Complex product development faces huge amount of components with complex structure and dynamic changes in product lifecycle, which is a key challenge in mass customization. This paper addresses this issue by providing a product configuration solution based on bill of materials (BOM): First, conceptual objects with flexible structure and unified identifier (UID) in 2d-code form are built to represent business entities. Second, the relations among the instances of the conceptual objects are analyzed to dynamically generate BOM in different lifecycle phase, which is the product configuration view. Third, quality information monitoring and completeness check can be achieved based on BOM structure. Finally, a prototype system for aircraft manufacturing is built, which supports requirement change and quality management in the whole product lifecycle.