面向大数据分析的智能交互向导系统

doi:10.11896/jsjkx.200900083

计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 110-117.doi: 10.11896/jsjkx.200900083

所属专题：智能数据治理技术与系统

• 智能数据治理技术与系统^* • 上一篇下一篇

面向大数据分析的智能交互向导系统

余乐章^1,2, 夏天宇^1,2, 荆一楠^1,2, 何震瀛^1,2, 王晓阳^1,3

1 复旦大学计算机科学技术学院上海201203
2 上海市数据科学重点实验室(复旦大学) 上海200433
3 上海智能电子与系统研究院上海201203

收稿日期:2020-09-10 修回日期:2021-01-29 出版日期:2021-09-15 发布日期:2021-09-10
通讯作者: 荆一楠(jingyn@fudan.edu.cn)
作者简介:19212010048@fudan.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB1004404)

Smart Interactive Guide System for Big Data Analytics

YU Yue-zhang^1,2, XIA Tian-yu^1,2, JING Yi-nan^1,2, HE Zhen-ying^1,2, WANG Xiao-yang^1,3

1 School of Computer Science and Technology,Fudan University,Shanghai 201203,China
2 Shanghai Key Laboratory of Data Science,Fudan University,Shanghai 200433,China
3 Shanghai Institute of Intelligent Electronics and Systems,Shanghai 201203,China

Received:2020-09-10 Revised:2021-01-29 Online:2021-09-15 Published:2021-09-10
About author:YU Yue-zhang,born in 1997,master.His main research interest includes big data analysis.
JING Yi-nan,born in 1978,Ph.D,associate professor.His main research intere-sts include big data analysis,spatial and temporal data management,mobile computing,and security and privacy.
Supported by:
National Key R&D Program Funded Project of China(2018YFB1004404)

摘要/Abstract

摘要： 传统的大数据工具一般为专业数据分析人员打造,具有难以上手、操作交互性差以及不够智能化等特点。而智能交互向导系统是针对大数据交互式分析系统目前存在的问题而研制的一套大数据分析辅助工具。系统既研发了用户意图理解、数据抽样及列推荐、可视化推荐、分析方法推荐等核心关键技术,也拥有良好的图形化界面与人性化的智能交互体验。在满足用户多种交互式分析需求的同时,还具有极高的响应速度。不仅可以随时回溯到分析流程任意一步重新选择方法的执行流程,还可以通过接口与各种分析应用快速集成以部署应用于不同场景。经过实验测试,系统的平均交互时间均在3 s以内,且与传统分析方法相比系统交互的执行时效加快了3倍左右。通过用户用例测试,系统的满意度相比传统工具更加优秀。智能交互向导系统通过在易用性、时效性、可交互性和智能性等方面的探索,让不同基础的用户群体都可以使用此系统完成所需的大数据分析目标。

关键词: 大数据系统, 方法推荐, 数据分析, 用户意图, 智能交互

Abstract: Traditional big data tools are generally built for professional data analysts,and they have the characteristics of being difficult to get started,poor operation interaction,and not intelligent enough.The intelligent interactive guidance system is a set of big data analysis auxiliary tools developed around the current problems of the big data interactive analysis system.The system not only develops core key technologies such as user intention understanding,data sampling and column recommendation,visualization recommendation,and analysis method recommendation,but also has a good graphical interface and a humanized intelligent interactive experience.While meeting the user's multiple interactive analysis needs,it also has a very high response speed.Not only can you go back to any step of the analysis process to reselect the method execution process at any time,but you can also quickly integrate with various analysis applications through the interface to deploy and apply to different scenarios.After experimental tests,the average interaction time of the system is within 3 seconds,and the execution time of the system interaction is accelerated by about 3 times compared with the traditional analysis method.After using case testing,the system is also more satisfying than the use of traditional tools.Through the exploration of ease of use,timeliness,interactivity,and intelligence,the smart interactive guide system allows users of different basic groups to use the system to complete the required big data analysis goals.

Key words: Big data system, Data analysis, Method recommendation, Smart interaction, User intention

中图分类号:

TP311.5

余乐章, 夏天宇, 荆一楠, 何震瀛, 王晓阳. 面向大数据分析的智能交互向导系统[J]. 计算机科学, 2021, 48(9): 110-117. https://doi.org/10.11896/jsjkx.200900083

YU Yue-zhang, XIA Tian-yu, JING Yi-nan, HE Zhen-ying, WANG Xiao-yang. Smart Interactive Guide System for Big Data Analytics[J]. Computer Science, 2021, 48(9): 110-117. https://doi.org/10.11896/jsjkx.200900083

参考文献

[1]CHAUDHURI S,DAYAL U.An overview of data warehousing and OLAP technology[J].ACM Sigmod Rec,1997,26(1):65-74.
[2] LO E,KAO B,HOW S,et al.OLAP on sequence data[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data.Vancouver:ACM,2008:649-660.
[3] CHUI C,KAO B,LO E,et al.S-olap:An olap system for analyzing sequence data[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data.Indianapolis:ACM,2010:1131-1134.
[4]MOHAMMAD S,SOUVIK B,BISHWARANJAN B,et al.L-Store:A Real-time OLTP and OLAP System[C]// Procee-dings of the 21th International Conference on Extending Database Technology.Vienna:EDBT,2018:540-551.
[5]MOHAMADINA A A,GHAZALI M R B,IBRAHIM M R B,et al.Business intelligence:concepts issues and current systems[C]//2012 International Conference on Advanced Computer Science Applications and Technologies.Kuala Lumpur,Malaysia:IEEE,2012:234-237.
[6]JOEL R,BRÁULIO A,SéRGIO M.Business intelligence in a public institution－Evaluation of a financial data mart[C]// 12th Iberian Conference on Information Systems and Technologies (CISTI).Lisboa,Portugal,2017:1-6.
[7]GUO C P,WANG Z,HAN F,et al.HaoLap:An Hadoop Based OLAP System for Massive Data[J].Journal of Computer Research and Development,2013,50(S1):378-383.
[8]CHEN X Q,XU J,GUO J F,et al.BDA:An open big data ana-lysis engine[J].Newsletter of Chinese Computer Society,2017,13(8):33-39.
[9]LI G J,CHEN X Q.Research Status and Scientific Thinking of Big Data[J].Bulletin of the Chinese Academy of Sciences,2012,27(6):647-651.
[10]PARK Y,MOZAFARI B,SORENSON J,et al.Verdictdb:Universalizing approximate query processing[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data.New York:ACM,2018:1461-1476.
[11]WU Z G,JING Y N,HE Z Y,et al.POLYTOPE:a flexible sampling system for answering exploratory queries[J].World Wide Web,2019,23(1):1-22.
[12]CLEVELAND W S,MCGILL R.Graphical perception:Theory,experimentation,and application to the development of graphical methods[J].Journal of the American Statistical Association,1984,79(387):531-554.
[13]MACKINLAY J D.Automating the design of graphical presentations of relational information[J].ACM Transactions on Graphics,1986,5(2):110-141.
[14]LUO Y,QIN X,TANG N,et al.DeepEye:Towards Automatic Data Visualization[C]// 2018 IEEE 34th International Confe-rence on Data Engineering.Paris:IEEE,2018:101-112.
[15]TROUILLON T,DANCE C R,GAUSSIER é,et al.Knowledge graph completion via complex tensor factorization[J].The Journal of Machine Learning Research,2017,18(1):4735-4772.
[16]SUN Z Y,CHEN Z X,HE Z Y,et al.A Fast Automated Model Selection Approach Based on Collaborative Knowledge[J].Database System for Advanced Applications,2020,12112:655-662.
[17]ASHISH T,JOYDEEP S S,NAMIT J,et al.Hive:a warehou-sing solution over a map-reduce framework[C]// Proceedings of the 35th International Conference on Very Large Data Bases.Lyon:Springer,2009:1626-1629.

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面向大数据分析的智能交互向导系统

Smart Interactive Guide System for Big Data Analytics

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