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Smart city applications is not just limited to the use of technology in building better cities and improving the citizens’ quality of lives. In reality, it is capable of building comprehensive solutions for the development of sustainable communities, irrespective of being urban or rural. Different communities will have their own dynamics and complexity, some communities are widespread and distributed, while some are more dense and concentrated. Each of these characteristics can either become an advantage or a barrier while developing solutions for these communities, which can be addressed with the proper use of technology. Internet of things (IoT) and wireless sensor networks (WSN), are the technologies in particular that can help in building comprehensive solutions for creating sustainable communities as they have a wide variety of applications such as disaster management, healthcare, energy, water, surveillance, structural health monitoring and environmental monitoring. This paper describes about four major research works in smart city application areas such as (a) design and deployment of an IoT system for early warning of landslides, (b) heterogeneous wireless communication design and optimization for enabling real-time management of interoperation of heterogeneous devices and data services in a hybrid electrical network, (c) development of (low-cost) IoT sensors for remote patient monitoring and (d) design and deployment of systems and solution for providing mobile offshore connectivity for fishing vessels—internet over sea. This paper details the current state of the art, challenges to be solved, and the methodology that we have adapted for developing solutions in all these research areas. The extensive research in IoT, WSN and its applications has resulted in receiving the award, titled “World Center of Excellence in Landslide Risk Reduction” and five patents, namely (a) network-based system for predicting landslides and providing early warnings (b) mobile infrastructure for coastal region offshore communications and networks (c) wearable wireless tongue controlled devices (d) Systems, methods, and devices for remote health monitoring and management (e) Systems, methods, and devices for remote health monitoring and management using internet of things sensors

With recent development in vehicular communication technologies, much attention has been paid to data dissemination in vehicular networks. In particular, the infrastructure-to-vehicle (I2V) communication is one of the primary technologies to provide a variety of information services. To enhance the bandwidth efficiency of I2V communication, this work considers in a software-defined vehicular networks (SDVN), aiming at exploiting synergistic effects of network coding and vehicular caching. First, we consider a data service scenario in which roadside unites (RSUs) are connected with the controller, which exercises scheduling decisions based on service requests received from vehicles. On this basis, we formulate a cooperative coding and caching scheduling problem with the objective of maximizing the bandwidth efficiency of I2V communication. Then, we propose a binary particle swarm optimization (BPSO)-based coding scheduling (BPSO_CS) algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation. The results conclusively demonstrate the superiority of the proposed solution.

With the rapid development of service-oriented computing, a large number of software applications have been developed based on the services computing framework. It is well known that software engineering is a knowledge-intensive activity, and thus the effective management of service-related knowledge facilitates service-oriented software development. Although many methodologies have been proposed for service-oriented knowledge management, little attention has been paid to mining knowledge (especially domain-specific functionalities) from service resources. To address this issue, we propose an approach to mine domain knowledge on service goals (i.e., service functionalities) from textual descriptions of services. The approach consists of two components: service goal extraction from textual service descriptions based on linguistic analysis and domain service goal construction that merges semantically similar service goals within a domain. The effectiveness of the proposed approach is validated by a series of experiments conducted on a real-world dataset crawled from the ProgrammableWeb.

In today's data centers, memory-based key-value systems, such as Memcached and Redis, play an indispensable role in providing high-speed data services. The rapidly growing capacity and quickly falling price of DRAM memory in the past years have enabled us to create a large memory-based key-value store, which is able to serve hundreds of Gigabytes to even Terabytes of key-value data all in memory. Unfortunately, CPU cache in modern processors has not seen a similar growth in capacity, still remaining at the level of a few dozens of Megabytes. Such an extremely low cache-to-memory ratio (less than 0.1%) poses a significant new challenge---the limited CPU cache is becoming a severe performance bottleneck that hinders us from fully exploiting the great potential of high-speed memory-based key-value stores. To address this critical challenge, we propose a highly cache-efficient scheme, called Cavast , to optimize the cache utilization of large-capacity in-memory key-value stores. Our goal is to maximize cache efficiency and system performance without any hardware changes. We first present two light-weight, software-only mechanisms to enable user to indirectly control the cache content at application level. Then we propose a set of optimization policies to address several critical design issues that impair cache's efficacy in the current key-value store systems. By carefully reorganizing the data layout in memory, redesigning the hash indexing structure, and offloading garbage collection, we can effectively improve the utilization of the limited cache space. We have developed a module in Linux as a kernel-level support, and implemented two prototypes based on Memcached and Redis with the proposed Cavast scheme. Our experimental studies show promising results. On a 6-core Intel Xeon processor with only 15-MB cache, we can raise the cache hit ratio up to 82.7% with a very small cache-to-memory ratio (0.023%), and significantly increase the key-value system throughput by a factor of up to 4.2.

Purpose Research data publishing is today widely regarded as crucial for reproducibility, proper assessment of scientific results, and as a way for researchers to get proper credit for sharing their data. However, several challenges need to be solved to fully realize its potential, one of them being the development of a global standard for links between research data and literature. Current linking solutions are mostly based on bilateral, ad hoc agreements between publishers and data centers. These operate in silos so that content cannot be readily combined to deliver a network graph connecting research data and literature in a comprehensive and reliable way. The Research Data Alliance (RDA) Publishing Data Services Working Group (PDS-WG) aims to address this issue of fragmentation by bringing together different stakeholders to agree on a common infrastructure for sharing links between datasets and literature. The paper aims to discuss these issues. Design/methodology/approach This paper presents the synergic effort of the RDA PDS-WG and the OpenAIRE infrastructure toward enabling a common infrastructure for exchanging data-literature links by realizing and operating the Data-Literature Interlinking (DLI) Service. The DLI Service populates and provides access to a graph of data set-literature links (at the time of writing close to five million, and growing) collected from a variety of major data centers, publishers, and research organizations. Findings To achieve its objectives, the Service proposes an interoperable exchange data model and format, based on which it collects and publishes links, thereby offering the opportunity to validate such common approach on real-case scenarios, with real providers and consumers. Feedback of these actors will drive continuous refinement of the both data model and exchange format, supporting the further development of the Service to become an essential part of a universal, open, cross-platform, cross-discipline solution for collecting, and sharing data set-literature links. Originality/value This realization of the DLI Service is the first technical, cross-community, and collaborative effort in the direction of establishing a common infrastructure for facilitating the exchange of data set-literature links. As a result of its operation and underlying community effort, a new activity, name Scholix, has been initiated involving the technological level stakeholders such as DataCite and CrossRef.

Research data management is an important topic for funding agencies, universities and researchers. In this context, the main aim of this study is to collect preliminary information for Aperta, which is being developed by the Scientific and Technological Research Council of Turkey, to fulfil the following goals: determine the research data management awareness levels of researchers in Turkey; understand current research data management practices in their research environments; and find out their experiences of policy issues. For this, a questionnaire was distributed to 37,223 researchers, with 1577 researchers completing it. The results indicated that researchers who spend more time with data have more concerns about data management issues. The levels of experience of creating a data management plan were quite low. The importance of this study lies in how it is able to show the current research data management practices of Turkish scholars during the new repository’s foundational development stage.

With the widespread of Internet-of-Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders, such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this article proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their data set to the data service provider as a noncooperative game model. Based on the Nash equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

Brain informatics adopts a systematic methodology to study clinical diagnosis and pathology of mental disorders from macro, meso and micro points of view. Systematic studies produce multi-level and multi-aspect health big data of mental disorders. Effectively integrating multi-level mental health big data and providing multi-level and content-oriented data services for different types of users by an open and extendable mode becomes new requirements on the acquisition and computing of mental health big data. The existing Data-Brain-based brain data center cannot meet these new requirements. This paper proposes a brain and health big data center and develops a content-oriented cloud service architecture, namely, wisdom as a service, for realizing the brain and health big data center. The illustrative example demonstrates significance and usefulness of the proposed approach.

As novel healthcare paradiagm, smart healthcare can provide more efficient and high quality medical services for patients. However, smart healthcare needs patients to share their physiological information for online diagnoses, if the data sharing system of smart healthcare lacks effective security mechanisms, these sensitive information might be abused by illegal or malicious users. Moreover, smart healthcare needs to confront some brand-new challenges, such as resource-constrained IoT things, identity theft attacks and insider attacks. To tackle these problems, we propose a SDN-based security enforcement framework for data sharing systems of smart healthcare. In our framework, each patient has a dedicated virtual machine in data sharing system, each virtual machine provides a group data services which can be released to those authorized service consumers or IoT things. In additon, virtual machine is protected by the SDN-based gateway which provides a firewall mechanism and guarantees only authorized things can access patient’s virtual machine. Since each thing has a unique MAC address, thus our framework can effectively authenticate resource-constrained IoT things and tackle the problems caused by identity theft. To validate the effectiveness and feasibility of our framework, we implement an experimental system using POX controller and Mininet emulator. The experimental results illustrate our framework is effective under different test scenarios. As increasing the scale of information flow model, the framework can still work well and its performance can be still acceptable.

Due to its high speed and low latency, D2D communication plays an important role in providing proximity data services in 5G systems, which may contain privacy sensitive data. Covert communication is useful to protect the privacy of user data against adversaries. In a D2D underlaying cellular network, an antenna array can be used at a base station (BS) to transmit artificial noise to confuse adversaries that try to detect D2D covert signals. Based on the numbers of antennas at BS, two covert schemes are presented in this work and their performances are evaluated in terms of D2D covert throughput, i.e., maximum achievable D2D data rate with the given covertness requirements. As only channel distribution information (CDI) of adversary links is known to the BS, the average minimum error probability (AMEP) is used as a metric to measure the covertness performance. In the proposed schemes, closed-form expressions of the minimum AMEP and achievable D2D data rate are derived. It is shown that the derivation of minimum AMEP can be simplified if either one or massive antennas are allocated for sending artificial noise. The analytical results are compared to Monte-Carlo simulation results to verify the feasibility of the proposed schemes. It is also revealed that covert throughput can be further improved by setting system parameters properly, e.g., the number of antennas at BS and artificial noise transmit power.

Cloud computing provides an efficient and convenient platform for cloud users to store, process and control their data. Cloud overcomes the bottlenecks of resource-constrained user devices and greatly releases their storage and computing burdens. However, due to the lack of full trust in cloud service providers, the cloud users generally prefer to outsource their sensitive data in an encrypted form, which, however, seriously complicates data processing, analysis, as well as access control. Homomorphic encryption (HE) as a single key system cannot flexibly control data sharing and access after encrypted data processing. How to realize various computations over encrypted data in an efficient way and at the same time flexibly control the access to data processing results has been an important challenging issue. In this paper, we propose a privacy-preserving data processing scheme with flexible access control. With the cooperation of a data service provider (DSP) and a computation party (CP), our scheme, based on Paillier's partial homomorphic encryption (PHE), realizes seven basic operations, i.e., Addition , Subtraction , Multiplication , Sign Acquisition , Absolute , Comparison , and Equality Test , over outsourced encrypted data . In addition, our scheme, based on the homomorphism of attribute-based encryption (ABE), is also designed to support flexible access control over processing results of encrypted data. We further prove the security of our scheme and demonstrate its efficiency and advantages through simulations and comparisons with existing work.

The Italian Research Conference on Digital Libraries (IRCDL) is the annual Italian forum to discuss research topics on Digital Libraries and related technical, practical, and social issues. Along the years, IRCDL touched several aspects underlying the ?Digital Library" domain and promptly adapted to the evolution of the field. Today, the ?Digital Library" field includes theory and practices reflecting the evolution of the role of libraries in the scholarly communication domain, and also embracing scholarly communication and open science. The theme of IRCDL 2019 was ?Digital Libraries: Supporting Open Science". Three main reasons motivated this theme: (i) science is increasingly becoming digital, meaning that research is performed using data services and digital tools; (ii) the results of the research are no longer just traditional scientific publications; (iii) the outcomes of science are increasingly encompassing datasets, software, and experiments. As digital artifacts, such products can be shared and re-used together with the article, thus enabling comprehensive research assessment and various degrees of reproducibility of science. Positive consequences of this shift towards Open Science are: accelerating science, optimizing the cost of research, fraud detection, and fullyfledged scientific reward. Digital Libraries are central in the evolution of research outputs by targeting findability, preservation, interlinking, and re-use of research products and by integrating the components of the scholarly communication process. The conference has been organized in Pisa, and the proceedings are published in the Springer CCIS series Vol. 988 [22]. Pre-print versions, research datasets, and research software relative to the accepted contributions are accessible via Zenodo.org.

In the world of IT technology is growing rapidly, with the rapid development of these specialized companies engaged in the procurement of bandwidth must always conduct searches about IT technology in the field of network services. And the flow of highly needed data or information exchange continues to increase, one of which is via e-mail. In small companies that do not have their own e-mail server or email server backup, they look for services to hosting, network or service companies engaged in data services. Therefore PT NAP Info Lintas Nusa wishes to improve services by adding new services, namely the Email server service. This service is intended for companies that do not have their own e-mail server or companies that want to back up their e-mail. In this case PT NAP Info Lintas Nusa does not yet have specific criteria to determine what operating system will be used that can support the running of the service. So the company decided to use the AHP method with expert choice to determine what criteria would be chosen so as to determine the alternative to be chosen.

As flash-based solid-state drive (SSD) becomes more prevalent because of the rapid fall in price and the significant increase in capacity, customers expect better data services than traditional disk-based systems. However, the order of magnitude performance provided and new characteristics of flash require a rethinking of data services. For example, backup and recovery is an important service in a database system since it protects data against unexpected hardware and software failures. To provide backup and recovery, backup/recovery tools or backup/recovery methods by operating systems can be used. However, the tools perform time-consuming jobs, and the methods may negatively affect run-time performance during normal operation even though high-performance SSDs are used. To handle these issues, we propose an SSD-assisted backup/recovery scheme for database systems. Our scheme is to utilize the characteristics (e.g., out-of-place update) of flash-based SSD for backup/recovery operations. To this end, we exploit the resources (e.g., flash translation layer and DRAM cache with supercapacitors) inside SSD, and we call our SSD with new backup/recovery functionality BR-SSD. We design and implement the functionality in the Samsung enterprise-class SSD (i.e., SM843Tn) for more realistic systems. Furthermore, we exploit and integrate BR-SSDs into database systems (i.e., MySQL) in replication and redundant array of independent disks (RAID) environments, as well as a database system in a single BR-SSD. The experimental result demonstrates that our scheme provides fast backup and recovery but does not negatively affect the run-time performance during normal operation.

Vehicular communication networks have emerged to enable numerous vehicular data services and applications. Conventional vehicular ad hoc networks (VANETs) are often operated in the ad hoc mode and mainly focus on road safety applications based on the connection between vehicles and roadside units (RSUs). To support vehicular communications, dedicated shortrange communication (DSRC) and car-to-car communication consortium (C2C-CC) have been initiated in the United States and Europe, respectively. With the new era of the Internet of Things (IoT), the conventional VANETs have evolved to the Internet of Vehicles (IoV). In IoV, each vehicle is envisioned as an intelligent object, equipped with sensing platforms, computing facilities, control units, and storages and is connected to any entity (other vehicles, RSUs, charging/gas stations, cloud, and so on) via vehicle-to-everything (V2X) communications. Intelligent vehicles can take different roles, i.e., being both a client and a server, taking and providing big data services, leading to numerous new IoV applications, from assisted/autonomous driving and platooning, secure information sharing and learning to traffic control and optimization.

Smartphones and other connected devices rely on data services, such as Web Services (WS), Google Cloud Messaging (GCM) and Firebase Cloud Messaging (FCM), to share the information they collect or use. Traditionally, these services were classified according to the average number of bytes transmitted or their delivery time. However, when dealing with battery-operated devices, another important parameter to be taken into account is their power consumption. Furthermore, software designers and developers often do not consider the efficiency of a data communication system, but are simply concerned about ease-of-use and response time. In this paper, we compare FCM, GCM and two types of WS, namely Simple Object Access Protocol (SOAP) and REpresentational State Transfer (REST) WS in terms of delay, data efficiency, and power consumption. The final outcome is that RESTful WS outperforms all others, making GCM and FCM a viable alternative only when the amount of data to be transmitted is very limited, or when the mobile application requires the advanced services offered by FCM or GCM only. Keywords: Web Services, Google Cloud Messaging, Firebase Cloud Messaging, REST, SOAP

These days Cloud Computing is in demand and the latest technology used by the organisation to handle large amount of data. This paper will explain the concept of Cloud Computing, its models, advantages, disadvantages and also the challenges. The type of testing use for cloud computing will also be discussed in this paper. The organisations may have their own cloud data centre or also can tie up with other data service providers who provides the facility of cloud computing. The data is shared among various servers and then can be accessed from there. It helps to increase the efficiency of the business organisation.

With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.

The NASA open data policy and the increases in data volume from recent and future missions have resulted in a need to re-evaluate data archive information systems and services. For example, the Surface Water Ocean Topography mission (SWOT) will produce $\sim 15$ TB of data every day that needs to be freely and openly accessible through the NASA Physical Oceanography Distribute Active Archive Center (PO.DAAC). Because of the computational, and distribution challenges associated with making such volumes of Earth Observation data publicly available, the NASA Earth Science Data Information System (ESDIS) is moving their archive systems, like PO.DAAC, to the cloud. To facilitate this migration, system requirements need to accommodate expected use. As such, the objective of this study was to assesses expected user needs and develop a quantitative framework that can be traced to requirements for the cloud-based data archive system architecture and services that enable data discovery, access and utility. We used a two-tier data gathering method that included a short survey of potential users (identified at professional meetings and community listservs) and in-depth interviews. The survey provided a means for scaling findings from the in-depth interviews to a more representative sample, thus enabling assessment of expected impact to various user communities for different services that could be provided. This framework enables traceability for setting priorities for data services development to requirements for a cloud-based data archive system and services that enable data discovery, access and utility.

Research data currently face a huge increase of data objects with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.

Computing in the Fog and at the Edge of the Internet of Things represent the greatest bilateral data exchange in the lives of others since the advent of the telephone. It may offer more than improved response times and reduced latency in home and personal data services. It may reintroduce user and community control over the data lives generated by remote systems introduced by the Internet of Things. At the same time the invasive risks it presents to home and personal security and privacy may increase regulatory complexity to protect those rights. External sensing of human activity through the Smart City, home and personal IOT devices with locational data introduces many complex issues, beneficial and dangerous. These issues are resolved in a legal-technical-social milieu. Together technical and legal controls for Fog and Edge computing may mediate the data avatars we produce and protect privacy, safety personal autonomy. Those controls must be in harmony, directed towards the protection of the people, and subject to sanctions for failure to comply.

Based on advances in image processing technology and Web-enabling technologies for mobile devices, mobile Augmented Reality (AR) and Virtual Reality (VR) has developed rapidly. The rendering and interaction of 3D models is an important part of AR and VR applications and is closely related to user experience. However, since the existing WebGL 3D JavaScript libraries for Web-based mobile 3D (represented by three.js and babylon.js) load the entire model file at once, large-size 3D models with complex interactions cannot be rendered smoothly due to limited data transmission, the weak computation capabilities of mobile Web browsers, and the latency of 3D model rendering. In this paper, we first propose model-animation data separation and an on-demand loading mechanism to improve the data request and loading process of Web 3D models. The main mechanisms are the following: (1) The model data are segmented into topological data and animation data sequences, and only the necessary data of the model are loaded when the Web-based mobile 3D model is first rendered. (2) The 3D model animation data sequence is semantically decomposed, and a multigranular model animation data service is established to provide continuous animation data support. (3) An asynchronous request-response mechanism is used to optimize the loading method of the model data. The model rendering mechanism uses an on-demand request and rendering method to transform the centralized loading process of the 3D model into a decentralized process. According to the testing and verification results, this optimization method can reduce the latency of mobile Web 3D in model data transmission and rendering by 24.72% for the experiment models. The interaction experience of Web-based mobile AR and VR is substantially improved relative to existing Web 3D rendering engines and rendering mechanisms, especially in complex interactive service scenarios.

Twitter is one of the most comprehensive sources of public conversations over the world. As a platform, it provides a medium to a large group of audience to express their views and opinions. Owing to the snowballing of the cheaper smartphone market coupled with cheaper data services, the platform has experienced an enormous increase in its use and has become a good medium of influence. Hence, Twitter data i.e. around 1.1 lakh tweets over a period of three months (January to March, 2019) are used to extract the tweets surrounding the discussion of future of work which is the result of the emerging technologies vis-a-vis automation, IoT and Industry 4.0. to understand the perceptions of people, organizations and businesses regarding the same. Furthermore, themes surrounding the discussions are identified and technology enablers that are perceived to bring about a change in the future of work. The social media discussions can strongly influence the opinions and perceptions of people, businesses or organizations regarding Future of Work. The results of sentiment analysis exhibit that there is not much negative sentiment regarding the change in nature of work. Artificial Intelligence and Robotics are identified as the main technology enablers.

Research infrastructures available for researchers in environmental and earth science are diverse and highly distributed; dedicated research infra-structures exist for atmospheric science, marine science, solid earth science, biodiversity research, and more. These infrastructures aggregate and curate key research datasets and provide consolidated data services for a target re-search community, but they also often overlap in scope and ambition, sharing data sources, sometimes even sites, using similar standards, and ultimately all contributing data that will be essential to addressing the societal challenges that face environmental research today. Thus, while their diversity poses a problem for open science and multidisciplinary research, their commonalities mean that they often face similar technical problems and consequently have common requirements when addressing the implementation of best practices in curation, cataloguing, identification and citation, and other related core topics for data science.In this chapter, we review the requirements gathering performed in the context of the cluster of European environmental and earth science research infrastructures participating in the ENVRI community, and survey the common challenges identified from that requirements gathering process.

The growing demand for a fast and cheap Internet access, especially via mobile devices, significantly changes the perception for the Internet services provided by the mobile operators. Easy access to the Internet “world” through the mobile technologies like LTE-Advanced provides full support of different types of services and applica- tions which have strict requirements regarding network performance, bandwidth and speeds. This is a big challenge for the mobile operators in order to provide adequate support for all the users and various types of services. On the other hand, the users expect certain quality levels to be maintained. This paper describes and gives examples of the main QoE indicators measured from live mobile networks, how to optimize them and how to come close or achieve the apex of the current mobile networks performance.

In the era of rapid data expansion and computer technology development, discrete storage, multiband push and fuzzy query remote sensing data management methods are no longer suitable for the data analysis needs of users, including the needs for long time series, global regions and multidata fusion. After analyzing traditional data management techniques, this paper discusses the existing achievements and development trends of current technologies. This paper aims to solve the problem of data sharing difficulties and organizational inconsistency caused by the use of different formats for the same spatial object. Based on a discrete global grid, this paper studies the blocky division method and coding specification of Google S2 and then accomplishes the layered storage of remote sensing data in HBase. Finally, Kylin is used to build a cube model to discuss the information mining analysis changes in the new data management model. Experiments show that the blocky and layered management schema (BLMS) can realize the unified management of global remote sensing data with multisource, heterogeneous, multiscale, and long-term characteristics and provide accurate data services on demand.

Public safety networks (PSNs) refer to dedicated telecommunication networks used by public safety agencies, for instance, police, fire, emergency, and many more for critical communications. It is clear that the evolution of PSNs from terrestrial trunk radio (TETRA), TETRAPOL, and ARCP Project 25 toward Long Term Evolution (LTE) based systems have shown effectiveness and efficiency. This chapter looks at the challenges PSNs face by developing solutions for data rates such as introducing broadband data services into safer communication. It provides a discussion on the integration of technologies, for example, cellular LTE and how such networks can be upgraded using software defined networking and network function virtualization. This softwarization is part of the solutions to the issues raised with commercial LTE and how PSN communications can be enhanced. The potential of these technologies is associated with a list of important characteristics such as rapid deployment, reliability, security, and dynamic resource management.

SILADDU or the Integrated Data Services Information System is an application to record data of the population of a village. This application is expected to help village officials in administering the settlement. In addition, the application is expected to become the reference in government programs such as direct assistance from the government in providing information about who will be given help, because this application helps classify the population data based on the level of economic ability, educational level, marital status, age, gender and others. This application is relatively new so attracted me as a researcher to examine the level of user satisfaction. Thus the researchers conducted this study in order to become one of the source application developers have to pay attention to the satisfaction of his application.

The centralized zone data service (CZDS) was introduced by the Internet Corporation for Assigned Names and Numbers (ICANN) to facilitate sharing and access to zone data of the new generic Top-Level Domains (gTLDs). CZDS aims to improve the security and transparency of the naming system of the Internet. In this paper, we investigate CZDS’s transparency by measurement and evaluation. By requesting access to zone data of all gTLDs listed in the CZDS portal, we analyze various aspects of CZDS, including access status, responsiveness and provided reasons for granting access or denial. Among other findings, we find that while a large percent of the gTLD admins respond within a reasonable time, more than 10% of them have a long request-to-decision waiting time, and sometimes requests go unanswered even after six months of a request. Furthermore, we find that denial cases were for unjustified reasons, where administrators who denied the requests have asked for information that was already provided in the request form. We discuss implications, and how to enforce better outcomes of CZDS using insight from our measurement and evaluation.

The IoT network allows IoT devices to communicate with other devices, applications, and services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC, emerging for alleviating high latency data services in cloud computing framework plays an important role in the IoT network. Network performance and intelligence can be improved by integrating cognitive and cooperative mechanisms in the MEC framework. However, the QoS of computation-intensive tasks may degrade because of the limited available computational resources in MEC servers. Moreover, the characteristics of resources belonging to MEC servers and cloud servers are commonly different. In order to optimize the strategy of resource assignment, the tasks of assigning the limited computational resources in MEC servers and resolving the high latency problem in cloud servers have attracted growing interest from researchers. In this article, we propose a joint optimization paradigm for task-driven resource assignment based on evolutionary computation considering the power consumption and computation/communication delay simultaneously. The MEC framework consists of MEC servers, mobile devices, and cloud servers, and offloads the computational resources to the edge of end users. Additionally, we introduce and analyze three typical task-driven cases, which are the server-determined condition, server-flexible condition, and server-uncertain condition, respectively. Finally, we present the existing technical challenges and discuss the open research issues.

Telekom Malaysia Berhad (TM), Malaysia’s Convergence Champion and No. 1 Converged Communications Services Provider offers a comprehensive suite of communication services, mobility, content, Wi-Fi and smart services. As a market leader, TM is driven by stakeholder value creation in a highly competitive environment.(https://www.tm.com.my/Pages/Home.aspx) Telekom Malaysia Berhad also has TM GLOBAL as one of their global company, focusing on domestic and international wholesale business, offering a comprehensive suite of cutting-edge communication services and solutions in connectivity and beyond. Recently, based on our research Telekom Malaysia Berhad’s net profit fell in the second quarter ended June 30, 2018 on lower earnings from its voice and data services. In a filing with Bursa Malaysia today, the fall in voice and data services' profits was affected by the provision recognised for reduction in revenue in view of regulatory mandated access pricing.(New Straits Times)

Purpose The purpose of this paper is to understand the landscape of data curation services among public and academic libraries in the USA, with a focus on library capacity for providing data curation services. Design/methodology/approach The authors conducted an online survey by employing stratified sampling from the American Library Directory. A total of 198 responses were analyzed. Findings The authors’ findings provide insight into the current landscape of libraries’ data curation services. The survey participants evaluated six capacity dimensions for both public and academic libraries – value, financial, administrative, technical infrastructure, human resources and network. The ratings the participants gave to these capacity dimensions were significantly different between academic and public libraries. Practical implications This study suggests several areas in which libraries will benefit from further developing their capacity to successfully run data curation services. Originality/value This is among the first research study to address the concept of capacity in the context of libraries’ data curation services.

Purpose The purpose of this paper is to quantitatively examine factors of trust in data reuse from the reusers’ perspectives. Design/methodology/approach This study utilized a survey method to test the proposed hypotheses and to empirically evaluate the research model, which was developed to examine the relationship each factor of trust has with reusers’ actual trust during data reuse. Findings This study found that the data producer (H1) and data quality (H3) were significant, as predicted, while scholarly community (H3) and data intermediary (H4) were not significantly related to reusers’ trust in data. Research limitations/implications Further disciplinary specific examinations should be conducted to complement the study findings and fully generalize the study findings. Practical implications The study finding presents the need for engaging data producers in the process of data curation, preferably beginning in the early stages and encouraging them to work with curation professionals to ensure data management quality. The study finding also suggests the need for re-defining the boundaries of current curation work or collaborating with other professionals who can perform data quality assessment that is related to scientific and methodological rigor. Originality/value By analyzing theoretical concepts in empirical research and validating the factors of trust, this study fills this gap in the data reuse literature.

The data traffic that is accumulated at the Macro Base Station (MBS) keeps on increasing as almost all the people start using mobile phones. The MBS cannot accommodate all user’s demands, and attempts to offload some users to the nearby small cells so that the user could get the expected service. For the MBS to offload data traffic to an Access Point (AP), it should offer an optimal economic incentive in a way its utility is maximized. Similarly, the APs should choose an optimal traffic to admit load for the price that it gets from MBS. To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading, Software Defined Networking (SDN) assisted Stackelberg Game (SaSG) model is proposed. In this model, the MBS selects the users carefully to aggregate the service with AP, so that the user experiencing least service gets aggregated first. The MBS uses the Received Signal Strength Indicator (RSSI) value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi. Each player involved in the game tries to maximize their payoff utilities, and thus, while incorporating those utilities in real-time scenario, we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience (QoE). Thus, the proposed SaSG model proves better when compared with other game theory models, and hence an optimal data offloading is achieved. Keywords: LTE, WiFi, Aggregation, Software Defined Networking, Stackelberg Game

Data infrastructure systems such as the National Aeronautics and Space Administration (NASA) Planetary Data System (PDS), European Space Agency (ESA) Planetary Data Archive (PSA)and Japan Aerospace Exploration Agency (JAXA) Data Archive and Transmission System (DARTS) archive large amounts of scientific data obtained through dozens of planetary exploration missions and have made great contributions to studies of lunar and planetary science. Since China started lunar exploration activities in 2007, the Ground Research and Application System (GRAS), one of the five systems developed as part of China’s Lunar Exploration Program (CLEP) and the Planetary Exploration of China (PEC), has gradually established China’s Lunar and Planetary Data System (CLPDS), which involves the archiving, management and long-term preservation of scientific data from China’s lunar and planetary missions; additionally, data are released according to the policies established by the China National Space Administration (CNSA). The scientific data archived by the CLPDS are among the most important achievements of the CLEP and PEC and provide a resource for the international planetary science community. The system plays a key and important role in helping scientists obtain fundamental and original research results, advancing studies of lunar and planetary science in China, and improving China’s international influence in the field of lunar and planetary exploration. This paper, starting from CLEP and PEC mission planning, explains the sources, classification, format and content of the lunar and Mars exploration data archived in the CLPDS. Additionally, the system framework and core functions of the system, such as data archiving, management and release, are described. The system can be used by the international planetary science community to comprehensively understand the data obtained in the CLEP and PEC, help scientists easily access and better use the available data resources, and contribute to fundamental studies of international lunar and planetary science. Moreover, since China has not yet systematically introduced the CLPDS, through this article, international data organizations could learn about this advanced system. Therefore, opportunities for international data cooperation can be created, and the data service capability of the CLPDS can be improved, thus promoting global data sharing and application for all humankind.

This study describes the process of persona-creation in a liberal-arts university well-integrated within a global network of Geographic Information System researchers. We introduce the concept of personas as an effective user experience research methodology with potential applications to academic library services. We detail the collection of data from high-level researchers as a way to inform the library’s strategic planning and programming. We present the results of the study in the form of three personas: Rachel the Novice, A.J. the Professor, and Jean-Paul the Big-Thinker. These results are specific to our institution, but the method itself can transfer to other contexts. We note the study’s limitations in the final analysis, and make suggestions for maintaining and refining the personas to accurately represent the community of users.

Nowadays, many organizations store and publish their data and services based on the Cloud Computing paradigm. In this scenario, cloud consumers access these resources anytime and anywhere. Software as a Service (SaaS) and Data as a Service are examples of cloud services. While DaaS delivers and manages data on-demand, SaaS is a delivery model of applications in a cloud environment. However, the vast amount of social data and applications enable different formats of DaaS, such as non-structured (e.g., text), semi-structured (e.g., JSON), and structured format (e.g., Relational Database). The lack of standardization makes users dependent on a system due to the lack of interoperability among different providers. Interoperability is heterogeneous systems' ability to communicate transparently, and it is classified into syntactic, semantic, and pragmatic levels. Middleware for SaaS and DaaS (MIDAS) is a solution to provide interoperability among cloud services. Although the latest version of MIDAS promotes a semantic approach, pragmatic aspects are not addressed. This paper enhances MIDAS to provide pragmatic interoperability in a cloud environment. Our approach presents the necessary elements that MIDAS must consider to provide pragmatic interoperability among cloud services. We conduct a set of experiments to validate our pragmatic MIDAS. We evaluate the overhead of our approach, the correctness of our novel MIDAS, and the effort to implement the MIDAS middleware with dynamic pragmatic information. Results evidence that our approach is towards pragmatic interoperability among cloud services.

Large bibliographic data sets hold the promise of revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Providing high-quality data services for large network datasets such as the Microsoft Academic Graph, which contains more than two billion citation links, poses significant difficulties for universities. Data systems based on the property graph model are capable of delivering efficient graph query services for large networks. However, real-life queries often combine multiple types of data models. To satisfy the needs of different user groups, we developed and deployed a cloud-based data system consisting of scalable graph and text-indexed query engines. For non-expert users, the property graph model also presents a technological barrier. To alleviate the steep learning curve, we designed an intuitive graphical user interface for query-building. For advanced users, a scalable notebook service in our platform provides a more flexible computing environments where the query results can be further analyzed. These systems form the data-backbone of the Collaborative Archive and Data Research Environment (CADRE), which provides efficient and high-quality bibliographic data services to eleven large public universities in North America.

As various data services are provided to realize Society 5.0, the usage of personal data is estimated to increase along with the explosive increase in data traffic. It is important that the protection of privacy keeps pace with increases in the exchange of data containing personal information. Starting from the enforcement of the General Data Protection Regulation (GDPR), stricter privacy protection regulations are expanding to more countries. These restrictions require that personal data should be hidden or anonymized before they are propagated over the network. The secondary usage of data is assumed in smart communities, and systems that can protect privacy are required for secure network infrastructures. In this study, we propose an edge-based computing platform that manages the privacy of users on the network of a smart community. For the platform, we prepared three models: The Basic, Preceding Packet, and Piggyback models. These are considered OpenFlow models, and the network efficiency for each was evaluated.

Since its introduction in 2011, Trimble’s RTX positioning service has been providing reliable real-time centimeter-level accurate positioning for standalone GNSS receivers. The service is based on the estimation of precise satellite orbits, clocks and signal biases as well as atmospheric models computed from the data of a worldwide network of GNSS reference stations. The correction data is transmitted to the user receiver via L-band from geostationary satellites or via the internet. Using dual-frequency pseudorange and carrier phase observations together with the received correction data, the user can perform absolute cm-level accurate positioning anywhere on Earth. By using all current GNSS an accuracy of 2.5 cm (95%) is achieved after a convergence time of less than 10 minutes (95% of the time) globally or less than one minute when using regional ionospheric and tropospheric corrections available throughout Europe and North America. In recent years, the service has undergone a major rework introducing new features, such as BeiDou-3 support and integrity monitoring that result in a substantial improvement in terms of positioning performance and reliability. In order to improve the overall positioning performance the Chinese BeiDou-3 system comprising 24 MEO and 3 IGSO satellites was added to the service. With its global coverage and excellent observation quality, BeiDou-3 can be considered an ideal addition to the existing RTX service. In order to support the full set of signals, currently defined by BeiDou-3 ICDs the global RTX tracking network was upgraded to Trimble Alloy receivers and the correction data generation was extended to support the additional frequencies. As a consequence the RTX service is providing correction data for the B1C, B2a, B1I and B3I signals. It will be shown that using all available satellites including BeiDou3 as well as additional signals such as L5, E6 and B3 the convergence time of the RTX global service is very significantly reduced by more than 50 %. For stations with good observation data quality convergence times of less than one minute are achieved without any regional augmentation (RTX-Fast). Since some user groups, especially in the automotive sector, are limited to a subset of satellite systems and signals, the service area of the RTX-Fast correction service was continuously extended over the past years and is now covering complete Europe and the US. Since the RTX-Fast corrections contain very accurate atmospheric corrections derived from a much denser reference station network a user reaches cm-level accuracy in less than one minute even when using only a subset of GNSS. In addition, RTX-Fast corrections are integrity monitored both before broadcast and after reception by dedicated integrity receivers across the service area. In case integrity violations are detected in the broadcast correction data or the integrity monitoring is not available the user receives a corresponding alarm message within seconds. From the very beginning the RTX service allowed to handle switches between redundant RTX server instances and between data centers using a dedicated handover mechanism with minimal user impact. Recently, a new way of server switches was implemented that allows completely seamless switching without impacting clients that can not handle the existing handover mechanism, while maintaining the highest degree of robustness and redundancy. During 10 years of operation Trimble’s RTX correction data service has come a long way and is currently serving more than a hundred thousand users around the world 24/7 with excellent availability. With the upcoming 2021 RTX service release another big step in terms of performance and reliability will become available to the customers. Users with BeiDou-3 capable receivers will see a highly improved performance of the global RTX service in terms of convergence time and robustness. Other RTX users can benefit from much extended RTX-Fast coverage areas including a fully deployed integrity monitoring system. Finally, the impact of maintenance server switches on the RTX data stream and therefore the user application will become virtually invisible.

Objective : This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services. Methods : We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library’s RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle. Results : The findings show that the two models’ crosstab framework can provide an overview of an institution’s current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers’ needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users. Conclusion : These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.

Comprehensive datasets for nature-based solutions (NBS), and their diverse relationships have not yet been accumulated into a deployable format. This research describes the development of a novel National Spatial Data Infrastructure (NSDI) system for NBS co-benefits throughout the contiguous United States. Here, we gather and integrate robust geospatial datasets from the social, ecological, environmental, and hydrologic domains using seamless, cloud-based data services to facilitate the trans-disciplinary assessment of NBSs as a function of society and Earth. This research enhances practical decision making and research by assimilating web-based datasets and describing the missing links between national policy and robust adoption of NBSs as a sustainability solution. This NSDI serves to foster participatory planning capabilities and integrate local sustainability goals into decision–support frameworks. Such a platform strengthens the knowledge base necessary for addressing multiple, co-evolving issues of societal relevance, an essential component of fully espousing NBSs within the realm of socio-technological systems and improving policies and implementation regarding sustainable solutions. The efficacy of the proposed platform to serve as a holistic data information system is assessed by exploring important characteristics associated with geospatial NSDI tools, namely, openness, spatial functionality, scalability, and standardization. By placing GIS strengths and weaknesses in the context of transdisciplinary NBSs, we reveal strategic directions toward further co-production of such NSDIs. We conclude with recommendations for facilitating a shared vision of transdisciplinary technologies to strengthen the amalgamation of broad co-benefits and multi-disciplinary influences in sustainability planning.

As modern clothing majors continue to use the advancement of technology to improve the training of professional talents, vocational ability embedded education is the main direction for future education reform and innovation. Vocational ability embedded education mainly uses the school-enterprise cooperation model to tap the industry’s potential and guides students’ learning at school with employment as the orientation. At the same time, it uses cloud data services to realize information sharing between schools and enterprises through the cloud platform, which can better set the development direction for students and improve the practical problem of students’ difficulty in obtaining employment after graduation. This paper mainly explores the construction and practice of professional ability embedded in the clothing major under the cloud data service. In addition, this paper analyzes the current development in the form of questionnaire surveys, and it also explores practical methods and future development trends.

Policy can articulate the scope of work. For repositories that house data, policy can help users manage expectations, especially for individuals who are new to data sharing, or where expectations for sharing data have changed. We cover some of the current literature around the process for writing policy, specifically focusing on policy for data collections and repositories, factors that encouraged us to create a repository policy, our collaborative process for creating the policy, and lessons learned. We hope that others can use our processes to build their own policy that reflects the needs of their campuses and scholars and further moves the needle toward the “Library as Publisher” model.

The success and popularity of cloud computing is obvious from technological advancement. It is gaining confidence of clients by offering cost effective architecture supporting computation transmission and data storage. These trustworthy data services face lots of challenges just because of loss of control over data as well as cloud abstract nature. For handling security related issues, lots of encryption – decryption algorithms have already been designed and implemented. All of pre-existing algorithms require key from the user and then start encryption accordingly. The main objective of this research paper is to design and implement new algorithms that can produce key on sensing data automatically and consequently encrypt data on the basis of key produced. Algorithm1 has been designed into phases, in first phase it senses the data and generates key, in second phase it encrypts the data and in third phase it compresses the data. Algorithm2 has also been design into phases, in first phase it decompresses, in second phase it decrypts the data. Algorithm3 has been designed only. Its implementation is under progress and may be seen in future scope of the paper. For securing data, we have used the concept of encryption-decryption and for efficient utilization of cloud storage we have compressed the data by using GZIP java inbuilt library with ZIP package. Collectively Algorithm1 and Algorithm2 have been entitled as Data Sensitive Algorithm (DSA).

National Institute of Aeronautics and Space of Indonesia is obliged to organize remote sensing data storage and distribution through National Remote Sensing Data Bank (BDPJN). BDPJN is a node of the remote sensing data network in a spatial national data network system. BDPJN has the primary function of collecting, storing, distributing metadata and remote sensing data for Indonesia territory. Evaluation of data operation management is needed to guarantee the continuity of the remote sensing data service at BDPJN. It has to ensure that: the catalog can be accessed by users, the catalog is continuously updated with data stored in the BDPJN storage system, and the data is available when needed. This research is purposed to evaluate data operation management on BDPJN. It is implemented by measuring the maturity level of data operations management activities using the CMMI and DMBOK frameworks. The results indicated that BDPJN has ten activities that have met an expected level of maturity. However, five of fifteen activities have below maturity level 3. Three activities at maturity level 1 are Monitor and Tune Database Performance, Plan for Data Retention, Archive, Retain, and Purge Data. The two others are Define the Data Technology Architecture and Evaluate Data Technology, which is at maturity level 2. Recommendations have been arranged based on references from DMBOK for these five activities.

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.