Research of data architecture in digital ocean

The characters of marine data, such as multi-source, polymorphism, diversity and large amount, determine their differences from other data. How to store and manage marine data rationally and effectively to provide powerful data support for marine management information system and ＂Digital Ocean＂ prototype system construction is an urgent problem to solve. Different types of system planning data, such as marine resource, marine environment, marine econotny and marine management, and establishing marine data architecture frame with uniform standard are to realize the effective management of all level marine data, such as national marine data, the provincial （municipal） marine data, and meet the need of fundamental information-platform construction.

Research on the Realization Path and Application of a Data Governance System Based on Data Architecture

The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.

Big Data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.

Exploring the Approaches to Data Flow Computing

Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widelyused for general purpose computing.Processors based on the data flow architecture employ fine-grain data-driven parallelism.These architectures have thepotential to exploit the inherent parallelism in compute intensive applicationslike signal processing,image and video processing and so on and can thusachieve faster throughputs and higher power efficiency.In this paper,severaldata flow computing architectures are explored,and their main architecturalfeatures are studied.Furthermore,a classification of the processors is presented based on whether they employ either the data flow execution modelexclusively or in combination with the control flow model and are accordinglygrouped as exclusive data flow or hybrid architectures.The hybrid categoryis further subdivided as conjoint or accelerator-style architectures dependingon how they deploy and separate the data flow and control flow executionmodel within their execution blocks.Lastly,a brief comparison and discussionof their advantages and drawbacks is also considered.From this study weconclude that although the data flow architectures are seen to have maturedsignificantly,issues like data-structure handling and lack of efficient placementand scheduling algorithms have prevented these from becoming commerciallyviable.

Research on the Trusted Energy-Saving Transmission of Data Center Network

According to the high operating costs and a large number of energy waste in the current data center network architectures, we propose a kind of trusted flow preemption scheduling combining the energy-saving routing mechanism based on typical data center network architecture. The mechanism can make the network flow in its exclusive network link bandwidth and transmission path, which can improve the link utilization and the use of the network energy efficiency. Meanwhile, we apply trusted computing to guarantee the high security, high performance and high fault-tolerant routing forwarding service, which helps improving the average completion time of network flow.

A Cloud Service Architecture for Analyzing Big Monitoring Data

Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.

Design of Service-Oriented Architecture for Spatial Data Integration and Its Application in Building Web-based GIS Systems

In this paper we propose a service-oriented architecture for spatial data integration (SOA-SDI) in the context of a large number of available spatial data sources that are physically sitting at different places, and develop web-based GIS systems based on SOA-SDI, allowing client applications to pull in, analyze and present spatial data from those available spatial data sources. The proposed architecture logically includes 4 layers or components; they are layer of multiple data provider services, layer of data in-tegration, layer of backend services, and front-end graphical user interface (GUI) for spatial data presentation. On the basis of the 4-layered SOA-SDI framework, WebGIS applications can be quickly deployed, which proves that SOA-SDI has the potential to reduce the input of software development and shorten the development period.

CALL FOR PAPERS Special Section of JCST on Computer Architecture and Systems for Big Data

Introduction Research on computer architecture and systems is typically driven by technology and applications. Big data has emerged as an important application domain which has shown its huge impact on scientific research, business, and society. Big data is known for its large volume, high velocity, and a variety of formats. The collection, storage, retrieval, processing, and visualization of big data issues many challenges to computer architecture and systems. This special section is an effort to encourage and promote research to address the big data challenges from the computer architecture and systems perspectives.