Efficient Data Services Research Articles

Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review.

With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.

Integrated Management Technology of Geophysics and Geology Data

This paper describes the three main development stages of geophysics and geology data management, analyzes the business and data characteristics faced at each stage, discusses the development trend of integrated geophysical and geological data management with the development of information technology and database technology; analyzes the challenges faced by geophysical and geological data in terms of data collection, data storage and data application, explains the sources and causes of the problems. The concept of integrated management of geophysical and geological data is proposed, based on the data governance framework system, the integrated data management platform is built, covering the construction of data source collection, efficient data services and comprehensive data management, so as to realize unified, efficient and secure data collection, management and application services, the content and characteristics of the main data management strategy of geophysical and geological data are analyzed, and on the basis of the above discussion. Finally, it explores and practices the application of this technology system outside the oil and gas field, proposes the application support scheme of integrated geophysical and geological data management for business, and further demonstrates the value and significance of the wide business application of integrated data management.

TEBDS: A Trusted Execution Environment-and-Blockchain-supported IoT data sharing system

Data sharing services based on massive IoT data have been widely used in various fields such as health monitoring and image recognition, providing users with more reliable, efficient, and flexible data services and significantly improving the user service quality. With the popularization of IoT applications, the usage rate of IoT data is getting higher and higher. Due to the dangerous network environment, the security of IoT data faces challenges. On the one hand, attacks such as data tampering can lead to the failure of IoT tasks, and on the other hand, the intrusion of malicious users can lead to the collapse of the entire IoT network. In recent years, many protection schemes used for IoT data security have been proposed. However, security flaws still exist in these schemes. Therefore, to solve the data security and identity security issues in the IoT data sharing process, in this paper, we propose a TEE-and-Blockchain-supported IoT data sharing architecture(TEBDS), which combines on-chain and off-chain methods to meet the security requirements of the IoT data sharing framework. Therein, the consortium blockchain realizes the protection of on-chain IoT data and the access control of IoT users. An Intel SGX-based Distributed Storage System (SDSS) is proposed to secure off-chain data. Besides, an incentive mechanism is developed to facilitate the whole system. Security analysis shows that TEBDS meets the requirements of data security and identity security. Experimental results show that TEBDS has better performance than the centralized method SPDS.

Fog-to-MultiCloud Cooperative Ehealth Data Management With Application-Aware Secure Deduplication

The healthcare industry faces challenges regarding the security and efficiency of data management for patient health records (PHRs). We propose SafePHR: a secure and efficient medical data service with application aware deduplication in fog-to-multicloud encrypted storage. It introduces a fog-to-multicloud cooperative storage model, which combines low-latency and high-safety local fog with unlimited capacity and built-in disaster recovery in remote multi-cloud to enhance eHealth data management. It also provides an application aware secure deduplication scheme to improve the data traffic and space efficiency of cloud storage for encrypted PHRs using variants of convergent encryption. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a fault-tolerant coding scheme. Compared with the state-of-the-art of cloud-assisted eHealth systems, our experiments demonstrate that SafePHR can ensure data confidentiality of deduplication, achieve a competitive low cloud storage space overhead, and improve system performance with high fault-tolerance capability.

Evaluating a Business Ecosystem of Open Data Services Using the Fuzzy DEMATEL-AHP Approach

The government has formulated and implemented an open data policy to promote administrative transparency and economic development in recent years. Therefore, most previous studies on open data have focused on e-government. Consequently, an open data service industry committed to providing innovative value-added data application services has emerged in Taiwan, with small- and medium-sized enterprises being the driving force. However, in a complex industrial environment, enterprises need to promote efficient data services development by developing a cross-disciplinary business ecosystem cooperatively. Nevertheless, few studies have discussed the open data service industry from the perspective of business ecosystems, making it impossible for enterprises to evaluate the business ecosystem of open data services built by them. In this study, we used the fuzzy analytic hierarchy process and fuzzy decision-making and trial evaluation laboratory methods to construct five evaluation dimensions and thirty-one evaluation criteria. We organized them into an evaluation scale to measure the business ecosystem’s performance of open data services built by enterprises. Then, using the case analysis method, we tested the applicability of the evaluation scale. This study examined the assessment scale of ecosystem construction in the open data industry, from the perspective of a business ecosystem, and analyzed the importance of each key criterion.

Construction of Smart Campus Cloud Service Platform Based on Big Data Computer System

At present, universities are facing a critical period of transition from digital campus to smart campus. Because cloud service data has the characteristics of diversity, in addition to structured data, there are also unstructured data, which play an important role in the study and life of the majority of students. Therefore, it is very important to improve the construction level of smart campus cloud service platform. At this stage, there are great differences between the service platform under the traditional model and the smart campus platform under the big data computer system. Under the traditional mode, the construction method of smart campus cloud service platform in the city has some shortcomings. Therefore, it is urgent to build a smart campus cloud service platform. Based on this, this paper compares the smart campus cloud service platform of the traditional service platform and the big data computer system. The results show that the latter has a timely response speed and can bring high-quality and efficient data services to users. At the same time, it also analyzes the ideas and goals of building an intelligent cloud service platform, and puts forward corresponding construction methods.

Lifespan-Balance-Based Energy-Efficient Routing for Rechargeable Wireless Sensor Networks

The rechargeable wireless sensor networks provide efficient data services for many applications in the Internet of Things (IoT), in which the sensors can be recharged continuously. Wireless recharging technology has been widely studied and applied. Reasonable energy allocation and identification of the nodes which need recharging in each round is key to the technology. According to dynamic recharging planning, it is crucial to improve energy efficiency in networks with excellent Quality of Service (QoS). Hence, a lifespan-balance-based energy-efficient routing is proposed. The optimization problem of maximizing recharging payoff is first designed and it is solved by our heuristic algorithm, which enables dynamic adjustment of the ratio of moving time to recharging time according to energy states of nodes. Then the QoS routing based on multi-hop strategy which integrates multiple related factors is designed. Finally, the indicator which contains energy supplement to quantify lifespan is designed. Combined with our QoS routing, the method of lifespan balance based on the global relay range regulation is proposed to maximize the minimum lifespan of all nodes intelligently. The simulation result shows that our algorithm can improve the energy efficiency with excellent QoS in the studied networks and prolong the network lifetime.

An Intelligent Transaction Migration Scheme for RAFT-Based Private Blockchain in Internet of Things Applications

The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an anomalytriggered data flow in a certain area exceeds the block generation speed. This letter proposes an intelligent transaction migration scheme for RAFT-based private blockchain in IoT applications to migrate transactions in busy areas to idle regions intelligently. Simulation results show that the proposed scheme can apparently reduce the latency in high data flow circumstances.

Pr-CAI: Priority based-Context Aware Information scheduling for SDN-based vehicular network

The Intelligent Transport System (ITS) services recon upon reliable information dissemination among the heterogeneous vehicular network components along the roads. The information sharing among vehicles, pedestrian units and RoadSide Units (RSUs) is paramount in dynamic vehicular network to provide real-time data services in vehicular networks. Due to distinctive vehicular network characteristics, it is extremely challenging to provide efficient data services in heterogeneous traffic scenario. Firstly, the ITS services have different Quality of Service (QoS) constraints which varies in line with application specification. Secondly, there is a need of coordination among network resources in vehicular network. In this work, a Software Defined Network (SDN) based vehicular network model has been presented to provide reliable data services in heterogeneous traffic in urban environment. The Context Aware Information Scheduling (CAIS) problem has been formulated by analysing the characteristics and QoS requirements of ITS services. A Priority based Context Aware Information (Pr-CAI) scheduling algorithm has been proposed to ensure real time data service scheduling, which assigns priority to requests based on service deadline, request selection priority and traffic dynamics. The Pr-CAI has been simulated for different heterogeneous traffic scenarios to analyse its performance. The comprehensive simulation results prove the ascendancy of proposed algorithm with respect to the existing compared algorithms. The Pr-CAI scheduling algorithm ensures real time data service scheduling for different ITS services that maximizes the service ratio and provides fairness amongst users.

SECURING CLOUD DATA AGAINST CYBER-ATTACKS USING HYBRID AES WITH MHT ALGORITHM

Cloud computing is dealing with large amount of data during data communication. This data processing is named as big data. The big data is growth of the demand in accessing the storage, computation and communication. This big data has the major defects. A raising issue in emerging big data is cost minimization. The architecture of big data ranges over multiple machines and cluster which have sub system. The major challenge of this big data is pre-processing and analysing the data patterns. This research article is dealing with different data pre-processing and secure data storage. There are many research challenges during this data process. The possible gap and drawbacks in the technology are identified through this survey and the efficient big data service is provided through MHT and AES algorithm. The main aim of this proposed method is to provide better data security during larger data process. The proposed hybrid MHT with AES algorithm is to minimize the encryption and decryption time apart from that it reduces the attacker ratio. All these parameters automatically increase the Quality of Service.

CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries.

Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) Community–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) Access–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) Data-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) Reproducibility–the platform should be designed to foster and encourage reproducible research; 5) Empowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them.

The development of China national geohazard information system

Geohazards caused significant damages to people and economical properties. The prevention and mitigation of geohazards in China is facing great challenges. To solve the problem, establishing a national geohazard information system is important in accessing the distribution of geohazards, optimizing the efforts of prevention and mitigation, and improving the public awareness and risk reduction capabilities. After two decades’ of development, China national geohazard information system have upgraded from client-server (C/S) to browser-server (BS) architecture, and now to the micro-service based architecture, more than 320 thousands of geohazards which were mainly collected through field surveys and the routine field inspections have been saved in the database; through the information system, data acquisition and integration, classified storage, efficient data processing, real-time statistics and data services have been achieved and standardized by numerous technical specifications. In this paper, the current status of China national geohazard information system, including the development progress, database architecture, system functionality and data applications are introduced, and expected to provide references of establishing an efficient and comprehensive geohazard information system to improve the geohazard risk reduction capabilities for the countries or regions suffering from geohazards.

AdPS: Adaptive Priority Scheduling for Data Services in Heterogeneous Vehicular Networks

Data service scheduling is paramount in realization of Intelligent Transportation System (ITS). ITS services recon upon real-time information sharing among vehicular network components. To provide real time information services in vehicular networks, numerous challenges are imposed due to unique characteristics of such networks. For efficient information dissemination, there is a prime requirement of an adaptive scheduling algorithm for approximate vehicular dynamics information. In this work, a Dedicated Short Range Communication (DSRC) based vehicular communication model has been used for efficient data services in heterogeneous traffic environment. A novel Adaptive Priority data Service scheduling (AdPS) algorithm has been proposed to provide real-time data services, which is based on fuzzy logic request deadline estimation and request prioritization. The prioritized requests has been stored in multilevel queues according to the urgency of requests to provide simultaneous access. The proposed algorithm has been simulated for different traffic scenarios for a real city environment and its performance has been analyzed at different traffic instances. The proposed protocol ensures the real time service scheduling which provides fairness among the users.

Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme

Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.

Bandwidth Efficiency and Service Adaptiveness Oriented Data Dissemination in Heterogeneous Vehicular Networks

Heterogeneous network resources are expected to cooperate with each other to support efficient data services in vehicular networks. However, current data scheduling methods cannot efficiently exploit the benefit of heterogeneous wireless communication interfaces in vehicular networks. In this paper, we propose a software-defined network based service architecture, which enables the scheduler to manage heterogeneous network resources in a centralized way. We analyze the heterogeneity of both data items and networks in terms of data sizes and network features (e.g., cost, transmission rate, coverage, etc.), respectively. On this basis, we formulate a data broadcast and network interface selection (DBNIS) problem, which aims to minimize both the service delay and the network access cost. To tackle the problem efficiently, we propose a coding-assisted multiobjective evolutionary algorithm (CMOEA), which consists of two components: packet encoding and network interface selection. Specifically, for packet encoding, we first develop a packet-size based random linear encoding (PRLE) technique to improve bandwidth efficiency. Then, we theoretically analyze the performance bound of PRLE. For network interface selection, we propose a multiobjective algorithm for network interface selection to adaptively satisfy dynamic requirements with respect to service delay and network access cost by deriving a set of pareto-solutions. Finally, we build the simulation model and implement CMOEA for performance evaluation. The comprehensive simulation results demonstrate the superiority of CMOEA under a wide range of scenarios.

LTE Multimedia Broadcast Multicast Service Provisioning Based on Robust Header Compression

One important issue that confronts network service providers is the need to provide reliable multimedia data service efficiently over cellular networks for a large number of subscribers under dynamic channel conditions. In long term evolution (LTE) networks, multimedia broadcast multicast service (MBMS) is a bandwidth efficient data service to simultaneously support multiple users at high bandwidth efficiency. In this paper, instead of considering spectrum resource allocation, we investigate MBMS provisioning for each mobile user based on the higher layer robust header compression (ROHC) consideration in response to user channel quality to reduce packet losses. We formulate a profit maximization problem for two different MBMS channel models and further propose a new MBMS assignment scheme for each user to be assigned a target MBMS with optimal ROHC parameters. We further develop a dynamic programming algorithm for user assignment and ROHC parameters optimization to achieve maximal profit with high spectrum resource utility. Our numerical results demonstrate substantial profit gain achieved by the proposed method in LTE systems.

Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO.

Nowadays, in the panorama of Internet of Things (IoT), finding a right compromise between interactivity and security is not trivial at all. Currently, most of pervasive communication technologies are designed to work locally. As a consequence, the development of large-scale Internet services and applications is not so easy for IoT Cloud providers. The main issue is that both IoT architectures and services have started as simple but they are becoming more and more complex. Consequently, the web service technology is often inappropriate. Recently, many operators in both academia and industry fields are considering the possibility to adopt the eXtensible Messaging and Presence Protocol (XMPP) for the implementation of IoT Cloud communication systems. In fact, XMPP offers many advantages in term of real-time capabilities, efficient data distribution, service discovery and inter-domain communication compared to other technologies. Nevertheless, the protocol lacks of native security, data confidentiality and trustworthy federation features. In this paper, considering an XMPP-based IoT Cloud architectural model, we discuss how can be possible to enforce message signing/encryption and Single-Sign On (SSO) authentication respectively for secure inter-module and inter-domain communications in a federated environment. Experiments prove that security mechanisms introduce an acceptable overhead, considering the obvious advantages achieved in terms of data trustiness and privacy.

Network-Coding-Assisted Data Dissemination via Cooperative Vehicle-to-Vehicle/-Infrastructure Communications

Vehicle-to-vehicle/vehicle-to-infrastructure (referred to as V2X) communications have potential to revolutionize current road transportation systems with respect to vehicle safety, transportation efficiency, and travel experience. This paper puts the first effort on applying network coding in cooperative V2X communication environments to improve bandwidth efficiency and enhance data service performance. Specifically, we investigate new arising challenges on network-coding-assisted data dissemination by considering both communication constraints and application requirements in vehicular networks. We present the system model and give an insight into the characteristics of cooperative data dissemination with network coding. On this basis, we formulate the problem and propose a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications and exploit their joint effects on providing efficient data services. We design a cache strategy that allows vehicles to retrieve their unrequested data items. This strategy not only increases the opportunity of data sharing among vehicles but also gives higher probability of packet decoding, which in turn enhances the data service performance. We give an intensive analysis on the scheduling overhead, which shows the scalability of the algorithm. Finally, we build the simulation model and conduct a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Structural Summary Tree with Complete Path Representation for Intelligent Query of Social-Web Documents

 Abstract—Compiling documents in extensible markup language (XML) increasingly requires access to data services which provide both rapid response and the precise use of search engines. Efficient data service should be based on a skillful representation that can support low complexity and high precision search capabilities. In this paper, a novel complete path representation (CPR) associated with a modified inverted index is presented for the provision of efficient XML data services, where queries can be versatile in terms of predicates. CPR can completely preserve hierarchical information, and the new index is used to save semantic information. The CPR approach can provide template-based indexing for fast data search. An experiment is also conducted for the evaluation of the CPR approach.

Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks

In this paper, we investigate on-demand data services for high-speed trains via a cellular/infostation integrated network. Service requests and acknowledgements are sent through the cellular network to a content server, while data delivery is achieved via trackside infostations. The optimal resource allocation problem is formulated by taking account of the intermittent network connectivity and multi-service demands. In order to achieve efficient resource allocation with low computational complexity, the original problem is transformed into a single-machine preemptive scheduling problem based on a time-capacity mapping. As the service demands are not known a priori, an online resource allocation algorithm based on Smith ratio and exponential capacity is proposed. The performance bound of the online algorithm is characterized based on the theory of sequencing and scheduling. If the link from the backbone network to an infostation is a bottleneck, a service pre-downloading algorithm is also proposed to facilitate the resource allocation. The performance of the proposed algorithms is evaluated based on a real high-speed train schedule. Compared with the existing approaches, our proposed algorithms can significantly improve the quality of on-demand data service provisioning over the trip of a train.