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20 July 2017, Volume 3 Issue 4

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FOCUS

Real-time processing technology,platform and application of streaming big data

Chun CHEN

2017, 3(4):  1-8.  doi:10.11959/j.issn.2096-0271.2017036

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According to its timeliness,big data processing systems can be categorized into two groups,namely batching big data processing and streaming big data processing.Both systems mentioned above are unable to meet the real-time requirement for censoring and query analysis tasks.To this end,the “stream cube” real-time data analysis technology and platform were presented,which can perform timely query with low lag.Currently,this technology has been applied to many fields,including financial risk management,anti-fraud as well as web bots defense,and offers promising prospects for further applications.

TOPIC:BIG DATA STANDARDS

 

DU Xiaoyong, WU Dongya

2017, 3(4):  9-10. 

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Big data standards system

Qun ZHANG,Dongya WU,Jinghua ZHAO

2017, 3(4):  11-19.  doi:10.11959/j.issn.2096-0271.2017037

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With the development of big data,standardization involves more and more content,and the objects are becoming more and more complex.The status of national and international big data standardization work was systematically analyzed.In combination with the national strategy of "Platform for the Development of Big Data" and the Thirteenth Five-Year Plan of National Economic and Social Development of the People's Republic of China,as well as the demand for big data standardization,the problems of national big data standardization were pointed out,big data reference architecture and standards system was proposed,and suggestions for future work were given.

ISO/IEC JTC1/WG9 big data international standards and the impact on Chinese domestic standards

Liang GUANG,Qun ZHANG

2017, 3(4):  20-28.  doi:10.11959/j.issn.2096-0271.2017038

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ISO/IEC JTC1/WG9 is dedicated to the development of foundational big data standards.The on-going standardization works on vocabulary and big data reference architecture have great value and impact on the Chinese domestic standards.The structure and current projects of WG9,and the foundational status of its standards in international big data standards system were presented.In addition,the standards work in the WG9 and the Chinese domestic standards system were compared,and the values of participating in and contributing to WG9 standards were discussed.At last,suggestions on how to get involve in and contribute to WG9 were given.

Data management capability maturity model

Bing LI,Junzhi BIN

2017, 3(4):  29-36.  doi:10.11959/j.issn.2096-0271.2017039

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To promote the continuous development of big data and improve the government and enterprises’ awareness of data management,the organizational data management of the eight process areas were extracted by analyzing and summarizing the data management capabilities,combining with data lifecycle management at all stages of the characteristics and the theory from domestic and abroad.Otherwise,each capability was divided into two process areas and development levels,introduced the functions and formulated the standardization of assessment.

Basic capability and performance test of big data platform

Chunyu JIANG,Kai WEI

2017, 3(4):  37-45.  doi:10.11959/j.issn.2096-0271.2017040

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The whole big data technology is now leaded by open source society which results in coexist of many competing technologies.Open sources also help to cultivate a great number of big data commercial software.The enterprise market is now crowded by various providers.How to evaluate these softwares becomes a new research topic.At the beginning,the development of big data system was briefly reviewed.Then the requirement of big data technology standardization was illustrated.After reviewing the progress of international big data technology standardization,the standardization and test practices in big data products under the organization of Data Center Alliance was introduced.Finally,the drawbacks of current practices were discussed,and the future direction of standardization and test for big data products was summarized.

Research on big data platform architecture and standardization for specific fields

Wangqun LIN,Chenxu GAO,Ke TAO,Bo DENG,Yang BAO

2017, 3(4):  46-59.  doi:10.11959/j.issn.2096-0271.2017041

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The research development and related works of big data were analyzed.According to the characteristics of big data and the particularly requirements of the specific fields mentioned above,the reference architecture was presented.The different components of the inference architecture in detail combing the mainstream technology and basic platform were analyzed.The milestones of big data standardization were studied and the standards system of big data was presented.All of these contribute the technology development of reference architecture and standardization of big data in future,and provide technical reference for architecture-oriented and standardization-oriented construction of large data-related fields.

Logistics big data standardization and case study

Fan JIANG

2017, 3(4):  60-66.  doi:10.11959/j.issn.2096-0271.2017042

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In the field of logistics distribution,the solution of data and intelligent transformation process was introduced to solve how to face the challenges of big data standardization,and how to improve the single distribution efficiency and save multiple distribution cost standard solution from the scheduling system and open platform.The case provides a reference for the analysis of logistics data standardization technology,the implementation of cases and innovative experience.

STUDY

Industrial big data technologies and architecture

Shuquan ZHENG,Haihuan QIN,Qian WANG

2017, 3(4):  67-80.  doi:10.11959/j.issn.2096-0271.2017043

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Industrial big data is an important asset of industrial enterprises.It is a crucial factor for an industrial enterprise to realize transformation and upgrading.The main sources and characteristics of industrial big data were analyzed,and reference architecture of industrial big data with three dimensions was proposed.Three aspects of the realization of business innovation and transformation of industrial enterprise based on industrial big data respectively were discussed,which included the typical application and business innovation of enterprise,cyber (physical) systems deployed at all levels of the enterprise,and the business architecture,information systems architecture and IT architecture that guide the implementation of application of industrial big data.Finally,the architecture and technology of a typical application of industrial big data were analyzed,which verified the validity of the proposed architecture.

APPLICATION

Ocean big data and applications in ship behavior prediction under disaster weather

Donghai WANG,Feng LU,Xiaorong FANG,Gang GUO

2017, 3(4):  81-90.  doi:10.11959/j.issn.2096-0271.2017044

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With the explosive growth of marine data,the ocean big data have received more attention and concern recently.The current status and key technologies of ocean big data here were summarized and analyzed.A specific case about the application of machine learning in the prediction model of ocean big data was also focused,which was a forecasting test of maritime ships behavior based on regression training in disaster weather (typhoon).The sample data for validating and evaluating three machine learning algorithms of decision tree,Bagging and random forest were trained and tested.The final results prove the best and robust effect of the random forest algorithm in the prediction of ship density under the disaster weather.

FORUM

Discussions of the value expectations of big data

Chongjun WANG

2017, 3(4):  91-103.  doi:10.11959/j.issn.2096-0271.2017045

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People from all social circles are concerned about the big data,because all of them think that big data is valuable.However,different people have different value expectation,all of which are the goals when implementing big data project.Multiple kinds of definitions and understandings of big data were attempted to indicate,and then different research perspectives and different value expectations from different people were introduced.Furthermore,some practical and feasible methods,ideas and strategies were given after briefly expressing the relevant research status and industrialization status.

TREND

Limits of predictions

2017, 3(4):  104-108.  doi:10.11959/j.issn.2096-0271.2017046

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