Integration Of Data Services Research Articles

Blockchain-Based Big Data Integrity Service Framework for IoT Devices Data Processing in Smart Cities

In modern times, smart cities are using advanced technology to control and coordinate business, physical, social and commercial systems to deliver quality services to their residents while ensuring the efficient use of available resources. Many large corporations, as well as government agencies, are involved in building smart cities because smart city activities also require information available from organizations. Blockchain-based technology allows peer-to-peer exchanges as a public logger that records transaction records, smart contracts, agreements, and exports from third party mediators. In the future, smart cities will be equipped with several new technologies, a large amount of data, multiple Internet of Things (IoT) devices, and a unique environment. Data processing among IoT devices in future smart cities is a challenging task. In this paper, the author has proposed the framework of Big Data based blockchain data integrity service to process IoT device data processing in smart cities. The author collected the necessary information from various sources and used three tests to evaluate key performance indicators. The K-mean algorithm was used to separate the data, and a blockchain strategy was used to ensure secure communication between IoT devices. The frame is modelled and tested using up to 100 devices. The proposed framework allows IoT devices to make better use of the information collected from various resources to participate in operations and exchange of information.

Design and Creation of Multi-Source Enduring Linked Assets

IntroductionThe Australian Institute of Health and Welfare is collaborating with a range of government and other institutions to build enduring data assets for improving analysis and informed policy outcomes. There were lessons learnt that can be shared, in addition to the architecture and linkage techniques.
 The enduring assets were created by linking States’ and Territories’ health or welfare data to Commonwealth datasets such as Medicare Consumer Directory (MCD), Residential Aged Care (RAC) and National Death Index (NDI) data. The linkage was carried out by the Australian Institute of Health and Welfare (AIHW) Data Integration Services Centre (DISC).
 Objectives and ApproachTo create the integrated asset, the linkage spine was assembled by de-duplicating and linking MCD and NDI data. The states’ datasets and other commonwealth datasets involved in the project were linked to this spine. Each unique individual in the spine was assigned Personal Project Number (PPN) which was added to each record linked to the individual. The unlinked individuals from these datasets were de-duplicated and assigned different PPNs.
 Names, dates of birth and addresses were used in probabilistic linkage process.
 To enable investigators to interrogate the sequences of the events without releasing the exact dates, the central events file was created. It contains date differences for every event in the asset, calculated as the difference (in days) between event and not released “date zero”, different for each individual.
 ResultsBetween 96% and 99% of records in the supplied datasets were linked to the spine with linkage accuracy at least 98.5%. The linkage rates depends on the data completeness and the nature of the datasets as not all individuals accessing states’ servicers are eligible for Medicare.
 Conclusion / ImplicationsThe person-focused de-identified analytical assets allow to study journeys of the individual through Australian health and welfare systems which transcends jurisdictional boundaries.

Scalable System for Smart Urban Transport Management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.

Blockchain-Based Continued Integrity Service for IoT Big Data Management: A Comprehensive Design

The state-of-the-art centralized Internet of Things (IoT) data flow pipeline has started aging since it cannot cope with the vast number of newly connected IoT devices. As a result, the community begins the transition to a decentralized pipeline to encourage data and resource sharing. However, the move is not trivial. With many instances allocating data or service arbitrarily, how can we guarantee the correctness of IoT data or processes that other parties offer. Furthermore, in case of dispute, how can the IoT data assist in determining which party is guilty of faulty behavior. Finally, the number of Service Level Agreement (SLA) increases as the number of sharing grows. The problem then becomes how we can provide a natural SLA generation and verification that we can automate instead of going through a manual and tedious legalization process through a trusted third party. In this paper, we explore blockchain solutions to answer those issues and propose continued data integrity services for IoT big data management. Specifically, we design five integrity protocols across three phases of IoT operations—during the transmission of IoT data (data in transit), when we physically store the data in the database (data at rest), and at the time of data processing (data in process). In each phase, we first lay out our motivations and survey the related blockchain solutions from the literature. We then use curated papers from our surveys as building blocks in designing the protocol. Using our proposal, we augment the overall value of IoT data and commands, generated in the IoT system, as they are now tamper-proof, verifiable, non-repudiable, and more robust.

A Flexible and Efficient Authentication and Secure Data Transmission Scheme for IoT Applications

Internet-of-Things (IoT) applications have been rapidly deployed into pervasive environment, where both challenges and opportunities abound. On the one hand, a large number of IoT devices and their rich functions contribute significant volumes of data, which has brought tremendous convenience to the daily lives of end users. On the other hand, the heterogeneous IoT devices and a large amount of private information transmitted through networks also bring serious security and privacy issues. It is a big challenge to model IoT systems and trust relationships between different entities with a large number of heterogeneous IoT devices. In this article, we study a general IoT system architecture with consideration of heterogeneous IoT devices. Different trust models are proposed and analyzed based on the trust relationships between different entities in the IoT system. We propose a flexible and efficient authentication scheme with a consideration of heterogeneous IoT devices based on the least trust-required model. The proposed scheme provides security and privacy to resource-limited IoT devices flexibly and efficiently by utilizing IoT devices with better storage and computational ability. Moreover, secure data transmission is presented with contextual privacy and data integrity services. The proposed scheme achieves not only the mutual authentication, initial session key agreement, and data integrity but also anonymity, contextual privacy, forward security, end-to-end security, and key escrow resilience. Security analysis is presented to provide verification of the proposed protocol and security objectives. Moreover, performance evaluation is presented with comparison to the other schemes in terms of security features, computational overhead, and communication overhead. The performance comparisons show that our proposed scheme provides flexible and efficient security by consideration of heterogeneous IoT devices. With the higher proportion of resource-limited IoT devices, our proposed scheme outperforms other similar schemes.

PENGUKURAN KEPUASAN PENGGUNA DAN PENINGKATAN LAYANAN SILADDU (Sistem Informasi Pelayanan Desa dan DataTerpadu)

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.

Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks

Over the past 20 yr, many models have been developed to predict risk for West Nile virus (WNV; Flaviviridae: Flavivirus) disease in the human population. These models have aided our understanding of the meteorological and land-use variables that drive spatial and temporal patterns of human disease risk. During the same period, electronic data systems have been adopted by surveillance programs across much of the United States, including a growing interest in integrated data services that preserve the autonomy and attribution of credit to originating agencies but facilitate data sharing, analysis, and visualization at local, state, and national scales. At present, nearly all predictive models have been limited to the scientific literature, with few having been implemented for use by public-health and vector-control decision makers. The current article considers the development of models for spatial patterns, early warning, and early detection of WNV over the last 20 yr and considers some possible paths toward increasing the utility of these models for guiding interventions.

Semantic Data Integration Service for eHealth Applications.

Technologies for health have been receiving considerable attention with the popularization of devices for internet access. The Internet can be seen as a repository of knowledge due to its large amount of available information; however, on the other hand, in the midst of this vast amount of content, there is information either scientifically inaccurate or incomplete. This work presents a semantic integration service to provide information of diabetes from medical databases to eHealth applications.

A dynamic data security mechanism based on position aware Merkle tree for health rehabilitation services over cloud

Cloud computing paradigm has simplified the delivery of diverse services with the federated organization of computing hardware, storage, software, and services. Many organizations have embraced cloud-based business models to cater to the dynamism of clients. Utility computing harnesses the cloud computing infrastructure to provide services on demand to the customers. It frees the clients from the burden of establishing expensive infrastructures, under-utilization, and wastage of resources, etc. Utility computing provides a wide range of services including storage, computing, networking, web hosting, email, groupware, and data center services. Almost all enterprise models are centered on these services to provide reliable solutions to the clients. In recent years, healthcare industries have resorted to cloud-based solutions to deliver services to remote clients. Rehabilitation is a vital component of healthcare, ascribing to the restoration of the physical, mental and sensory elements of patients. Telerehabilitation services are provided over the cloud for convenience of the healthcare providers and clients. However, these services require maintenance of medical records in the cloud storage for immediate access to the patients and caregivers. Data security is an important concern in such environments in protecting the privacy of a patient. It is required to implement stringent mechanisms for proof generation and verification for authentication and integrity checks. This paper presents a novel publicly certifiable method for the above requirements. This approach is based on a position aware Merkle tree, which employs a 3-tuple scheme, which has been testified for its robustness in providing authentication and data integrity services. The experimental results with the proposed system and performance metrics demonstrate the appropriateness of the system in health rehabilitation services.

EHealth Integrity Model Based on Permissioned Blockchain

(1) Background: Large eHealth systems should have a mechanism to detect unauthorized changes in patients’ medical documentation, access permissions, and logs. This is due to the fact that modern eHealth systems are connected with many healthcare providers and sites. (2) Methods: Design-science methodology was used to create an integrity-protection service model based on blockchain technology. Based on the problem of transactional transparency, requirements were specified and a model was designed. After that, the model’s security and performance were evaluated. (3) Results: a blockchain-based eHealth integrity model for ensuring information integrity in eHealth systems that uses a permissioned blockchain with off-chain information storage was created. In contrast to existing solutions, the proposed model allows information removal, which in many countries’ eHealth systems is a legal requirement, and is based on a blockchain using the Practical Byzantine Fault Tolerant algorithm. (4) Conclusion: A blockchain can be used to store medical data or only security-related data. In the proposed model, a blockchain is mainly used to implement a data-integrity service. This service can be implemented using other mechanisms, but a blockchain provides a solution that does not require trusted third parties, works in a distributed eHealth environment, and supports document removal.

PENGUKURAN KEPUASAN PENGGUNA DAN PENINGKATAN LAYANAN SISTEM INFORMASI PELAYANAN DESA DAN DATA TERPADU (SILADDU) KABUPATEN MAJALENGKA

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. Keywords: SILADDU, User Satisfaction, Aplication

Cloudmatcher

As data science applications proliferate, more and more lay users must perform data integration (DI) tasks, which used to be done by sophisticated CS developers. Thus, it is increasingly critical that we develop hands-off DI services, which lay users can use to perform such tasks without asking for help from developers. We propose to demonstrate such a service. Specifically, we will demonstrate CloudMatcher, a hands-off cloud/crowd service for entity matching (EM). To use CloudMatcher to match two tables, a lay user only needs to upload them to the CloudMatcher's Web page then iteratively label a set of tuple pairs as match/no-match. Alternatively, the user can enlist a crowd of workers to label the pairs. In either case, the lay user can easily perform EM end-to-end without having to involve any developers. Cloud-Matcher has been used in several domain science projects at UW-Madison and at several organizations, and is scheduled to be deployed in a large company in Summer 2018. In the demonstration we will show how easy it is for lay users to perform EM (either via interactive labeling or crowdsourcing), how users can easily create and experiment with a range of EM workflows, and how CloudMatcher can scale to many concurrent users and large datasets.

Design and implementation of Chinese materia medica resources survey results display system

From the beginning of the fourth national census of traditional Chinese medicine resources in 2011, a large amount of data have been collected and compiled, including wild medicinal plant resource data, cultivation of medicinal plant information, traditional knowledge, and specimen information. The traditional paper-based recording method is inconvenient for query and application. The B/S architecture, JavaWeb framework and SOA are used to design and develop the fourth national census results display platform. Through the data integration and sorting, the users are to provide with integrated data services and data query display solutions. The platform realizes the fine data classification, and has the simple data retrieval and the university statistical analysis function. The platform uses Echarts components, Geo Server, Open Layers and other technologies to provide a variety of data display forms such as charts, maps and other visualization forms, intuitive reflects the number, distribution and type of Chinese material medica resources. It meets the data mapping requirements of different levels of users, and provides support for management decision-making.

SCALEUS: Semantic Web Services Integration for Biomedical Applications.

In recent years, we have witnessed an explosion of biological data resulting largely from the demands of life science research. The vast majority of these data are freely available via diverse bioinformatics platforms, including relational databases and conventional keyword search applications. This type of approach has achieved great results in the last few years, but proved to be unfeasible when information needs to be combined or shared among different and scattered sources. During recent years, many of these data distribution challenges have been solved with the adoption of semantic web. Despite the evident benefits of this technology, its adoption introduced new challenges related with the migration process, from existent systems to the semantic level. To facilitate this transition, we have developed Scaleus, a semantic web migration tool that can be deployed on top of traditional systems in order to bring knowledge, inference rules, and query federation to the existent data. Targeted at the biomedical domain, this web-based platform offers, in a single package, straightforward data integration and semantic web services that help developers and researchers in the creation process of new semantically enhanced information systems. SCALEUS is available as open source at http://bioinformatics-ua.github.io/scaleus/ .

A Survey on Emerging Computing Paradigms for Big Data

The explosive growth of data volume and the ever-increasing demands of data value extraction have driven us into the era of big data. The “5V” (Variety, Velocity, Volume, Value, and Veracity) characteristics of big data pose great challenges to traditional computing paradigms and motivate the emergence of new solutions. Cloud computing is one of the representative technologies that can perform massive-scale and complex data computing by taking advantages of virtualized resources, parallel processing and data service integration with scalable data storage. However, as we are also experiencing the revolution of Internet-of-things (IoT), the limitations of cloud computing on supporting lightweight end devices significantly impede the flourish of cloud computing at the intersection of big data and IoT era. It also promotes the urgency of proposing new computing paradigms. We provide an overview on the topic of big data, and a comprehensive survey on how cloud computing as well as its related technologies can address the challenges arisen by big data. Then, we analyze the disadvantages of cloud computing when big data encounters IoT, and introduce two promising computing paradigms, including fog computing and transparent computing, to support the big data services of IoT. Finally, some open challenges and future directions are summarized to foster continued research efforts into this evolving field of study.

A New Approach for Cloud Data Security: From Single to Cloud-of-Clouds

Abstract In the corporate world, a large number of people store their data on clouds. For doing this they need to give their confidential data in the hands of the third party, commonly known as service providers. These cloud service providers cannot be trusted since the complete data is stored in one single cloud. This increases security risks to the user’s sensitive data. Due to this issue of data integrity risk and service availability failure, the concept of “Cloud-of-Clouds” comes into picture. Cloud-of-clouds are also known as “inter-clouds” or “multi-clouds”. Use of cloud-of-clouds provides a higher level of security to the user’s confidential data. The aim of this paper is to secure the user’s data by using cloud-of clouds.

System architecture of coastal remote sensing data mining and services based on cloud computing

Coastal remote sensing images have huge data which has features of the volume, variety, complexity and specialisation. The problem is how to effectively carry out data integration, fast extract and data mining of knowledge and information from these massive remote sensing data as it is far behind the requirements of coastal professional applications. In this paper, based on data mining, remote sensing theory, space information and cloud computing technology towards the goal of coastal zone remote sensing data integration and data mining service system, the meta-data model, data storage model, data mining framework, web service framework model, etc. are provided and designed. Finally, a prototype system of remote sensing data mining services in the cloud computing environment is designed and developed using system integration, and is demonstrated and verified by professional applications. It is valuable to serve the development of the coastal zone, monitoring, planning and integrated management and other fields.

Controlling and Monitoring Specific Hardware Resources

(ProQuest: ... denotes formulae omitted.)1 IntroductionRich Internet Applications (RIAs) is an umbrella term for various that are combining media-rich power of the traditional desktop with the deployment and content-rich nature of web applications [1]. According to Gartner, RIA frameworks can be divided in JavaScript/AJAX-based frameworks and plugin-based frameworks. The first ones are browser-based and more lightweight, the second ones are more heavyweight with a bigger download footprint (Valdez, 2009; Busch & Koch, 2009).Plugin-based frameworks like Adobe Flex, JavaFX and Microsoft Silverlight, offer extended support for media, are highly interactive, cross-platform, cross-device, cross-browser and desktop-like with offline and out-off-browser support [8].A typical RIA application has a multi-tier architecture, based on data integration layer, business logic & services layer and presentation layer. Taking into consideration the new client-side capacities, the new presentation features, and the different communication flows between the client and the server, the following four phases in designing RIA are necessary:* data model design,* business logic & services design,* presentation design, and* communication design [12].Communication is a cross-cutting concern related to data synchronization, business logic & services distribution and presentation. The communication must provide the binding between the presentation and the underlying data/business logic & services layers using synchronous/ asynchronous methods.Best practices in RIA development enrich the general design considerations with specific design issues depending on the requirements and the technologies used for implementation. With respect to the general architecture of a RIA application (Figure 1), a prototype for controlling and monitoring specific hardware resources was developed.The demarche was developed within an Adobe Flex framework, a just-in-time deployment model being employed.2 Agile Approach for Developing the RIA PrototypeBased on the author's results obtained in the agile development of portals [10], [11], the demarche was applied for developing the proposed RIA application. The main functionalities of the RIA application for controlling and monitoring specific hardware resources are described by the use cases diagram (Figure 2).The agile development framework proposed in [10], [11] recommends the use of prototype technique enriched with MDA (Model Driven Architecture) specific attributes and is based on the following phases:* Conception: the PIM model elaboration is targeted, according to the requirements;* Design: targets the elaboration of the PSM model specific for the RIA prototype, i.e. the finalization of the architecture of this model, taking into account all details regarding the IT infrastructure, which must sustain the unitary, integrating vision of the PIM model; the building of the PSM model will take into account the future implementation solution of the RIA, by relating the model to certain developing technologies and frameworks;* I.T.I (Implementing -Testing- Installation) phase has the goal to implement the RIA prototype according to the PSM model, followed by the testing of the prototype; often, as a result of testing its functionality, the prototype invalidation leads to the revision of the PSM model and aims at correcting some aspects related to technologies and implementing frameworks.With respect to the RIA general architecture, the PIM and further the corresponding PSM models have been elaborated: model for data layer; model for business & services layer; model for presentation layer; model for communication schema; model for security schema; model for general management and maintenance of the RIA application.Both PIM and PSM models were described with the help of the UML language, al kind of specific diagrams being developed. …

Progress in Multi-Disciplinary Data Life Cycle Management

Modern science is most often driven by data. Improvements in state-of-the-art technologies and methods in many scientific disciplines lead not only to increasing data rates, but also to the need to improve or even completely overhaul their data life cycle management.Communities usually face two kinds of challenges: generic ones like federated authorization and authentication infrastructures and data preservation, and ones that are specific to their community and their respective data life cycle. In practice, the specific requirements often hinder the use of generic tools and methods.The German Helmholtz Association project ’’Large-Scale Data Management and Analysis” (LSDMA) addresses both challenges: its five Data Life Cycle Labs (DLCLs) closely collaborate with communities in joint research and development to optimize the communities data life cycle management, while its Data Services Integration Team (DSIT) provides generic data tools and services.We present most recent developments and results from the DLCLs covering communities ranging from heavy ion physics and photon science to high-throughput microscopy, and from DSIT.

Data integration of cost materials needs through a service oriented architecture (study case: pt x garment ungaran)

Data processing is an important part of a garment company. With increasingly complex and developing a garment company, data processing and integration became a very important requirement. The need for data integration in determining the cost of materials becomes a very important part in the garment industry. Data distribution or dissemination from one to another section gives results in data duplication, so that it may cause the data to be inconsistent. In addition, the efficiency of the process in determining the cost of materials is highly needed to achieve the selling price determination target. Nowadays, there is web-based technology, which is capable of handle data integration service, called SOA (Service Oriented Architecture). Business processes (work flows) involving the supplier need to go back to the supplier with the output price that has been determined by the system, can be integrated with Web service with concentration of BPELSOA. By utilizing the SOA technology then data processing and integration problems that occurred in the garment industry could be made into an integrated information system, so that the problems in the garment industry can be solved.