Proactive Personalized Services Through Fog-Cloud Computing in Large-Scale IoT-Based Healthcare Application

With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing(CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named Fog Cep Care. Experimental result shows that Fog Cep Care is superior to the traditional IoT-based healthcare application.

Mobile Cloud for Personalized Any-Media Services

In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personalized and suggest that virtualization and tighter cross-layer communication allows for convergence and seamless transition of services. These are also creating new and never-before seen ways of developing and delivering personalized any-media services. We discuss current paradigms for implementing cloud-based any-media services that generate revenue. Future research topics and requirements for evolving network and service elements are also discussed.

Construction of personalized knowledg environment for professional libraries:From the practice of Chinese National Agricultural Library

Purpose:Centralized on the construction of a personalized knowledge environment that can better meet the need of Chinese agricultural researchers,this paper presents the practice conducted by the National Agricultural Library(NAL)of Chinese Academy of Agricultural Sciences(CAAS)in the digital research environment.Based on the analysis of information needs of Chinese agricultural researchers,CAAS constructed digital research environments for its researchers at different levels,by means of providing more professional knowledge services for targeted researcher users.Design/methodology/approach:On the basis of the construction of public service platform—National Agriculture Information System of the Library of CAAS(i.e.NAL),local resources were integrated with public open resources by using key technologies,such as Web 2.0,knowledge navigation,linked data and intelligent retrieval,to construct an institutional personalized digital repository for CAAS researchers.Findings:By using the construction tool CAASPKE,knowledge environment platforms have been constructed for 10 CAAS institutes,taking roughly 33%of CAAS institutes.In addition,16 discipline information environment platforms have been set up for CAAS research teams,and 5 professional digital libraries for provincial agricultural academies,which spread in Beijing,Shanxi,Sichuan,and the Xinjiang and Tibet Autonomous Regions.Research limitations:User’s local collections ought to be integrated with IR resources of the CAAS platform constantly.Due to the lack of overall knowledge resources,functions of the platform have not been fully explored,so the effect need be evaluated with the time being.Practical implications:The construction of research knowledge environment in CAAS has not only contributed to the development of its personalized knowledge service system,but also made the functions of libraries transformed into the information service system.In this case,CAAS libraries are now playing more and more important roles in the innovation process of Chinese agricultural researchers.Originality/value:The innovative practice is the first endeavor that ever implemented in the agricultural information service area across China’s mainland.The construction tool developed for the knowledge environment of personalization could customize resources flexibly according to the need of different CAAS users,and it can organize the knowledge resources of CAAS institutes effectively.

Mining Interesting Knowledge from Web-Log

Web-log contains a lot of information related with user activities on the Internet. How to mine user browsing interest patterns effectively is an important and challengeable research topic. On the analysis of the present algorithm’s advantages and disadvantages we propose a new concept: support-interest. Its key insight is that visitor will backtrack if they do not find the information where they expect. And the point from where they backtrack is the expected location for the page. We present User Access Matrix and the corresponding algorithm for discovering such expected locations that can handle page caching by the browser. Since the URL-URL matrix is a sparse matrix which can be represented by List of 3-tuples, we can mine user preferred sub-paths from the computation of this matrix. Accordingly, all the sub-paths are merged, and user preferred paths are formed. Experiments showed that it was accurate and scalable. It’s suitable for website based application, such as to optimize website’s topological structure or to design personalized services. Key words Web Mining - user preferred path - Web-log - support-interest - personalized services CLC number TP 391 Foundation item: Supported by the National High Technology Development (863 program of China) (2001AA113182)Biography: ZHOU Hong-fang (1976-), female.Ph. D candidate, research direction: data mining and knowledge discovery in databases.

Exploration on China's Institution-oriented Elderly Care Service System Based on Human Factors Engineering

Objective Aging process in China has brought more and more challenges to traditional home-based care for the aged and more attentions to the mode of institution-oriented elderly care.This work,by analyzing the problems and defects of institution engaging in elderly care based on the human factors engineering theory,discusses the people-centered institution-oriented elderly care service system,in terms of the problems that the pattern of institution-oriented elderly care focuses more on the facilities and environment construction than the principal role of people in the elderly care service system.Methods Sampled the institutions engaging in elderly care in Hefei City,Anhui Province,P.R.China(a medium-sized city in China);surveyed 45 institutions engaging in elderly care and 828 registered elders in Hefei City by hierarchical and systematic sampling method.Results(1)institutions engaging in elderly care shared the occupancy rate of 49.6%and low total service efficiency;(2)institutions engaging in elderly care offered 100%life-oriented service items,but only 6.1%personalized service items provided;(3)100%simple housing facilities in elderly care institutions,but extremely less personalized service facility(3.2%),with poorer quality and less satisfaction;(4)insufficient professional service staff could be available;48.1%institutions have 5 or less professional service staffs.Conclusion A countermeasure to improve the elderly care service system was raised here by aiming at improving the institution-oriented elderly care service:(1)to transfer service toward the people-oriented concept,formulating preferential policy for endowment insurance;(2)to pay attention to personalized design of service facility,offer personalized and featured service items,and improve service management level;(3)to build professional team for elderly care.

Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot

In order to satisfy the robotic personalized service requirements that can select exclusive items to perform inference and planning according to different service individuals,the service robots need to have the ability to independently obtain the ownership relationship between humans and their carrying items.In this work,we present a novel semantic learning strategy for item ownership.Firstly,a human-carrying-items detection network based on human posture estimation and object detection model is used.Then,the transferred convolutional neural network is used to extract the characteristics of the objects and the back-end classifier to recognize the object instance.At the same time,the face detection and recognition model are used to identify the service individual.Finally,on the basis of the former two,the active learning of ownership items is completed.The experimental results show that the proposed ownership semantic learning strategy can determine the ownership relationship of private goods accurately and efficiently.The solution of this problem can improve the intelligence level of robot life service.

Personalized Service System Based on Hybrid Filtering for Digital Library

Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.

Trust-Based Personalized Service Recommendation: A Network Perspective

Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.

Evolving Information Filtering for PersonalizedInformation Service

Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user's information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence.

Study of Construction of Personalized Service Oriented Government Virtual Information Service Center

In this paper, we proposed the idea of personalized information service for enterprises and customers through government visual information service center (GVISC). It discusses an extensible, reusable, reconstructive and integrated framework of GVISC with new techniques, such as XML, agent, data mining and information portal. They include the design and analysis of the logical structure, the design of the data layer, and the description of the function application. We show an example of personalized information service for forest fire prevention in the government virtual information service center.

Smart Management Information Systems(Smis):Concept,Evolution,Research Hotspots and Applications

Management information system(MiS),a human-computer system that deeply integrates next-generation information technology and management services,has become the nerve center of society and organizations.With the development of next-generation information technology,Mis has gradually entered the smart period.However,research on smart managementinformation systems(SMIS)is still limited,lacking systematic summarization of its conceptual definition,evolution,research hotspots,and typical applications.Therefore,this paper defines the conceptual characteristics of SMIS,provides an overview of the evolution of SMIS,examines research focus areas using bibliometric methods,and elaborates on typical application practices of sMis in fields such as health care,elderly care,manufacturing,and transportation.Furthermore,we discuss the future development directions of SMIS in four key areas:smart interaction,smart decisionmaking,efficient resource allocation,and flexible system architecture.These discussions provide guidance and a foundation for the theoretical development and practical application of SMIS.

Client-Centric Adaptive Scheduling of Service-Oriented Applications

The paper proposes a client-centric computing model that allows for adaptive execution of service-oriented applications. The model can flexibly dispatch application tasks to the client side and the network side, dynamically adjust an execution scheme to adapt to environmental changes, and thus is expected to achieve better scalability, higher performance and more controllable privacy. Scheduling algorithms and the rescheduling strategies are proposed for the model. Experiments show that with the model the performance of service-oriented application execution can be improved.

User friendly preferential private recommendation

To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework(named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection.And users’ preference is utilized at the user side to improve recommendation performance without increasing users’ burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed.The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.