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    20 June 2015, Volume 1 Issue 3
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    Special Topic:Network big data
    Network Representation Learning
    Weizheng Chen, Yan Zhang, Xiaoming Li
    2015, 1(3):  8-22.  doi:10.11959/j.issn.2096-0271.2015025
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    Along with the constant growth of massive online social networks such as Facebook,Twitter,Weixin and Weibo,a tremendous amount of network data sets are generated.How to represent the data is an important aspect when we apply machine learning techniques to analyze network data sets.Firstly,the research background was introduced and the definitions of NRL (network representation learning) were related.According to the categories of different algorithms,five kinds of primary NRL algorithms were introduced.Particularly,a detailed introduction to NRL algorithms based deep learning techniques was given emphatically.Then the evaluation methods and application scenarios of NRL were discussed.Finally,the research prospect of NRL in the future was discussed.

    Big Data and Recommendation System
    Cuiping Li, Mengwei Lan, Benyou Zou, Shaoqing Wang, Kankan Zhao
    2015, 1(3):  23-35.  doi:10.11959/j.issn.2096-0271.2015026
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    In big data era,recommendation system is the key means to tackle the issue of “information overload”.Recommendation system has been widely applied to many domains.The most typical and promising domain is the e-commence.Recently,with the rapid development of e-commence,recommendation system becomes more and more important and is promoted as a hot research field.The history and development of recommendation system,its domain requirements and system architecture,its characteristics and challenges under big data environment,its key techniques,open source big data recommendation systems were introduced.And at last,the open research problems and future trends of bid data recommendation system were discussed.

    Web Analytical Engine in the Big Data Era
    Zhicheng Dou, Jirong Wen
    2015, 1(3):  36-47.  doi:10.11959/j.issn.2096-0271.2015027
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    Web search engines can only return a list of Web documents (the so-called ten blue links),whereas users may need high-order knowledge that is contained within the Web documents.The demand of analytical services atop the Web is becoming stronger with the rapid development of the internet and the increase of big Web data.The concept of“Web Analytical Engine”,which aims to provide analytical service atop the huge amount of Web documents,was introduced.A simple infrastructure was described and the key research problems for building such an engine were discussed.

    Reviewing Large Graph Computing from a System Perspective
    Chengwen Wu, Guangyan Zhang, Weimin Zheng
    2015, 1(3):  48-61.  doi:10.11959/j.issn.2096-0271.2015028
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    Large graphcomputing has been a fundamental computing pattern in both academic and industry field,and it was applied to a lot of practical big data applications,such as social network analysis,web page search,and goods recommendation.In general,most of large graphs scale to billions of vertices,and corresponding to hundreds billions of edges,which brings us challenges of efficient graph processing.Therefore,the basic feature and challenges of current large graph computing,typical computing models,and representative distributed,and single machine large graph processing systems were introduced.Then,some key technologies employed in large graph computing were summarized.Finally,some research directions in large graph computing from a system perspective were given.

    Text Content Analysis for Web Big Data
    Xueqi Cheng, Yanyan Lan
    2015, 1(3):  62-71.  doi:10.11959/j.issn.2096-0271.2015029
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    Text content analysis is an effective way to understand and acquire the “value” of big fata.The challenges and research results were investigated in the three hot topics: topic modeling for short texts,word embedding and learning to rank for web pages.In the end,some remaining problems in this area were proposed.

    Text Big Data Content Understanding and Development Trend Based on Feature Learning
    Shuhan Yuan, Yang Xiang, Shijia E
    2015, 1(3):  72-81.  doi:10.11959/j.issn.2096-0271.2015030
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    Big data contains important value information.Text big data as an important part of big data is the main carrier of human knowledge.Feature represents the inherent law of the data.Mapping the text big data to its feature space which reflects the nature of data is an important method to understand the semantic meaning of the text.Text big data feature representations and feature learning were reviewed.Then the progress of feature learning used in text content understanding was presented.Finally,the future development trends of big text data content understanding were discussed.

    STUDY
    Research on Influence Diffusion in Social Network
    Wei Chen
    2015, 1(3):  82-98.  doi:10.11959/j.issn.2096-0271.2015031
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    With the wide spread of internet and big data research and applications,influence diffusion research in social network becomes one of the hot topics in data mining and social network analysis in recent years.The main results on social influence diffusion research from the field of computer science in the last decade,which covers the three main areas-- influence diffusion modeling,influence diffusion learning,and influence diffusion optimization,were summarized.Different techniques,such as stochastic modeling,data mining,algorithmic optimization,and game theory,were demonstrated in their application to influence diffusion research.Finally,some discussions on the current issues,challenges and future directions in influence diffusion research and applications were provided.

    Big Data Stream Computing:Features and Challenges
    Dawei Sun
    2015, 1(3):  99-105.  doi:10.11959/j.issn.2096-0271.2015032
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    In big data era,the timeliness of data has become one of the most important factors,and the streaming feature of data has become more obvious.More and more applications need to be deployed in stream computing platforms.Big data stream computing as a major form of big data computing has become more and more important.The features of big data stream computing application were systematically analyzed.The principle strategies to build a big data stream computing system were given from the perspective of system architecture.Combined with some typical big data stream computing systems,some technology challenges in big data stream computing environments were focused,such as resource scheduling in online environments,fault tolerance strategy in node-dependence environments.

    Challenge and Solution of Big Data Backup and Recovery
    Shengmei Luo, Ming Li, Yuwen Ye
    2015, 1(3):  106-112.  doi:10.11959/j.issn.2096-0271.2015033
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    Big data has become the focus of the social attention,it will raise a new competition in science and technology and comprehensive national strength,especially in the disaster recovery and backup data.Therefore,based on the analysis of the current industrial status and the requirements about big data disaster recovery and backup,the advantage and disadvantage of several typical technology solutions were discussed,then a better incremental backup data recovery solution was proposed.This solution can support minute RPO,and meet current requirements about the disaster recovery and backup data.

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