Web环境下地学数据共享用户行为模式分析

doi:10.3724/SP.J.1047.2016.01174

Abstract

Abstract:

Understanding the user behavior of science data sharing is a key step to implement effective and accurate service for science data sharing. This study aims to explore the user behavior of science data sharing using spatial data mining and Web usage mining techniques for the National Earth System Science Data Sharing Platform. At the stage of data preprocessing, procedures of user identification, session identification and user location identification were performed. Spatial hotspot analysis was conducted to analyze the user pageviews, sessions, and dataset visits to explore the geographical variance of user behaviors using the Getis-Ord Gi* method. FP-growth was taken to be the algorithm for mining association rules, and was performed for analyzing data visits and data downloads. Data mining results show that: (1) the user distribution of data sharing platform does not show significant correlation with the overall university population distribution in China, but shows a significant positive correlation with the population of research-oriented universities; (2) the hotspot analysis shows that regions of hotspots were clustering in Beijing, Tianjin, and northern Hebei Province for all three perspectives, whereas the cold spots geographically scattered to a greater extent, e.g. the southern coastal provinces, Henan Province, Shandong Province, Sichuan Province, etc.; (3) the association rules mining reveals a number of frequently visited item sets and rules from the valuable user pageviews. The frequently visited item sets for data downloads were well coincided with the frequently visited data. However, no conspicuous rules occurred in data downloads. Results of the spatial hotspot analysis and association rules mining detected the geographical variance of users’ interests in data and discovered the usage patterns for the frequently visited data, which can be used for designing the personalized recommendation. This study provides a method for mining web user behaviors with the combination of Web usage mining and spatial data mining techniques, which can also be applied to the data mining of websites in other fields.

Key words: Web usage mining, spatial data mining, user behavior mining, science data sharing, Earth System Science data

WANG Mo,WANG Juanle. A Study on the User Behavior of Geoscience Data Sharing Based on Web Usage Mining[J].Journal of Geo-information Science, 2016, 18(9): 1174-1183.DOI:10.3724/SP.J.1047.2016.01174

Figures/Tables 14

Fig.1

Tab.1

Fig.2

Fig.3

Tab.2

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Tab.3

Tab.4

Tab.5

Tab.6

References 35

[1]	Tenopir C, Allard S, Douglass K.Data sharing by scientists: practices and perceptions[J]. PLoS ONE, 2011,6(6):e21101.
[2]	刘闯. 美国国有科学数据共享管理机制及对我国的启示[J].中国基础科学,2003(1):34-39. doi: 10.3969/j.issn.1009-2412.2003.01.008
	[ Liu C.Regulatory mechanisms of national science data sharing of USA and the inspiration to China[J]. China Basic Science, 2003,1:34-39. ] doi: 10.3969/j.issn.1009-2412.2003.01.008
[3]	徐冠华. 实施科学数据共享增强国家科技竞争力[J].中国基础科学,2003(1):5-9. doi: 10.3969/j.issn.1009-2412.2003.01.002
	[ Xu G H.Emhancing national science competitiveness with science data sharing[J]. China Basic Science, 2003,1:5-9. ] doi: 10.3969/j.issn.1009-2412.2003.01.002
[4]	Facca F M, Lanzi P L.Mining interesting knowledge from weblogs: asurvey[J]. Data & Knowledge Engineering, 2005,53(3):225-241. doi: 10.1016/j.datak.2004.08.001
[5]	Sajid N A, Zafar S, Asghar S.Sequential pattern finding: A survey[C]. 2010 International Conference on Information and Emerging Technologies (ICIET), 2010:1-6.
[6]	Wang Y T, Lee A J T. Mining Web navigation patterns with a path traversal graph[J]. Expert Systems with Applications, 2011,38(6):7112-7122. doi: 10.1016/j.eswa.2010.12.058
[7]	Bayir M A, Toroslu I H, Demirbas M, et al.Discovering better navigation sequences for the session construction problem[J]. Data & Knowledge Engineering, 2012,73(2):58-72. doi: 10.1016/j.datak.2011.11.005
[8]	Chen L, Bhowmick S S, Nejdl W.COWES: Web user clustering based on evolutionary web sessions[J]. Data & Knowledge Engineering, 2009,68(10):867-885. doi: 10.1016/j.datak.2009.05.002
[9]	Dimopoulos C, Makris C, Panagis Y, et al.A web page usage prediction scheme using sequence indexing and clustering echniques[J]. Data & Knowledge Engineering, 2010,69(4):371-382. doi: 10.1016/j.datak.2009.04.010
[10]	Narvekar M, Banu S S.Predicting user's Web navigation behavior using hybrid approach[J]. Procedia Computer Science, 2015,45:3-12. doi: 10.1016/j.procs.2015.03.073
[11]	Mobasher B, Cooley R,Srivastava J.Automatic personalization based on Web usage mining[J]. Communications of the ACM, 2000,43(8):142-151.
[12]	Park D H, Kim H K, Choi I Y, et al.A literature review and classification of recommender systems research[J]. Expert Systems with Applications, 2010,39(11):10059-10072.
[13]	Pierrakos D, Paliouras G, Papatheodorou C, et al.Web usage mining as a tool for personalization: a survey. user modeling and user adapted interaction, 2003,13(4):311-372. doi: 10.1023/A:1026238916441
[14]	Carmona C J, Ramírez-Gallego S, Torres F, et al.Web usage mining to improve the design of an e-commerce website: OrOliveSur com. expert systems with applications, 2012,39(12):11243-11249. doi: 10.1016/j.eswa.2012.03.046
[15]	Yin P Y, Guo Y M.Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining[J]. Applied Mathematics and Computation, 2013,219(24):11082-11095. doi: 10.1016/j.amc.2013.05.033
[16]	Song Q, Shepperd M J.Mining Web browsing patterns for E-commerce[J]. Computers in Industry, 2006,57(7):622-630. doi: 10.1016/j.compind.2005.11.006
[17]	Lopes P, Roy B.Dynamic recommendation system using Web usage mining for e-commerce users[J]. Procedia Computer Science, 2015,45:60-69. doi: 10.1016/j.procs.2015.03.086
[18]	Hung Y S, Chen K L B, Yang C T, et al. Web usage mining for analyzing elder self-care behavior patterns[J]. Expert Systems with Applications, 2013,40(2):775-83. doi: 10.1016/j.eswa.2012.08.037
[19]	Munka M, Drl K M.Impact of different pre-processing tasks on effective identification of users' behavioral patterns in Web-based educational system[J]. Procedia Computer Science, 2011,4:1640-1649. doi: 10.1016/j.procs.2011.04.177
[20]	Romero C, Espejo P G, Zafra A, et al.Web usage mining for predicting final marks of students that use Moodle courses[J]. Computer Applications in Engineering Education, 2013,21(1):135-146. doi: 10.1002/cae.20456
[21]	王琨,郭风华,李仁杰,等.基于Tripadvisior的中国旅游地国际关注度及空间格局[J].地理科学进展,2014(11):1462-1473. doi: 10.11820/dlkxjz.2014.09.004
	[ Wang K, Guo F H, Li R J.Tourism attention degree about China from overseas and its spatial patterns based on Tripadvisor[J]. Progress in Geography, 2014(11):1462-1473. ] doi: 10.11820/dlkxjz.2014.09.004
[22]	Arbelaitz O, Gurrutxaga I, Lojo A, et al.Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it[J]. Expert Systems with Applications, 2013,40(18):7478-7491. doi: 10.1016/j.eswa.2013.07.040
[23]	Cooley R, Mobasher B, Srivastava J.Data preparation for mining World Wide Web browsing patterns[J]. Knowledge and Information Systems, 1999,1(1):5-32.
[24]	Kosala R, Blockeel H.Web mining research: a survey[J]. Sigkdd Explorations, 2000,2(1):1-15. doi: 10.1145/360402.360406
[25]	Berendt B, Mobasherb B, Nakagawa M, et al.The impact of site structure and user environment on session reconstruction in web usage analysis[A]. In: Zaïane O, Srivastava J, Spiliopoulou M, et al (eds.). WEBKDD 2002 - mining web data for discovering usage patterns and profiles[M]. Berlin: Springer Berlin Heidelberg, 2003,2703:159-179.
[26]	Levene M, Borges J, Loizou G.Zipf's law for Web surfers[J]. Knowledge and Information Systems, 2001,3(1):120-129. doi: 10.1007/PL00011657
[27]	Liu B.Web data mining (second edition)[M]. Chicago: Springer, 2011:540-542.
[28]	Getis A, Ord J K.The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 1992,24:189-206. doi: 10.1111/j.1538-4632.1992.tb00261.x
[29]	Peeters A, Zude M, K Thner J, et al. Getis-Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data[J]. Computers and Electronics in Agriculture, 2015,111:140-150. doi: 10.1016/j.compag.2014.12.011
[30]	Feske M L, Teeter L D, Musser J M, et al.Including the third dimension: a spatial analysis of TB cases in Houston Harris county[J]. Tuberculosis, 2011,91(Supplement 1):24-33. doi: 10.1016/j.tube.2011.10.006 pmid: 22094150
[31]	Luković J, Blagojevć D, Kilibarda M, et al.Spatial pattern of north Atlantic oscillation impact on rainfall in Serbia[J]. Spatial Statistics, 2015,14(Part A):39-52. doi: 10.1016/j.spasta.2015.04.007
[32]	Chopin P, Blazy J-M.Assessment of regional variability in crop yields with spatial autocorrelation: banana farms and policy implications in Martinique[J]. Agriculture, Ecosystems & Environment, 2013,181:12-21. doi: 10.1016/j.agee.2013.09.001
[33]	Agraval R, Srikant R.Fast algorithms for mining association rules in large data bases[C]. Proceedings of the 20th International Conference on Very Large Data Bases, 1994.
[34]	Zaki M J, Parthasarathy S, Ogihara M, et al.Parallel algorithms for discovery of association rules[J]. Data Mining and Knowledge Discovery, 1997,1(4):343-173. doi: 10.1023/A:1009773317876
[35]	Han J, Pei J, Yin Y.Mining frequent patterns without candidate generation[J]. Sigmod Record, 2000,29(2):1-12. doi: 10.1145/342009.335372

类别	详情
主机IP	128.227.49.92
时间	05/Aug/2014:10:26:42 +0800
方法	GET
URL	/extra/res/libs/kendo/extensions/kendo.extension.ui.js
协议	HTTP/1.1
状态	200
文件大小	15072
访问来源	http://www.geodata.cn/extra/TopicsWin2/pro3.jsp
客户端	Mozilla/5.0 (Windows NT 6.3; WOW64; rv:31.0) Gecko/20100101 Firefox/31.0

项目集	支持度（S）/（%）	内容描述
100101-22	27.1	中国1:400万地貌图（形态）
100101-2	12.9	中国1:400万资源环境数据（中国地形,1988年）
100101-18	11.6	全国土地利用数据库（分省:1980s,1987-2001年;分县：1980s）
100101-38	10.8	全国1 km网格人口数据（1995,2000, 2003,2005和2010年）
100101-66	10.6	中国1:400万全要素基础数据（1970s-1990s）

项目集	支持度（S）/(%)	内容描述
100101-18	34.1	全国土地利用数据库(分省:1980s,1987-2001年;分县:1980s)
100101-38	32.4	全国1 km网格人口数据(1995、2000、2003、2005和2010年)
100101-2	30.7	中国1:400万资源环境数据(中国地形,1988年)
100101-3	29.6	1996年浙江省1:25万数字化土地利用现状图
100101-30	29.2	全国多年平均降雨分布图(1 km)(建站到1996年)
100101-38、100101-18	28.0	全国1 km网络人口数据、全国土地利用数据库
100101-18、100101-2	27.5	全国土地利用数据库、中国1:400万资源环境数据
100101-30、100101-18	27.2	全国多年平均降雨分布图、全国土地利用数据库
100101-66	27.1	中国1:400万全要素基础数据(1970 s-1990 s)
100101-18、100101-3	26.8	全国土地利用数据库、1996年浙江省1:25万数字化土地利用现状图

关联规则	置信度（C）/(%)
100101-30 ==> 100101-2	90.4
100101-3==> 100101-18	90.8
100101-38、 100101-18==> 100101-2	91.4
100101-18、100101-2==> 100101-3	92.4
100101-2、100101-18 ==> 100101-38	92.9
100101-30、100101-18==> 100101-3	93.0
100101-30 ==> 100101-18	93.1
100101-18、100101-3==> 100101-30	94.1
100101-18、100101-2==> 100101-30	94.2
100101-18、100101-3 ==> 100101-2	94.6
100101-30、100101-2==> 100101-3	95.4
100101-30、100101-18 ==> 100101-2	95.4
100101-2、100101-3 ==>100101-30	96.9
100101-38、100101-2 ==> 100101-18	97.2
100101-2、100101-3==> 100101-18	97.8
100101-30、100101-3 ==> 100101-2	98.2
100101-30、100101-2 ==> 100101-18	98.2
100101-30、100101-3==> 100101-18	98.5

项目集	支持度（S）/（%）	内容描述
100101-66	13.7	中国1:400万全要素基础数据（1970s-1990s）
100101-38	9.6	全国1 km网格人口数据（1995、2000、2003、2005和2010年）
100101-11860	8.1	全国1:25万土地覆被数据（1980s,2005年）
100101-18	8.0	全国土地利用数据库（分省：1980s,1987-2001年;分县：1980s）
100101-29	7.3	陆地卫星MSS/TM/ETM+（1973-2008年、覆盖全国）

A Study on the User Behavior of Geoscience Data Sharing Based on Web Usage Mining

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 14

References 35

Related Articles 9

Metrics

Comments

Recommended 0

[1]	YANG Yaping, JIANG Hou, SUN Jiulin. Practice of Scientific Data Sharing: A Case Study of the National Earth System Science Data Center [J]. Journal of Geo-information Science, 2020, 22(6): 1358-1369.
[2]	XU Zhen,JING Yaodong,BI Rutian,GAO Yang,WANG Peng. The Discovery of Spatial Association Patterns of Resource and Environment Information Based on Grid Data [J]. Journal of Geo-information Science, 2018, 20(1): 28-36.
[3]	WANG Mo,WANG Juanle,HE Yuntao. An Approach for Prediction of Web User Behavior and Data Recommendation for Geoscience Data Sharing Portals [J]. Journal of Geo-information Science, 2017, 19(5): 595-604.
[4]	LIN Hui,ZHENG Xinqi. Data Mining and GIS Modeling of Urban Spatial Distribution Characteristics Based on Voronoi Diagram: Taking Shanxi Province as an Example [J]. Journal of Geo-information Science, 2015, 17(1): 62-68.
[5]	CHEN Jiangping, HUANG Bingjian. Application and Effects of Data Spatial Autocorrelation on Association Rule Mining [J]. , 2011, 13(1): 109-117.
[6]	QIN Kun, LI Zhenyu, DU Yi. Review of Spatial Data Mining Methods Based on Concept Analysis [J]. , 2009, 11(1): 10-17.
[7]	WANG Zhangang, ZHUANG Dafang, QIU Dongsheng, MING Tao. Application Analysis of Data Mining and Visualization in Forestry [J]. , 2007, 9(4): 19-22,141.
[8]	MEI Xin, NIU Zhenguo, WU Mengquan, YANG Xiaodong. Design of the Regional Circular Economy Decision Support System Based on Spatial Data Mining [J]. , 2007, 9(1): 88-92.
[9]	CHEN Chongcheng, TU Jiandong, HUANG Hongyu. The Study and Application on Visual Spatial Clustering [J]. , 2005, 7(2): 89-93.