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    20 March 2017, Volume 3 Issue 2
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    TOPIC:BIG DATA MANAGEMENT AND ANALYTICS
    Understanding on the big data:beyond the data management and analytics
    Aoying ZHOU
    2017, 3(2):  3-18.  doi:10.11959/j.issn.2096-0271.2017014
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    Big data is still a buzzword，and more and more people are talking about it with various kinds of different explanations.Based on writer’s understanding，the big data，big data strategy and “internet plus” initiative will be discussed here.The database philosophy was revisited，for understanding the development of data management is meaningful to catch the good opportunities in big data era.Moreover，from the point of view of a senior IT professional，the development paradigm for IT has been shifted in the past decade.The change was described，and three systems and their development and deployment were presented.A new concept，sharing database，was proposed to catch up the notion behind the block chain.

    Approaches for value extraction on big data
    Xiaoyong DU, Yueguo CHEN
    2017, 3(2):  19-25.  doi:10.11959/j.issn.2096-0271.2017015
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    The value of big data can be presented in different means，and therefore it has different ways to extract the value out of big data.Three approaches of value extraction on big data:data service，data analytics，and data exploration were summarized.The characteristics of these approaches were analyzed and compared.In summary，data service reflects the value of data from the micro-level by supporting high-performance and high-throughput read and write operations.Data analysis focuses on the usage of statistical models to generalize data distribution at macro-level，and it extracts values by generating insights from data.Data exploration focuses on interactive models in the constant interchange of micro-level and macrolevel to guide the users browse and discover values out of the data.

    Beware of traps of big data analytics in business
    KamFai WONG
    2017, 3(2):  26-30.  doi:10.11959/j.issn.2096-0271.2017016
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    In the era of data explosion，research and application of big data has become a hot topic.How to automatically discover useful information from big data was focused.The organization is as following:examples of big data“traps” and their influences were discussed.The framework of an intelligent system to process social media texts that avoids traps and extracts useful information from big data was described.The research works proposed by our team and based on the framework about event detection，summarization and rumor detection were covered.

    Making big data analysis more credible
    Tengjiao WANG, Xilian LI
    2017, 3(2):  31-37.  doi:10.11959/j.issn.2096-0271.2017017
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    Big data is playing an increasingly important role in various areas of academia and industry.However，whether big data can be trusted has caused widespread concern and intense discussion among countless researchers.The credibility of big data from the historical evolution of big data names，case studies of big data applications and big data engineering was explored，and thus the three challenges needed to be addressed to ensure the correctness of big data analysis were concluded:the right choice of data source，the scientific sampling of representative and valuable data，the rigorous and complete big data engineering analysis method.

    Applications of social network analysis in public security
    Yingxia SHAO, Shicong FENG
    2017, 3(2):  38-44.  doi:10.11959/j.issn.2096-0271.2017018
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    Social network analysis (SNA) is a general and effective approach of studying the complex relationship patterns among social members.Public security field was focused.Firstly，the theory of SNA was introduced，and then three applications of applying SNA in public security were described，including crime gang mining，core criminal member identification，and serial and joint cases analysis.It’s beneficial to readers to know about the capability of SNA in public security fields.

    STUDY
    Deep learning for chemoinformatics
    Youjun XU, Jianfeng PEI
    2017, 3(2):  45-66.  doi:10.11959/j.issn.2096-0271.2017019
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    Deep learning have been successfully used in computer vision，speech recognition and natural language processing，leading to the rapid development of artificial intelligence.The key technology of deep learning was also applied to chemoinformatics，speeding up the implementation of artificial intelligence in chemistry.As developing quantitative structure-activity relationship model is one of major tasks for chemoinformatics，the application of deep learning technology in QSAR research was focused.How three kinds of deep learning frameworks，namely，deep neural network，convolution neural network，and recurrent or recursive neural network were applied in QSAR was discussed.A perspective on the future impact of deep learning on chemoinformatics was given.

    APPLICATION
    Application-oriented integration platform construction on big data
    Xiangfei MENG, Jinghua FENG, Yang ZHAO, Zijun XIA
    2017, 3(2):  67-77.  doi:10.11959/j.issn.2096-0271.2017020
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    Big data from information society reform was introduced.The application-oriented platform architecture integrating big data with supercomputing and cloud computing was introduced in detail，which including physical infrastructure，system software and management system.Moreover，some typical applications were introduced，such as biology and genomes，meteorology and air pollution.Finally，the opinions on how to promote application development of big data，which can provide reference for the decision-making of the governments and industries，were proposed.

    Smart-card based campus friend mining and its applications
    Mingming LU, Dan ZHANG, Jianxin WANG
    2017, 3(2):  78-91.  doi:10.11959/j.issn.2096-0271.2017021
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    Recently，educational big data has become a hot topic.A distributed file system to store，preprocess，and analyze campus card data was adopted.Based on it，a student encounter model has been proposed，so as to mine students’ offline social relations.To distinguish real friends from familiar strangers，the offline social relations for either individual students or classes were analyzed.Through these two perspectives，the students’ offline encounters was analyzed，which can not only extract social relationship between friends (including the isolated students)，but also provide data support for the campus class management.The experimental results show that the mined social relations reflect the real relationship.

    Big data analysis technology and application on taxation
    Jiangping WANG, Rong XIAO
    2017, 3(2):  92-103.  doi:doi:10.11959/j.issn.2096-0271.2017022
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    Based on the practice of big data analysis on a provincial tax bureau，this study aimed to illustrate the issue on how to construct a big data analysis platform adapting for tax administration in the current informative situation，as well as the approaches of data processing and modeling in the application.Compared with traditional information approach，this research illustrated that big data analysis on taxation would be a powerful innovation with remarkable breakthroughs in tax administrational information construction.

    Foundation issues for big data research
    Yangyong ZHU, Yun XIONG
    2017, 3(2):  104-114.  doi:10.11959/j.issn.2096-0271.2017023
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    The key technical challenges for big data lie in how to discover the value of the low-value-density data and how to complete the task in the desired time.The ways to take up these challenges from three aspects were discussed.First is that the former challenge requires the combination of domain knowledge and data technology.This combination of theory and new algorithms forms the basis of application and analysis of big data.Second is that the latter challenge needs to design new types of computer，cluster system，computing framework，storage system and data management method，which forms the basis of computing and data of big data.Thirdly，both challenges relate to mathematical theory，which is the basis of mathematics of big data.In conclusion，several foundation issues for big data research including the basis of mathematics，computing，data，analysis and application of big data were analyzed.

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